| 1. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
- 2024
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Ingår i: Potato Research. - : Springer Nature. - 0014-3065 .- 1871-4528.
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Tidskriftsartikel (refereegranskat)abstract
- Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.
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| 2. |
- Al-Madhlom, Qais, et al.
(författare)
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Potential use of UTES in Babylon Governorate, Iraq
- 2020
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Ingår i: Groundwater for Sustainable Development. - : Elsevier. - 2352-801X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- There is a global attention that the future energy systems will be based on renewable energy like solar and wind. The large-scale utilization of renewables in space heating and cooling requires large Thermal Energy Storage TES to overcome the varying supply and demand. The process of producing the best Underground Thermal Energy Storage UTES system pass through two steps: first, finding the best type of UTES system, second, finding the best locations to install UTES system. Both of these two steps depend extremely on the site specific parameters such that the depth to the groundwater, transmissivity, type of soil, the depth to the bedrock, and seepage velocity. The purpose of this paper is to explain some of the site specific parameters that the type of UTES-system depends on and explain the suitable type of UTES systems. This study considers Babylon province (Iraq) as study area. This province has electricity deficiency due to Heating Ventilating and Air Conditioning HVAC applications. The methodology of this study includes reviewing the literature that consider the study area, and using Arc Map/GIS to visualize some of the in-site parameters. The results indicate that the best type of UTES system for the considered region is either aquifer or pit type, due to the type of the soil and the depth to the crystalline bedrock. The hydraulic conductivity and the seepage velocity in the considered region are (0.0023–2.5) m/d and (1.3 × 10−6 – 3.45 × 10−3) m/d respectively. These conditions satisfy the standards which regard aquifer type.
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| 3. |
- Al-Madhlom, Qais, et al.
(författare)
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Site Selection Criteria of UTES Systems in Hot Climate
- 2019
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Ingår i: Proceedings of the XVII ECSMGE-2019. - Iceland : The Icelandic Geotechnical Society (IGS). ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Underground Thermal Energy Storage UTES systems are widely used around the world. The reason is that UTES is essential in utilizing Renewable Energy sources (RE). The efficiency of the energy system relies strongly on the efficiency of the storage system. Therefore, in the installation of a hyper-energy system, a lot of attention is to be paid in improving the storage system. In order to design an efficient storage system, firstly, standard criteria are to be investigated. These explain the process of making high efficiency storage system that must be specified. The criteria, mainly, depends on: best type and best location. These two variables are in high interference with each other. The bond between the two variables is represented by the geological, hydrological, meteorological, soil, hydrogeological properties/factors of the site. These factors are specified by geo-energy mapping. Despite the importance of this type of mapping, there is no specific criteria/formula that defines the choice. This paper aims to: give a brief literature review for UTES systems (types, classification, advantages/disadvantages for each type, and examples of an installed system). In addition, some factors within geo-energy mapping are highlighted and standard criteria to achieve good storage system are suggested. The suggested criterion comprises a process to transfer the quantity values to quality values according to the expert opinion. The suggested criteria are defined through the following stages: selecting the best type of UTES systems according to hydro-geological in site conditions; using the analytical hierarchy process to rank the best location to install the storage system and then using ArcMap (GIS-Software) to provide representative results as maps. Karbala Province (Iraq) is the study area used here
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| 4. |
- Al-Madhlom, Qais, et al.
(författare)
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Site Selection of Aquifer Thermal Energy Storage Systems in Shallow Groundwater Conditions
- 2019
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Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- Underground thermal energy storage (UTES) systems are widely used around the world, due to their relations to heating ventilating and air conditioning (HVAC) applications [1]. To achieve the required objectives of these systems, the best design of these systems should be accessed first. The process of determining the best design for any UTES system has two stages, the type selection stage and the site selection stage. In the type selection stage, the best sort of UTES system is determined. There are six kinds of UTES systems, they are: boreholes, aquifer, bit, tank, tubes in clay, and cavern [2–5]. The selection of a particular type depends on three groups of parameters. They are: Site specific, design, and operation parameters (Figure 1). Apart from site specific parameters, the other two types can be changed through the life time of the system. The site specific parameters, e.g., geological, hydrogeological, and metrological, cannot be changed during the service period of the ystem. Therefore, the design of the best type should depend, at first consideration, on site specific parameters.
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| 5. |
- Alabas, Mohammed A Almajeed A, et al.
(författare)
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Investigation of the Effect of Downstream Slope and Rockfill Materials on Flow Regimes over Gabion Stepped Weirs
- 2023
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Ingår i: Polish Journal of Environmental Studies. - : HARD. - 1230-1485 .- 2083-5906. ; 32:4, s. 3481-3490
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Tidskriftsartikel (refereegranskat)abstract
- It is important to determine the limits of flow regimes in the design of stepped weirs because of the hydraulic performance of each regime. The present study investigates the effect of downstream slope and rock fill materials on flow regimes in gabion stepped weirs. Nine physical models of gabion weirs were used in the experiments. The models’ downstream slopes ranged from 1:05 to 1:4 V:H. In addition, two types of rockfill materials: crushed stone of 0.42 porosity and rounded gravel of 0.38 porosity were used to study the effect of rockfill materials on flow regimes. The nominal size of the crushed stone was (37.5 mm - 13.2 mm) D50 = 23 mm and the nominal size of the rounded gravel was (26.5 mm - 13.2 mm) D50 = 16 mm. Each model has been tested with ten runs for discharge per unit width ranging (from 0.006 to 0.105 m3/sec. m) to cover all flow conditions and flow regimes. The onset of each flow regime for all physical models has been observed. The experimental data of the gabion stepped weirs have been used to develop equations to estimate the onset of each flow regime. The coefficient of correlation (R) of the developed equations ranged between 0.95 to 0.97. The results indicated on the steeper downstream slope models (1:0.5, 1:0.83), there is interference between the nappe and transition flow regimes. The nappe flow regime has not appeared on all steps at the same time. Moreover, the shape and size of the rockfill materials have an insignificant effect on flow regimes, especially at a high flow rate.
