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In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.
Sarmad Latif; Suzlyana Marhain; Shabbir Hossain; Ali Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia. Sustainability 2021, 13, 5900 .
AMA StyleSarmad Latif, Suzlyana Marhain, Shabbir Hossain, Ali Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia. Sustainability. 2021; 13 (11):5900.
Chicago/Turabian StyleSarmad Latif; Suzlyana Marhain; Shabbir Hossain; Ali Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2021. "Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia." Sustainability 13, no. 11: 5900.
Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.
Mohammad Ehteram; El- Shafie; Lai Sai Hin; Faridah Othman; Suhana Koting; Hojat Karami; Sayed-Farhad Mousavi; Saeed Farzin; Ali Najah Ahmed; Mohd Hafiz Bin Zawawi; Shabbir Hossain; Nuruol Syuhadaa Mohd; Haitham Abdulmohsin Afan; Hin; Bin Zawawi; Mohd; Afan; Amr H. El-Shafie; Ahmed El-Shafie. Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model. Applied Sciences 2019, 9, 3960 .
AMA StyleMohammad Ehteram, El- Shafie, Lai Sai Hin, Faridah Othman, Suhana Koting, Hojat Karami, Sayed-Farhad Mousavi, Saeed Farzin, Ali Najah Ahmed, Mohd Hafiz Bin Zawawi, Shabbir Hossain, Nuruol Syuhadaa Mohd, Haitham Abdulmohsin Afan, Hin, Bin Zawawi, Mohd, Afan, Amr H. El-Shafie, Ahmed El-Shafie. Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model. Applied Sciences. 2019; 9 (19):3960.
Chicago/Turabian StyleMohammad Ehteram; El- Shafie; Lai Sai Hin; Faridah Othman; Suhana Koting; Hojat Karami; Sayed-Farhad Mousavi; Saeed Farzin; Ali Najah Ahmed; Mohd Hafiz Bin Zawawi; Shabbir Hossain; Nuruol Syuhadaa Mohd; Haitham Abdulmohsin Afan; Hin; Bin Zawawi; Mohd; Afan; Amr H. El-Shafie; Ahmed El-Shafie. 2019. "Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model." Applied Sciences 9, no. 19: 3960.
Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.
Abobakr Saeed Abobakr Yahya; Ali Najah Ahmed; Faridah Binti Othman; Rusul Khaleel Ibrahim; Haitham Abdulmohsin Afan; Amr El-Shafie; Chow Ming Fai; Shabbir Hossain; Mohammad Ehteram; Ahmed Elshafie. Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios. Water 2019, 11, 1231 .
AMA StyleAbobakr Saeed Abobakr Yahya, Ali Najah Ahmed, Faridah Binti Othman, Rusul Khaleel Ibrahim, Haitham Abdulmohsin Afan, Amr El-Shafie, Chow Ming Fai, Shabbir Hossain, Mohammad Ehteram, Ahmed Elshafie. Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios. Water. 2019; 11 (6):1231.
Chicago/Turabian StyleAbobakr Saeed Abobakr Yahya; Ali Najah Ahmed; Faridah Binti Othman; Rusul Khaleel Ibrahim; Haitham Abdulmohsin Afan; Amr El-Shafie; Chow Ming Fai; Shabbir Hossain; Mohammad Ehteram; Ahmed Elshafie. 2019. "Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios." Water 11, no. 6: 1231.
The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered.
Mohammed Falah Allawi; Faridah Binti Othman; Haitham Abdulmohsin Afan; Ali Najah Ahmed; Shabbir Hossain; Chow Ming Fai; Ahmed El-Shafie. Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods. Water 2019, 11, 1226 .
AMA StyleMohammed Falah Allawi, Faridah Binti Othman, Haitham Abdulmohsin Afan, Ali Najah Ahmed, Shabbir Hossain, Chow Ming Fai, Ahmed El-Shafie. Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods. Water. 2019; 11 (6):1226.
Chicago/Turabian StyleMohammed Falah Allawi; Faridah Binti Othman; Haitham Abdulmohsin Afan; Ali Najah Ahmed; Shabbir Hossain; Chow Ming Fai; Ahmed El-Shafie. 2019. "Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods." Water 11, no. 6: 1226.
The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.
Mohammad Ehteram; Haitham Abdulmohsin Afan; Mojgan Dianatikhah; Ali Najah Ahmed; Chow Ming Fai; Shabbir Hossain; Mohammed Falah Allawi; Ahmed Elshafie; Afan; Fai. Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors. Water 2019, 11, 1130 .
AMA StyleMohammad Ehteram, Haitham Abdulmohsin Afan, Mojgan Dianatikhah, Ali Najah Ahmed, Chow Ming Fai, Shabbir Hossain, Mohammed Falah Allawi, Ahmed Elshafie, Afan, Fai. Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors. Water. 2019; 11 (6):1130.
Chicago/Turabian StyleMohammad Ehteram; Haitham Abdulmohsin Afan; Mojgan Dianatikhah; Ali Najah Ahmed; Chow Ming Fai; Shabbir Hossain; Mohammed Falah Allawi; Ahmed Elshafie; Afan; Fai. 2019. "Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors." Water 11, no. 6: 1130.
One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.
