SwePub - search: WFRF:(Shahid Shamsuddin)

Enumeration	Reference	Cover	Find
1.	Ahmad, Hafed Qasem, et al. (author) Assessment of Spatiotemporal Variability of Meteorological Droughts in Northern Iraq Using Satellite Rainfall Data 2021 In: KSCE Journal of Civil Engineering. - : Korean Society Of Civil Engineers-KSCE. - 1226-7988 .- 1976-3808. ; 25:11, s. 4481-4493 Journal article (peer-reviewed)abstract The absence of a dense rainfall monitoring network and longer period data are the major hindrances of hydroclimatic study in arid and semi-arid regions. An attempt has been made for the evaluation of spatiotemporal changes in droughts at the northern semi-arid region of Iraq for the period 1981-2018 using high-resolution (0.05 degrees) precipitation data of Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). The performance of CHIRPS in replicating rainfall and Standard Precipitation Index (SPI) for different timescales at eleven locations for the available period of observation data (2000-2014) was evaluated. The SPI was also used to estimate drought frequency and evaluate drought trends at all the CHIRPS grid points. A modified version of the non-parametric Mann-Kendall (MK) test was employed for a robust evaluation of the spatial distribution of temporal trends in droughts. The results showed a good ability of CHIRPS in reconstructing observed SPI with a correlation coefficient ranged from 0.64 to 0.87, BIAS between 1.05 and 1.81, Nash-Sutcliff efficiency from 0.39 to 0.55, and Willmott Index between 0.67 and 0.79. The CHIRPS also able to reconstruct the time series and probability distribution of observed SPI reasonably. Spatial distribution of droughts revealed a higher frequency of droughts of all categories and timescales in the east and north of Northern Iraq, mainly due to high rainfall variance. The MK test revealed a reduction in 6- and 12-month droughts in the northwest and an intensification at a few northeastern grids. It indicates droughts became more recurrent in the already drought-prone region and lessened in a less drought-prone region.
2.	Al-Janabi, Ahmed Mohammed Sami, et al. (author) Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity 2020 In: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11 Journal article (peer-reviewed)abstract The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.
3.	Homsi, Rajab, et al. (author) Precipitation projection using a CMIP5 GCM ensemble model : a regional investigation of Syria 2020 In: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 90-106 Journal article (peer-reviewed)abstract The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.
4.	Jing, Wang, et al. (author) Implementation of evolutionary computing models for reference evapotranspiration modeling : short review, assessment and possible future research directions 2019 In: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 811-823 Journal article (peer-reviewed)abstract Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ETo) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ETo is a major concern in hydrology. ETo can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ETo. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ETo. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ETo. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ETo in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ETo using EC.
5.	Malik, Anurag, et al. (author) Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test 2021 In: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 15:1, s. 1075-1094 Journal article (peer-reviewed)abstract Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ.
6.	Maroufpoor, Saman, et al. (author) A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation 2022 In: Frontiers in Environmental Science. - : Frontiers Media S.A.. - 2296-665X. ; 10 Journal article (peer-reviewed)abstract Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.
7.	Muhammad, Mohd Khairul Idlan, et al. (author) Heatwaves in Peninsular Malaysia: a spatiotemporal analysis 2024 In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14 Journal article (peer-reviewed)abstract One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950–2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.
8.	Pham, Quoc Bao, et al. (author) A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation 2021 In: Environmental Science and Pollution Research. - : Springer Science and Business Media LLC. - 0944-1344 .- 1614-7499. ; 28, s. 32564-32579 Journal article (peer-reviewed)abstract Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.
9.	Qutbudin, Ishanch, et al. (author) Seasonal Drought Pattern Changes Due to Climate Variability : Case Study in Afghanistan 2019 In: Water. - : MDPI. - 2073-4441. ; 11:5, s. 1-20 Journal article (peer-reviewed)abstract We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5 resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming.
10.	Safari, Ziauddin, et al. (author) Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing 2021 In: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 13:3 Journal article (peer-reviewed)abstract An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.
11.	Salih, Sinan Q., et al. (author) Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation : case study of Nasser Lake in Egypt 2019 In: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 878-891 Journal article (peer-reviewed)abstract Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.
