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1.	Ahsan, Amimul, et al. (författare) Modeling the impacts of best management practices (BMPs) on pollution reduction in the Yarra River catchment, Australia 2023 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13 Tidskriftsartikel (refereegranskat)abstract Pollution of a watershed by different land uses and agricultural practices is becoming a major challenging factor that results in deterioration of water quality affecting human health and ecosystems. Sustainable use of available water resources warrants reduction of Non-Point Source (NPS) pollutants from receiving water bodies through best management practices (BMPs). A hydrologic model such as the Soil and Water Assessment Tool (SWAT) can be used for analyzing the impacts of various BMPs and implementing of different management plans for water quality improvement, which will help decision makers to determine the best combination of BMPs to maximize benefits. The objective of this study is to assess the potential reductions of sediments and nutrient loads by utilizing different BMPs on the Yarra River watershed using the SWAT model. The watershed is subdivided into 51 sub-watersheds where seven different BMPs were implemented. A SWAT model was developed and calibrated against a baseline period of 1998–2008. For calibration and validation of the model simulations for both the monthly and annual nutrients and sediments were assessed by using the Nash–Sutcliffe efficiency (NSE) statistical index. The values of the NSE were found more than 0.50 which indicates satisfactory model predictions. By utilizing different BMPs, the highest pollution reduction with minimal costs can be done by 32% targeted mixed-crop area. Furthermore, the combined effect of five BMPs imparts most sediments and nutrient reductions in the watershed. Overall, the selection of a BMP or combinations of BMPs should be set based on the goals set in a BMP application project.
2.	Chabuk, Ali, et al. (författare) Integrating WQI and GIS to assess water quality in Shatt Al-Hillah River, Iraq using physicochemical and heavy metal elements 2023 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 13:7 Tidskriftsartikel (refereegranskat)abstract This study assessed the quality of water in the Shatt Al-Hillah River by adopting some variables of physical, chemical, and heavy metal elements. The samples have been taken at six sites along the river in 2020 (from January to December). The water quality index has been determined by using the weighted-arithmetic method which is including a series of equations. Also, the model of Inverse-Distance-Weighting in the Geographic information system was applied to create a map of the water quality in the study area. Eleven physicochemical variables and five elements of heavy metals were comprised of calcium, magnesium, dissolved oxygen, Hydrogen Ions, chloride, sulfate, total hardness, total dissolved solids, turbidity, alkalinity, electric conductivity, cadmium, copper, iron, lead, and zinc. The results showed the values of the water quality index ranged from 245 to 253 (with a category of 200–300). The water quality index was rated as very poor for the selected locations along the Shatt Al-Hillah River. The GIS result illustrated the distributing map of water quality for the Shatt Al-Hillah River for household uses. The combination of the water quality index calculations with GIS in the current study might be used as a guide for future studies.
3.	Das, Sushil K., et al. (författare) Calibration, validation and uncertainty analysis of a SWAT water quality model 2024 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:4 Tidskriftsartikel (refereegranskat)abstract Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and annual calibration and validation for streamflow, nutrients, and sediment at the three selected sites are found with Nash–Sutcliffe efficiency values greater than 0.50. Furthermore, the uncertainty analysis of the model shows satisfactory results where the p-factor value is reliable by considering 95% prediction uncertainty and the d-factor value is close to zero. The model's results indicate that the model performs well in the river's watershed, which helps construct a water quality management model. Finally, the model application in the cost-effective management of water quality might reduce pollution in water bodies due to land use and agricultural activities, which would be beneficial to water management managers.
4.	Ehsan, Muhsan, et al. (författare) Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques 2024 Ingår i: Applied water science. - : Springer Science and Business Media Deutschland GmbH. - 2190-5487 .- 2190-5495. ; 14:2 Tidskriftsartikel (refereegranskat)abstract Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands.
5.	Elbeltagi, Ahmed, et al. (författare) Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration 2022 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12:7 Tidskriftsartikel (refereegranskat)abstract For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that Tmin, RHAvg, Ux, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms.