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| 6. |
- Hommadi, Ali Hassan, et al.
(författare)
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Evaluating Water Scarcity Indices for Cultivation Region in Sadat Al-Hindya, Babylon, Iraq: A Case Study
- 2023
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Ingår i: Engineering. - : Scientific Research Publishing. - 1947-3931 .- 1947-394X. ; 15:10, s. 647-662
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Tidskriftsartikel (refereegranskat)abstract
- The study evaluated the Water Scarcity Indices for Cultivation Region in Sadat Al-Hindya, Babylon, Iraq. It calculated the reference evapotranspiration, actual evapotranspiration, and amount of precipitation with effective rainfall to estimate the droughts indicators which are the Standard Precipitation Index (SPI), the Standard Precipitation and Evaporation Rain Index (SPEI) and Reconnaissance Drought Index RDI. The study indicated that the greatest decrease in river flow occurred from 2019-2021 to 2020-2021 due to increasing temperature in summer and decreasing precipitation in winter. This research evaluated a wet and drought indicating for planning and management of water resources to face changes in climate of future. The research showed the last years were years of drought according to the three indicators. SPI ranged from 0.5 to 1.5 in the rainy years, but it was -0.5 to -1 as moderately dry because in the middle of Iraq while in the south of Iraq was severely dry or extremely dry. SPEI of the study area ranged from -1.5 to -2.5 which means severely dry. The SPEI measures are negative values meaning the months and years were drier. RDi ranged from 0 to -1 was dry and moderately dry while some months and years are positive and will be wet through rainfall for ten years (2014-2023). From indices showed that the region was a drying study area due to the impact of climate change because of the reduction of precipitation and increase in temperature which caused a rise of evapotranspiration during the last few years.
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| 7. |
- Kumar Singh, Abhinav, et al.
(författare)
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An Integrated Statistical-Machine Learning Approach for Runoff Prediction
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:13
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.
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| 8. |
- Vishwakarma, Dinesh Kumar, et al.
(författare)
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Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test
- 2023
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental and crucial component of water resource system engineering. Since the continuous measurement is often impossible, the stage-discharge relationship is generally used in natural streams to estimate discharge. This paper aims to optimize the rating curve using a generalized reduced gradient (GRG) solver and the test the accuracy and applicability of the hybridized linear regression (LR) with other machine learning techniques, namely, linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) and linear regression-M5 pruned (LR-M5P) models. An application of these hybrid models was performed and test to modeling the Gaula Barrage stage-discharge problem. For this, 12-year historical stage-discharge data were collected and analyzed. The 12-year historical daily flow data (m3/s) and stage (m) from during the monsoon season, i.e., June to October only from 03/06/2007 to 31/10/2018, were used for discharge simulation. The best suitable combination of input variables for LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models was identified and decided using the gamma test. GRG-based rating curve equations were found to be as effective and more accurate as conventional rating curve equations. The outcomes from GRG, LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models were compared to observed values of daily discharge based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index of Agreement (d), Kling-Gupta efficiency (KGE), mean absolute error (MAE), mean bias error (MBE), relative bias in percent (RE), root mean square error (RMSE) Pearson correlation coefficient (PCC) and coefficient of determination (R2). The LR-REPTree model (combination 1: NSE = 0.993, d = 0.998, KGE = 0.987, PCC(r) = 0.997, and R2 = 0.994 and minimum value of RMSE = 0.109, MAE = 0.041, MBE = −0.010 and RE = −0.1%; combination 2; NSE = 0.941, d = 0.984, KGE = 0. 923, PCC(r) = 0. 973, and R2 = 0. 947 and minimum value of RMSE = 0. 331, MAE = 0.143, MBE = −0.089 and RE = −0.9%) performed superior to the GRG, LR, LR-RSS, LR-SVM, and LR-M5P models in all input combinations during the testing period. It was also noticed that the performance of the alone LR and its hybrid models (i.e., LR-RSS, LR-REPTree, LR-SVM, and LR-M5P) was better than the conventional stage-discharge rating curve, including the GRG method.
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| 9. |
- Abba, S.I., et al.
(författare)
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Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling
- 2022
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffeefficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.
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| 10. |
- Abbas, Nahla, et al.
(författare)
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Flow Variation of the Major Tributaries of Tigris River Due to Climate Change
- 2019
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Ingår i: Engineering. - : Scientific Research Publishing. - 1947-3931 .- 1947-394X. ; 11:8, s. 437-442
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Tidskriftsartikel (refereegranskat)abstract
- Iraq relies greatly on the flow of the Euphrates and Tigris Rivers and their tributaries. Five tributaries namely Khabour, Greater Zab, Lesser Zab, AlAd- hiam and Daylia, which are the major tributaries of Tigris River, sustain Northern Iraq Region, a semi-arid, mainly a pastureland. These tributaries contribute about 24 km3 of water annually. The discharge in the tributaries, in recent times, has been suffering increasing variability contributing to more severe droughts and floods apparently due to climate change. This is because there were no dams constructed outside Iraq previously. For an appropriate appreciation, Soil Water Assessment Tool (SWAT) model was used to evaluate the impact of climate change on their discharge for a half-centennial lead time to 2046-2064 and a centennial lead time to 2080-2100. The suitability of the model was first evaluated, and then, outputs from six GCMs were incorporated to evaluate the impacts of climate change on water resources under three emission scenarios: A1B, A2 and B1. The results showed that wa-ter resources are expected to decrease with time.
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