12.	Salman, Saleem A., et al. (author) Changes in Climatic Water Availability and Crop Water Demand for Iraq Region 2020 In: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:8 Journal article (peer-reviewed)abstract Decreases in climatic water availability (CWA) and increases in crop water demand (CWD) in the background of climate change are a major concern in arid regions because of less water availability and higher irrigation requirements for crop production. Assessment of the spatiotemporal changes in CWA and CWD is important for the adaptation of irrigated agriculture to climate change for such regions. The recent changes in CWA and CWD during growing seasons of major crops have been assessed for Iraq where rapid changes in climate have been noticed in recent decades. Gridded precipitation of the global precipitation climatology center (GPCC) and gridded temperature of the climate research unit (CRU) having a spatial resolution of 0.5°, were used for the estimation of CWA and CWD using simple water balance equations. The Mann–Kendall (MK) test and one of its modified versions which can consider long-term persistence in time series, were used to estimate trends in CWA for the period 1961–2013. In addition, the changes in CWD between early (1961–1990) and late (1984–2013) periods were evaluated using the Wilcoxon rank test. The results revealed a deficit in water in all the seasons in most of the country while a surplus in the northern highlands in all the seasons except summer was observed. A significant reduction in the annual amount of CWA at a rate of −1 to −13 mm/year was observed at 0.5 level of significance in most of Iraq except in the north. Decreasing trends in CWA in spring (−0.4 to −1.8 mm/year), summer (−5.0 to −11 mm/year) and autumn (0.3 to −0.6 mm/year), and almost no change in winter was observed. The CWA during the growing season of summer crop (millet and sorghum) was found to decrease significantly in most of Iraq except in the north. The comparison of CWD revealed an increase in agricultural water needs in the late period (1984–2013) compared to the early period (1961–1990) by 1.0–8.0, 1.0–14, 15–30, 14–27 and 0.0–10 mm for wheat, barley, millet, sorghum and potato, respectively. The highest increase in CWD was found in April, October, June, June and April for wheat, barley, millet, sorghum and potato, respectively.
13.	Sammen, Saad Sh., et al. (author) Assessment of climate change impact on probable maximum floods in a tropical catchment 2022 In: Journal of Theoretical and Applied Climatology. - : Springer. - 0177-798X .- 1434-4483. ; 148:1-2, s. 15-31 Journal article (peer-reviewed)abstract The increases in extreme rainfall could increase the probable maximum flood (PMF) and pose a severe threat to the critical hydraulic infrastructure such as dams and flood protection structures. This study is conducted to assess the impact of climate change on PMF in a tropical catchment. Climate and inflow data of the Tenmengor reservoir, located in the state of Perak in Malaysia, have been used to calibrate and validate the hydrological model. The projected rainfall from regional climate model is used to generate probable maximum precipitation (PMP) for future periods. A hydrological model was used to simulate PMF from PMP estimated for the historical and two future periods, early (2031 − 2045) and late (2060 − 2075). The results revealed good performance of the hydrological model with Nash–Sutcliffe efficiency, 0.74, and the relative standard error, 0.51, during validation. The estimated rainfall depths were 89.5 mm, 106.3 mm, and 143.3 mm, respectively, for 5, 10, and 50 years of the return period. The study indicated an increase in PMP by 162% to 507% and 259% to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating floods in the future in his tropical catchment due to climate change.
14.	Sammen, Saad Sh., et al. (author) Rainfall modeling using two different neural networks improved by metaheuristic algorithms 2023 In: Environmental Sciences Europe. - : Springer Nature. - 2190-4707 .- 2190-4715. ; 35 Journal article (peer-reviewed)abstract Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models’ performance. The results indicated that the MLP–HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values were 0.90–0.23, 0.83–0.29, 0.85–0.25, 0.87–0.27, 0.81–0.31, and 0.80–0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO during testing processes and the corresponding NSE–PBIAS values were 0.92–0.22, 0.85–0.28, 0.89–0.26, 0.91–0.25, 0.83–0.31, 0.82–0.32, respectively. Based on the outputs of the MLP–HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP–HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP–HGSO was selected as an effective rainfall prediction model.
15.	Sharafati, Ahmad, et al. (author) Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters 2020 In: Advances in Civil Engineering / Hindawi. - London : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2020 Journal article (peer-reviewed)abstract An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall-runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC-HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. The uncertainties in HEC-HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. The flood events are obtained by simulating runoff for rainfall events using the generated model parameters. The uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). The results reveal a higher RCV index for flood volume (RCV = 153) than peak discharge (RCV = 116) for a 12-hr rainfall duration. The average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. The results indicate that rainfall depth is the main source of uncertainty of flood peak and volume.
16.	Tao, Hai, et al. (author) A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction 2020 In: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 83347-83358 Journal article (peer-reviewed)abstract Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station1) and Mosul (Station2) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r = 0.971 during testing phase at Station1 and Station2, respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.