6.	Elbeltagi, Ahmed, et al. (författare) Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment 2023 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:2 Tidskriftsartikel (refereegranskat)abstract Exact estimation of evaporation rates is very important in a proper planning and efficient operation of water resources projects and agricultural activities. Evaporation is affected by many driving forces characterized by nonlinearity, non-stationary, and stochasticity. Such factors clearly hinder setting up rigorous predictive models. This study evaluates the predictability of coupling the additive regression model (AR) with four ensemble machine-learning algorithms—random Subspace (RSS), M5 pruned (M5P), reduced error pruning tree (REPTree), and bagging for estimating pan evaporation rates. Meteorological data encompass maximum temperature, minimum temperature, mean temperature, relative humidity, and wind speed from three different agroclimatic stations in Iraq (i.e., Baghdad, Mosul, and Basrah) were utilized as predictor parameters. The regression model in addition to the sensitivity analysis was employed to identify the best-input combinations for the evaluated methods. It was demonstrated that the AR-M5P estimated the evaporation with higher accuracy than others when combining wind speed, relative humidity, and the minimum and mean temperatures as input parameters. The AR-M5P model provided the best performance indicators, i.e., MAE = 33.82, RMSE = 45.05, RAE = 24.75, RRSE = 28.50, and r = 0.972 for Baghdad; MAE = 25.82, RMSE = 35.95, RAE = 23.75, RRSE = 29.64, and r = 0.956 for Mosul station, respectively. The outcomes of this study proved the superior performance of the hybridized methods in addressing such intricate hydrological relationships and hence could be employed for other environmental problems.
7.	Elboughdiri, Noureddine, et al. (författare) Tailoring porous organic polymers with enhanced capacity, thermal stability and surface area for perfluorooctane sulfonic acid (PFOS) elimination from water environment 2023 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:11 Tidskriftsartikel (refereegranskat)abstract Perfluorooctane sulfonic acid (PFOS), a perfluoroalkyl substance, has engendered alarm over its presence in water sources due to its intrinsic toxicity. Hence, there is a pressing need to identify efficacious adsorbents capable of removing PFAS derivatives from water. To achieve this, batch adsorption studies under various circumstances were employed to tune amorphous polymer networks regarding their morphological configuration, heat durability, surface area and capacity to adsorb PFOS in water. A facile, one-pot nucleophilic substitution reaction was employed to synthesize amorphous polymer networks using triazine derivatives as building units for monomers. Notably, POP-3 exhibited a superlative adsorption capacity, with a removal efficiency of 97.8%, compared to 90.3% for POP-7. POP-7 exhibited a higher specific surface area (SBET) of 232 m2 g−1 compared to POP-3 with a surface area of 5.2 m2 g−1. Additionally, the study emphasizes the importance of electrostatic forces in PFOS adsorption, with pH being a significant element, as seen by changes in the PFOS sorption process by both polymeric networks under neutral, basic and acidic environments. The optimal pH value for the PFOS removal process using both polymers was found to be 4. Also, POP-7 exhibited a better thermal stability performance (300 °C) compared to POP-3 (190 °C). Finally, these findings indicate the ease with which amorphous polymeric frameworks may be synthesized as robust and effective adsorbents for the elimination of PFOS from waterbodies.
8.	Emami, Somayeh, et al. (författare) Application of ANFIS, ELM, and ANN models to assess water productivity indicators based on agronomic techniques in the Lake Urmia Basin 2023 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:2 Tidskriftsartikel (refereegranskat)abstract Water productivity (WP) is one of the most important critical indicators in the essential planning of water consumption in the agricultural sector. For this purpose, the WP and economic water productivity (WPe) were estimated using agronomic technologies. The impact of agronomic technologies on WP and WPe was carried out in two parts of field monitoring and modeling using novel intelligent approaches. Extreme learning machine (ELM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods were used to model WP and WPe. A dataset including 200 field data was collected from five treatment and control sections in the Malekan region, located in the southeast of Lake Urmia, Iran, for the crop year 2020–2021. Six different input combinations were introduced to estimate WP and WPe. The models used were evaluated using mean squared error (RMSE), relative mean squared error (RRMSE), and efficiency measures (NSE). Field monitoring results showed that in the treatment fields, with the application of agronomic technologies, the crop yield, WP, and WPe increased by 17.9%, 30.1%, and 19.9%, respectively. The results explained that irrigation water in farms W1, W2, W3, W4, and W5 decreased by 23.9%, 21.3%, 29.5%, 16.5%, and 2.7%, respectively. The modeling results indicated that the ANFIS model with values of RMSE = 0.016, RRMSE = 0.018, and NSE = 0.960 performed better in estimating WP and WPe than ANN and ELM models. The results confirmed that the crop variety, fertilizer, and irrigation plot dimensions are the most critical influencing parameters in improving WP and WPe.