Mahdi Valikhan-Anaraki; Sayed-Farhad Mousavi; Saeed Farzin; Hojat Karami; Mohammad Ehteram; Ozgur Kisi; Chow Ming Fai; Shabbir Hossain; Gasim Hayder; Ali Najah Ahmed; Amr H. El-Shafie; Huzaifa Bin Hashim; Haitham Abdulmohsin Afan; Sai Hin Lai; Ahmed El-Shafie. Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies. Sustainability 2019, 11, 2337 .
AMA StyleMahdi Valikhan-Anaraki, Sayed-Farhad Mousavi, Saeed Farzin, Hojat Karami, Mohammad Ehteram, Ozgur Kisi, Chow Ming Fai, Shabbir Hossain, Gasim Hayder, Ali Najah Ahmed, Amr H. El-Shafie, Huzaifa Bin Hashim, Haitham Abdulmohsin Afan, Sai Hin Lai, Ahmed El-Shafie. Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies. Sustainability. 2019; 11 (8):2337.
Chicago/Turabian StyleMahdi Valikhan-Anaraki; Sayed-Farhad Mousavi; Saeed Farzin; Hojat Karami; Mohammad Ehteram; Ozgur Kisi; Chow Ming Fai; Shabbir Hossain; Gasim Hayder; Ali Najah Ahmed; Amr H. El-Shafie; Huzaifa Bin Hashim; Haitham Abdulmohsin Afan; Sai Hin Lai; Ahmed El-Shafie. 2019. "Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies." Sustainability 11, no. 8: 2337.
Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide.
Zaher Mundher Yaseen; Mohammad Ehteram; Shabbir Hossain; Chow Ming Fai; Suhana Binti Koting; Nuruol Syuhadaa Mohd; Wan Zurina Binti Jaafar; Haitham Abdulmohsin Afan; Lai Sai Hin; Nuratiah Zaini; Ali Najah Ahmed; Ahmed El-Shafie. A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems. Sustainability 2019, 11, 1953 .
AMA StyleZaher Mundher Yaseen, Mohammad Ehteram, Shabbir Hossain, Chow Ming Fai, Suhana Binti Koting, Nuruol Syuhadaa Mohd, Wan Zurina Binti Jaafar, Haitham Abdulmohsin Afan, Lai Sai Hin, Nuratiah Zaini, Ali Najah Ahmed, Ahmed El-Shafie. A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems. Sustainability. 2019; 11 (7):1953.
Chicago/Turabian StyleZaher Mundher Yaseen; Mohammad Ehteram; Shabbir Hossain; Chow Ming Fai; Suhana Binti Koting; Nuruol Syuhadaa Mohd; Wan Zurina Binti Jaafar; Haitham Abdulmohsin Afan; Lai Sai Hin; Nuratiah Zaini; Ali Najah Ahmed; Ahmed El-Shafie. 2019. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems." Sustainability 11, no. 7: 1953.
Water scarcity is a serious problem throughout the world. One critical part of this problem is supplying sufficient water to meet irrigation demands for agricultural production. The present study introduced an improved weed algorithm for reservoir operation with the aim of decreasing irrigation deficits. The Aswan High Dam, one of the most important dams in Egypt, was selected for this study to supply irrigation demands. The improved weed algorithm (IWA) had developed local search ability so that the exploration ability for the IWA increased and it could escape from local optima. Three inflows (low, medium and high) to the reservoir were considered for the downstream demands. For example, the average solution for the IWA at high inflow was 0.985 while it was 1.037, 1.040, 1.115 and 1.121 for the weed algorithm (WA), bat algorithm (BA), improved particle swarm optimization algorithm (IPSOA) and genetic algorithm (GA). This meant that the IWA decreased the objective function for high inflow by 5.01%, 5.20%, 11.65% and 12% compared to the WA, BA, IPSOA and GA, respectively. The computational time for the IWA at high inflow was 22 s, which was 12%, 18%, 24% and 29% lower than the WA, BA, IPSOA and GA, respectively. Results indicated that the IWA could meet the demands at all three inflows. The reliability index for the IWA for the three inflows was greater than the WA, BA, IPSOA and GA, meaning that the released water based on IWA could well supply the downstream demands. Thus, the improved weed algorithm is suggested for solving complex problems in water resources management.
Mohammad Ehteram; Vijay P. Singh; Hojat Karami; Khosrow Hosseini; Mojgan Dianatikhah; Shabbir Hossain; Chow Ming Fai; Ahmed El-Shafie. Irrigation Management Based on Reservoir Operation with an Improved Weed Algorithm. Water 2018, 10, 1267 .
AMA StyleMohammad Ehteram, Vijay P. Singh, Hojat Karami, Khosrow Hosseini, Mojgan Dianatikhah, Shabbir Hossain, Chow Ming Fai, Ahmed El-Shafie. Irrigation Management Based on Reservoir Operation with an Improved Weed Algorithm. Water. 2018; 10 (9):1267.
Chicago/Turabian StyleMohammad Ehteram; Vijay P. Singh; Hojat Karami; Khosrow Hosseini; Mojgan Dianatikhah; Shabbir Hossain; Chow Ming Fai; Ahmed El-Shafie. 2018. "Irrigation Management Based on Reservoir Operation with an Improved Weed Algorithm." Water 10, no. 9: 1267.