17.	Tao, Hai, et al. (author) Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model 2021 In: Energy Reports. - Netherland : Elsevier. - 2352-4847. ; 7, s. 136-157 Journal article (peer-reviewed)abstract Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy.
18.	Tao, Hai, et al. (author) Groundwater level prediction using machine learning models: A comprehensive review 2022 In: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308 Research review (peer-reviewed)abstract Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
19.	Tao, Hai, et al. (author) Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters 2023 In: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 175 Journal article (peer-reviewed)abstract This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
20.	Yaseen, Zaher Mundher, et al. (author) Forecasting standardized precipitation index using data intelligence models : regional investigation of Bangladesh 2021 In: Scientific Reports. - UK : Springer Nature. - 2045-2322. ; 11 Journal article (peer-reviewed)abstract A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott’s Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.
21.	Yaseen, Zaher Mundher, et al. (author) Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model : Case Study in Tropical Region 2019 In: IEEE Access. - USA : IEEE. - 2169-3536. ; 7, s. 74471-74481 Journal article (peer-reviewed)abstract The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune the membership function of ANFIS model in order to improve the capability of streamflow forecasting. Different combination of antecedent streamflow was used to develop the forecasting models. The performance of the models was evaluated using a number of metrics including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( R2 ), and Willmott’s Index (WI) statistics. The results revealed that increasing number of inputs has a positive impact on the forecasting ability of both ANFIS and hybrid ANFIS models. The comparison of the performance of three optimization methods indicated PSO improved the capability of ANFIS model (RMSE = 7.96; MAE = 2.34; R2=0.998 and WI = 0.994) more compared to GA and DE in forecasting streamflow. The uncertainty band of ANFIS-PSO forecast was also found the lowest (±0.217), which indicates that ANFIS-PSO model can be used for reliable forecasting of highly stochastic river flow in tropical environment.
22.	Yaseen, Zaher Mundher, et al. (author) Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models 2020 In: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 70-89 Journal article (peer-reviewed)abstract Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation – the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM) – were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R2 = .92), and with all variables as inputs at Station II (R2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.
23.	Yaseen, Zaher Mundher, et al. (author) The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models : Application to Modeling River Dissolved Oxygen Concentration 2018 In: Water. - : MDPI. - 2073-4441. ; 10:9 Journal article (peer-reviewed)abstract The current study investigates an improved version of Least Square Support VectorMachines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO)concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novelintegrated model. The river water quality data at three monitoring stations located in the USA areconsidered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.
24.	Yaseen, Zaher, et al. (author) Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis 2019 In: Water. - Basel : MDPI. - 2073-4441. ; 11:3 Journal article (peer-reviewed)abstract In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% conﬁdence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.
25.	Yousif, Ali A., et al. (author) Open Channel Sluice Gate Scouring Parameters Prediction : Different Scenarios of Dimensional and Non-Dimensional Input Parameters 2019 In: Water. - : MDPI. - 2073-4441. ; 11:2 Journal article (peer-reviewed)abstract The determination of scour characteristics in the downstream of sluice gate is highly importantfor designing and protection of hydraulic structure. The applicability of modern data-intelligence technique known as extreme learning machine (ELM) to simulate scour characteristics has been examined in this study. Three major characteristics of scour hole in the downstream of a sluice gate, namely the length of scour hole (Ls), the maximum scour depth (Ds), and the position of maximum scour depth (Lsm), are modeled using different properties of the flow and bed material. The obtained results using ELM were compared with multivariate adaptive regression spline (MARS). The dimensional analysis technique was used to reduce the number of input variable to a smaller number of dimensionless groups and both the dimensional and non-dimensional variables were used to model the scour characteristics. The prediction performances of the developed models were examined using several statistical metrics. The results revealed that ELM can predict scour properties with much higher accuracy compared to MARS. The errors in prediction can be reduced in the range of 79%–81% using ELM models compared to MARS models. Better performance of the models was observed when dimensional variables were used as input. The result indicates that the use of ELM with non-dimensional data can provide high accuracy in modeling complex hydrological problems.

Skapa referenser, mejla, bekava och länka

Permalink

Träfflista för sökning "WFRF:(Shahid Shamsuddin) "

Refine your search

Year