9.	Falih, Ali Hasan, et al. (författare) Comparative study on salinity removal methods: an evaluation-based stable isotopes signatures in ground and sea water 2023 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13 Tidskriftsartikel (refereegranskat)abstract This research aims to attain the optimal method of removing the high salinity concentrations without its effect on the balance or accuracy of stable isotopes measurement of deuterium and oxygen-18 (δ18O, δ2H). Four treatment methods (i.e., distillation, vacuum distillation, electro dialysis and ion exchange) were applied for nine samples, which were obtained from different water sources (sea, groundwater, river).l Worth to notice that the samples have Electrical Conductivity (EC) ranged (1000–60,000 µs/cm). Liquid–Water Isotope Analyzer used to measure the isotope concentration of δ18O, δ2H. The research findings of the four applied methods revealed their effectiveness with various percentages (normal distillation: 92.37%; vacuum distillation: 88.31%; electro dialysis: 94.85%; ion exchange: 99.62%). In addition, the investigation was conducted a clear correspondence measurement of (δ18O, δ2H) isotopes before and after treatment. The four methods results indicated that samples with EC ranged (1000–5000 µs/cm) have no effect on stable isotope readings. Whereas, samples with EC higher than 10,000, have substantial influence on the stable isotope readings. Finally, vacuum distillation method attained the best results among the treatment methods for EC ranged (10,000–60,000 µs/cm) without affecting the isotopic content of (δ18O, δ2H). There is a clear correspondence of the stable isotopic measurements before and after treatment, for all the selected samples.
10.	Gupta, Sanjeev, et al. (författare) Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network 2024 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:6 Tidskriftsartikel (refereegranskat)abstract Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.
11.	Hameed, Mohammed Majeed, et al. (författare) Introducing high-order response surface method for improving scour depth prediction downstream of weirs 2024 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14 Tidskriftsartikel (refereegranskat)abstract Scour depth downstream of weirs is considered one of the most important hydraulic problems, which greatly influences the stability of weirs. Recently, artificial intelligence (AI) methods have become increasingly popular in modeling hydraulic variables, especially scour depth, because they can capture nonlinear relationships between input variables and their associated objectives. Despite their importance, these models have problems with hyperparameter tuning in scour depth modeling due to their structures, so algorithms must be used to tune the hyperparameters. Moreover, these algorithms are usually tuned by using the trial-and-error method to select the hyperparameters such as the number of hidden nodes, transfer function, and learning rate, and in this case, the main problem is overfitting during the training phase. To solve these problems, the high-order response surface method (HORSM), an improved version of the response surface method (RSM), is used as an alternative approach for the first time in this study to predict the scour depth. The HORSM model is based on high-order polynomial functions (from two to six) compared with the artificial neural network model (ANN). The findings indicate that the fifth order of the HORSM polynomial function yields the most precise predictions, with a higher coefficient of determination (R2) of 0.912 and Willmott Index (WI) of 0.972 compared to the values obtained using ANN (R2 = 0.886 and WI = 0.927). Moreover, the accuracy of the predictions is represented by a reduction of the mean square error by up to 44.17 and 29.01% compared to the classical RSM and ANN, respectively. The suggested model established an excellent correlation and accuracy with experimental values.
12.	He, Ting, et al. (författare) Poly(vinylidene fluoride) (PVDF) membrane fabrication with an ionic liquid via non-solvent thermally induced phase separation (N-TIPs) 2022 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12:3 Tidskriftsartikel (refereegranskat)abstract In this paper, ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate was the first time successfully utilized as single solvent in preparing the PVDF membrane with a good performance by N-TIPs method. The effects of quenching temperature and hydrophilic additive content on the morphology, permeability, and strength of the membranes were studied. All the prepared PVDF membranes were proved to be a pure β phase by FTIR and XRD, possessing a narrow pore size distribution. By adjusting quenching temperature and additive content, membranes with a flux of 383.2 L/m2 h and concentrated pore diameter of 26 nm obtained.
13.	Heddam, Salim, et al. (författare) Hybrid river stage forecasting based on machine learning with empirical mode decomposition 2024 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:3 Tidskriftsartikel (refereegranskat)abstract The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting approach was developed by combining machine learning techniques, namely random forest regression (RFR), bootstrap aggregating (Bagging), adaptive boosting (AdaBoost), and artificial neural network (ANN), with empirical mode decomposition (EMD) to provide a robust forecasting model. The singles models were first applied using only the river stage data without preprocessing, and in the following step, the data were decomposed into several intrinsic mode functions (IMF), which were then used as new input variables. According to the obtained results, the proposed models showed improved results compared to the standard RFR without EMD for which, the error performances metrics were drastically reduced, and the correlation index was increased remarkably and great changes in models’ performances have taken place. The RFR_EMD, Bagging_EMD, and AdaBoost_EMD were less accurate than the ANN_EMD model, which had higher R≈0.974, NSE≈0.949, RMSE≈0.330 and MAE≈0.175 values. While the RFR_EMD and the Bagging_EMD were relatively equal and exhibited the same accuracies higher than the AdaBoost_EMD, the superiority of the ANN_EMD was obvious. The proposed model shows the potential for combining signal decomposition with machine learning, which can serve as a basis for new insights into river stage forecasting.
14.	Jahad, Udai A., et al. (författare) Flow characteristics and energy dissipation over stepped spillway with various step geometries: case study (steps with curve end sill) 2024 Ingår i: Applied water science. - : Springer Science and Business Media Deutschland GmbH. - 2190-5487 .- 2190-5495. ; 14:3 Tidskriftsartikel (refereegranskat)abstract Stepped weirs are used in a wide range of applications, designed to increase energy dissipation. In this study, laboratory experiments were conducted in a flume on six stepped weir models, with a downstream angle of θ = 26.6°. The physical models used were on a scale of 10:1, and tests of discharges up to 0.055 m3/s were carried out. Several step geometries including traditional step, sill and curve geometries were used to study flow behavior and overall energy dissipation. The laboratory investigations were augmented by modelling numerically the within step flow and energy behavior using a 2-D CFD model, incorporating the k-ε model for turbulence closure. The results showed that energy dissipation was greatest for the curved steps by about 10.5%, where it was observed that the skimming flow regime was shifted to a higher discharge range. Numerical modelling results showed good agreement with the experimental results. An inspection of the modelled streamlines highlighted the increase in vortex intensity for the curve model, reflecting the strong circulation observed. The predicted stepwise energy dissipation showed the energy dissipation increase when the step number Ns increases. For the range of step height hs, tested, our results showed that energy dissipation increased with step height. The results from this study can be used to inform engineering design for steps with θ = 26.6° and provide estimates of the expected energy dissipation and residual energy.
15.	Jery, Atef El, et al. (författare) A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae 2023 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:7 Tidskriftsartikel (refereegranskat)abstract By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, CODinlet, and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, CODinlet=600mg/l, and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 mg/l, showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient (R2) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively.
16.	Khan, Ramsha, et al. (författare) Appraisal of water quality and ecological sensitivity with reference to riverfront development along the River Gomti, India 2022 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 12:1 Tidskriftsartikel (refereegranskat)abstract The conflict between the vitality of natural ecosystem versus artificially developed systems has existed since decades. The ecological sensitivity and socio-economic aspects associated with riverfront development along rivers have attracted the attention of environmentalists and ecologists across the globe. The present study evaluates the impacts of channelization and riverfront development on the water quality of river Gomti through Water Pollution Index (WPI) and other statistical tools. Of the total studied sites, 75% were found to be in the 'highly polluted' category even after the development of riverfront. An approximate increase of 274.5% and 171.76% was witnessed in the WPI values at the midstream sites of Kudiaghat and Daliganj, respectively. This increase in the WPI values clearly stated the deteriorated water quality of river Gomti after the channelization. The major issue of domestic sewage discharge with partial or no treatment into the river seems to be unresolved even after a considerable period of riverfront development. This study can provide a reference database toward development of such projects across the globe.
17.	Kumar, Deepak, et al. (författare) Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms 2023 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 13:10 Tidskriftsartikel (refereegranskat)abstract The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (ECt+1, ECt+2, and ECt+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin.
18.	Mokhtar, Ali, et al. (författare) Prediction of irrigation water quality indices based on machine learning and regression models 2022 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12 Tidskriftsartikel (refereegranskat)abstract Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies. The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. To achieve this goal, three artificial intelligence (AI) models (Support vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression models (Stepwise Regression, SW; Principal Components Regression, PCR; Partial least squares regression, PLS; Ordinary least squares regression, OLS) were applied and validated for predicting six irrigation water quality criteria (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; residual sodium carbonate, RSC; potential of salinity, PS; permeability index, PI; Kelly’s ratio, KR). Electrical conductivity (EC), sodium (Na+), calcium (Ca2+) and bicarbonate (HCO3−) were used as input exploratory variables for the models. The results indicated the water source is not suitable for irrigation without treatment. A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. Based on the performance statistics of the root mean square error (RMSE) and the scatter index (SI), SW emerged as the best (0.21% and 0.03%) followed by PCR and PLS with RMSE 0.22% and 0.21% for SAR, respectively. Based on the classification of the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC. These results highlight potential of using multiple regressions and the developed machine learning methods in predicting the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality.
19.	Patel, Khageshwar Singh, et al. (författare) Groundwater arsenic and fluoride in Rajnandgaon District, Chhattisgarh, northeastern India 2017 Ingår i: Applied water science. - : SPRINGER HEIDELBERG. - 2190-5487 .- 2190-5495. ; 7:4, s. 1817-1826 Tidskriftsartikel (refereegranskat)abstract The groundwater of Ambagarh Chouki, Rajnandgaon, India, shows elevated levels of As and F-, frequently above the WHO guidelines. In this work, the concentrations of As, F-, Na+, Mg2+, Ca2+, Cl-, SO42-, HCO3-, Fe, dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) in the groundwater of Ambagarh Chouki are described. The sources of dissolved components in the groundwater are investigated using the cluster and factor analysis. Five factors have been identified and linked to processes responsible for the formation of groundwater chemistry. High concentrations of dissolved As seems to be linked to high concentrations of DOC, suggesting reductive dissolution of ferric oxyhydroxides as arsenic mobilization process. Fluoride is found in shallow depth water, presumably as a consequence of evaporation of water and removal of Ca2+ by precipitation of carbonates.
20.	Pham, Quoc Bao, et al. (författare) Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms 2023 Ingår i: Applied water science. - : Springer Science and Business Media LLC. - 2190-5487 .- 2190-5495. ; 13:1 Tidskriftsartikel (refereegranskat)abstract Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were Xt-1,Xt-2,Xt-3,Xt-4,Xt-12) the ANFIS-WOA model attained root mean square error (RMSE ≈ 0.08 m), mean absolute error (MAE ≈ 0.06 m), and coefficient of determination (R2≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.
21.	Rahman, Khalil Ur, et al. (författare) Comparison of machine learning and process-based SWAT model in simulating streamflow in the Upper Indus Basin 2022 Ingår i: Applied water science. - : Springer Science and Business Media LLC. - 2190-5487 .- 2190-5495. ; 12:8 Tidskriftsartikel (refereegranskat)abstract This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.
22.	Rauf, Lanja F., et al. (författare) Indicator of suitability for evaluating the aquifer thermal energy storage using the GIS-based MCDA technique in the Halabja-Khurmal sub-basin 2023 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:5 Tidskriftsartikel (refereegranskat)abstract Thermal energy is one of the eco-friendly sources of energy used worldwide for storing heat and cold between seasons. The aquifer thermal energy storage system effectively reduces carbon dioxide emission gas in the Halabja governorate. It is an economical way to be used in cooling and heating applications. This study evaluates the suitability of aquifer thermal energy storage in the Halabja-Khurmal sub-basin. Six critical criteria were selected: the type of aquifers, groundwater recharge, fresh/saline groundwater, groundwater quality, seepage velocity, and mean annual temperature by applying decision-maker judgment. The hydrogeological and climate criteria analysis combination has a consistency ratio of 0.008 in AHP. As a result, the Aquifer thermal energy storage suitability map in the Halabja-Khurmal sub-basin displays a surface area of 62.1% as strongly suitable, 7.7% as suitable in northern and southern parts, 29.2% as weakly suitable in southwestern, east, southeast, and northeast, and 1% as not suitable for aquifer thermal energy storage system.
23.	Rauf, Lanja F., et al. (författare) Sustainability indicator for evaluating the ATES system in Halabja-Khurmal sub-basin NE-Iraq using GIS-based MCDA method 2024 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:6 Tidskriftsartikel (refereegranskat)abstract Thermal energy is utilized as an environmentally friendly energy source for seasonal heat and cold storage on a global scale. Specifically, the aquifer thermal energy storage system is highlighted for being cost-effective in cooling and heating applications. The study assesses the sustainability of the aquifer thermal energy storage in the Halabja-Khurmal sub-basin by evaluating six critical criteria: groundwater transmissivity, groundwater temperature, groundwater discharge, groundwater chemistry, population density, and per capita GDP. A multi-criteria decision analysis judgment is applied to analyze all criteria, resulting in a consistency ratio of 0.3% in the analytical hierarchy process. Consequently, the sustainability map for Aquifer Thermal Energy Storage in the Halabja-Khurmal sub-basin for heating reveals that 26.45% of the area is strongly sustainable located in the north and southwestern part of the sub-basin, 73.53% is moderate in the east, central, southeast, and southern regions, 0.02% is weakly sustainable as a tiny area in the southwestern. On the other hand, the sustainability map for Aquifer Thermal Energy Storage in the Halabja-Khurmal sub-basin for cooling reveals that 19% of the area is strongly sustainable located in the north, and southwestern parts of the sub-basin, 78% is moderate in the northeast, east, southeast, west, central, and southern regions, 3% is weakly sustainable as spots in the west and southwestern areas.
24.	Raza, Ali, et al. (författare) Use of gene expression programming to predict reference evapotranspiration in different climatic conditions 2024 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14 Tidskriftsartikel (refereegranskat)abstract Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions.
25.	Shinde, S. P., et al. (författare) Characterization of basaltic rock aquifer parameters using hydraulic parameters, Theis’s method and aquifer test software in the hard rock area of Buchakewadi watershed Maharashtra, India 2022 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 12:9 Tidskriftsartikel (refereegranskat)abstract The twelve wells were selected to carry out the various test, duration of pumping (min), maximum draw drown (m), duration of recovery (min), residual drawdown, and aquifer type in the basaltic rock aquifer parameters of Buchakewadi watershed. The source and flow of groundwater are essential concerns in hydrological systems that concern both spatially and temporally components of groundwater discharge and water supply problems. The content and temperature of groundwater flowing through an aquifer might change depending on the aquifer environment. As a result, hydrodynamic analyses can provide valuable information about a region’s subsurface geology. The present research attempts of aquifer variables such as transmissivity (T) and storativity (S) estimation are significant for groundwater resource development and evaluation. There are numerous approaches for calculating precise aquifer characteristics (i.e., hydrograph analysis, pumping test, etc.). A most frequent in situ analysis is a well-pumping test, which accurately measures the decline and rise of groundwater levels. During an aquifer pumping test, to characterize aquifer properties in an undiscovered location to forecast the rate of depletion of the groundwater table/potentiometric surface. The shallow, weathering subsurface water accessible above the Deccan traps in an unconfined state is insufficient to satisfy the ever-increasing pressure on water supplies. Maharashtra is similarly dominated by hard rocks, whose rainfall susceptibility is limited by weathering and primary porosity, as is their volume to store and convey water. Based on the hydraulic parameters and Theis method, results are optimized. Aquifer mapping and pumping test results can be more important for solving problems such as water scarcity, nonpolluting water, health issues, and source of fresh water on the earth surface. However, the characterization of aquifer parameters should be significant role in the scientific planning and engineering practices. Â© 2022, The Author(s).
26.	Sohail, Muhammad Tayyab, et al. (författare) Groundwater budgeting of Nari and Gaj formations and groundwater mapping of Karachi, Pakistan 2022 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12 Tidskriftsartikel (refereegranskat)abstract Groundwater depletion is an emerging problem worldwide due to changes in climate and an increase in urbanization. Two significant water-bearing formations, the Oligocene-aged Nari and the Miocene-aged Gaj, were utilized as a case study exposed near Karachi, Pakistan. Groundwater budgeting was performed through a classical equation. The inflow of groundwater in the formations was calculated by thermo-pluviometric data and water loss of Hub Dam. The potential of evapotranspiration (PET) was calculated by the Thornthwaite method. The groundwater inflow from Hub Dam was estimated by using 20 years of annual water loss data by removing PET. The total mean annual inflow of groundwater in the formations was 2414.12 US Gallons per Second (gps). The annual mean outflow was estimated by calculation of groundwater usage for industries and domestic purposes and the mean annual groundwater outflow was 5562.61 US gps and an annual deficit of groundwater was 3148.5 US gps. The research is composed of validating the groundwater budget. Direct Current Electrical Resistivity (DCER) and static water level data from existing industrial wells were used for groundwater maps. The DCER data indicates A-Type and K-Type sub-surface with high resistivity in the three-layer model. The average water table of residential areas in 2019 was 60 m and in industrial areas was 130 m. The oscillation of the groundwater table over the last 20 years and the deficit of the groundwater budget shows an alarming condition for the future. If the same scenario persists, then by 2025, the water table will decline up to 140 m.
27.	Swain, Sabyasachi, et al. (författare) A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India 2024 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:2 Tidskriftsartikel (refereegranskat)abstract The commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess the meteorological drought years based on annual and monsoonal precipitation over nineteen districts in Western Rajasthan, India, during 1901–2019. Additionally, a novel statistic called Discrepancy Measure (DM) is employed to assess the degree of discrepancy in the precipitation climatology of the districts for annual and monsoon precipitation time series. Based on annual precipitation, Jaisalmer district exhibited the highest number of historical drought years (35), whereas three districts, i.e., Jhunjhunu, Dausa, and Bhilwara exhibited the lowest number of drought years (11). Similarly, based on monsoon precipitation, Jaisalmer and Bhilwara encountered the highest (34) and the lowest (11) number of drought years, respectively. The return period of Drought-II is lower for monsoon precipitation-based DPI as compared to that of the annual precipitation-based DPI for all the districts. The DM and DPI-based total number of droughts are found to be strongly correlated for both annual and monsoon precipitation. The DM value is highest for Jaisalmer and lowest for Bhilwara district. The findings reveal DPI as an efficient tool for assessing drought years, particularly in arid climatic conditions. Moreover, as the DM value increases for a precipitation series, the DPI becomes more effective in capturing drought events.
28.	Swain, Sabyasachi, et al. (författare) Impact of climate change on groundwater hydrology: a comprehensive review and current status of the Indian hydrogeology 2022 Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12:6 Forskningsöversikt (refereegranskat)abstract Groundwater is the second largest store of freshwater in the world. The sustainability of the ecosystem is largely dependent on groundwater availability, and groundwater has already been under tremendous pressure to fulfill human needs owing to anthropogenic activities around various parts of the world. The footprints of human activities can be witnessed in terms of looming climate change, water pollution, and changes in available water resources. This paper provides a comprehensive view of the linkage between groundwater, climate system, and anthropogenic activities, with a focus on the Indian region. The significant prior works addressing the groundwater-induced response on the climatic system and the impacts of climate on groundwater through natural and human-instigated processes are reviewed. The condition of groundwater quality in India with respect to various physicochemical, heavy metal and biological contamination is discussed. The utility of remote sensing and GIS in groundwater-related studies is discussed, focusing on Gravity Recovery and Climate Experiment (GRACE) applications over the Indian region. GRACE-based estimates of terrestrial water storage have been instrumental in numerous groundwater studies in recent times. Based on the literature review, the sustainable practices adopted for optimum utilization of groundwater for different purposes and the possible groundwater-based adaptation strategies for climate change are also enunciated.
29.	Tuyen, Tran Thi, et al. (författare) Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models 2024 Ingår i: Applied water science. - : Springer Science and Business Media Deutschland GmbH. - 2190-5487 .- 2190-5495. ; 14 Tidskriftsartikel (refereegranskat)abstract Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.
30.	Zambrano, Jesús, et al. (författare) Machine learning techniques for monitoring the sludge profile in a secondary settler tank 2019 Ingår i: Applied water science. - : Springer Science and Business Media LLC. - 2190-5487 .- 2190-5495. ; 9:6 Tidskriftsartikel (refereegranskat)
31.	Zerouali, Bilel, et al. (författare) Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods 2022 Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 12:9 Tidskriftsartikel (refereegranskat)abstract In this paper, the Innovative Trend Methodology (ITM) and their inspired approaches, i.e., Double (D-ITM) and Triple (T-ITM), were combined with Hilbert Huang transform (HHT) time frequency-based method. The new hybrid methods (i.e., ITM-HHT, D-ITM-HHT, and T-ITM-HHT) were proposed and compared to the DWT-based methods in order to recommend the best method. Three total annual rainfall time series from 1920 to 2011 were selected from three hydrological basins in Northern Algeria. The new combined models (ITM-HHT, D-ITM-HHT, and T-ITM-HHT) revealed that the 1950â€“1975 period has significant wet episodes followed by a long-term drought observed in the western region of Northern Algeria, while Northeastern Algeria presented a wet period since 2001. The proposed approaches successfully detected, in a visible manner, hidden trends presented in the signals, which proves that the removal of some modes of variability from the original rainfall signals can increase the accuracy of the used approaches. Â© 2022, The Author(s).
32.	Berndtsson, Ronny, et al. (författare) Assessment of water quality in Upper Ganga Canal by using Comprehensive Pollution Index 2018 Ingår i: Applied water science. - 2190-5487. Tidskriftsartikel (refereegranskat)
33.	Kayhomayoon, Zahra, et al. (författare) Application of soft computing and evolutionary algorithms to estimate hydropower potential in multi-purpose reservoirs 2023 Ingår i: Applied water science. - 2190-5487. ; 13:9 Tidskriftsartikel (refereegranskat)abstract Hydropower is a clean and efficient technology for producing renewable energy. Assessment and forecasting of hydropower production are important for strategic decision-making. This study aimed to use machine learning models, including adaptive neuro-fuzzy inference system (ANFIS), gene expression programming, random forest (RF), and least square support vector regression (LSSVR), for predicting hydroelectric energy production. A total of eight input scenarios was defined with a combination of various observed variables, including evaporation, precipitation, inflow, and outflow to the reservoir, to predict the hydroelectric energy produced during the experimental period. The Mahabad reservoir near Lake Urmia in the northwest of Iran was selected as a study object. The results showed that a combination of hydroelectric energy produced in the previous month, evaporation, and outflow from the dam resulted in the highest prediction performance using the RF model. A scenario that included all input variables except the precipitation outperformed other scenarios using the LSSVR model. Among the models, LSSVR exerted the highest prediction performance for which RMSE, MAPE, and NSE were 442.7 (MWH), 328.3 (MWH), and 0.85, respectively. The results showed that Harris hawks optimization (HHO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) was better than particle swarm optimization (PSO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) in optimizing ANFIS during the prediction. The results of Taylor’s diagram indicated that the ANFIS-HHO model had the highest accuracy. The findings of this study showed that machine learning models can be used as an essential tool for decision-making in sustainable hydropower production.
34.	Mohammadi, Babak, et al. (författare) Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data 2024 Ingår i: Applied water science. - 2190-5487. ; 14:3 Tidskriftsartikel (refereegranskat)abstract Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

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