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A New Method for Flood Routing Utilizing Four-Parameter Nonlinear Muskingum and shark algorithm

WATER RESOURCES MANAGEMENT 2019年第14期33卷 4879-4893页

作者： Farahani, Nazanin Karami, Hojat Farzin, Saeed Ehteram, Mohammad Kisi, Ozgur El Shafie, Ahmad Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Ilia State Univ Sch Technol Tbilisi GA USA Univ Malaya Fac Engn Civil Engn Dept Kuala Lumpur Malaysia

Considering the great importance of flood prediction, flood routing based on shark algorithm (SA) and Four-Parameter Nonlinear Muskingum (FPNM) has been proposed in the present study. In fact, the Muskingum model is considered as one the most efficient method for predicting flood. However, to successfully implement Muskingum Model, there is a need to compute various parameters of this model utilizing a lot of data that characterized the physical features of the catchment. Therefore, there is a need to integrate the Muskingum model with an optimization method. Nevertheless, there are several drawbacks including trapping in local optima, overhead response and convergence time-consuming have been experienced using the existing optimization methods. Therefore, in this study, a proposal for utilizing an integrated evolutionary computing method namely;SA with FPNM has been introduced to overcome such drawbacks. Three case studies based on the definition of objective functions and different error indices were used to evaluate the algorithm. The results showed that SA significantly reduced the sum of the total square deviations (SSQs) and the sum of absolute deviation (SAD) between the predicted and observed discharges compared to other evolutionary algorithms. Moreover, the proposed model achieved high ability to accurately determine the peak value and peak time of the discharge. In addition, the calculated hydrodynamic shape has a high correlation with observed hydrographs.

关键词： Flood routing shark algorithm Muskingum model Peak discharge

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Optimizing dam and reservoirs operation based model utilizing shark algorithm approach

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KNOWLEDGE-BASED SYSTEMS 2017年 122卷 26-38页

作者： Ehteram, Mohammed Karami, Hojat Mousavi, Sayed-Farhad El-Shafie, Ahmed Amini, Zahra Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur Malaysia Univ Technol Malaysia Fac Engn Dept Civil Engn Kuala Lumpur Malaysia

Computational intelligence (CI) is a fast evolving area in which many novel algorithms, stemmed from various inspiring sources, were developed during the past decade. Nevertheless, many of them are dispersed in different research directions, and their true potential is thus not fully utilized yet. Therefore, there is a need to investigate the potential of these methods in different engineering optimization problems. In fact, shark algorithm is a stochastic search optimization algorithm which is started first in a set of random generated potential solutions, and then performs the search for the optimum one interactively. Such procedure is appropriate to the system features of the reservoir system as it is a stochastic system in nature. In this article, investigation of the potential of shark algorithm is examined as an optimization algorithm for reservoir operation. To achieve that real single reservoir and multi-reservoir optimal operations have been performed utilizing shark algorithm. Many performances indexes have been measured for each case study utilizing the proposed shark algorithm and another existing optimization algorithms namely, Genetic algorithm (GA) and Particle Swarm Optimization (PSO). The results showed that the proposed shark algorithm outperformed the other algorithms and achieved higher reliability index and lesser vulnerability index. Moreover, standard deviation and coefficient of variation in shark algorithm were less than the other two algorithms, which indicates its superiority. (C) 2017 Elsevier B.V. All rights reserved.

关键词： shark algorithm Multiple-reservoir systems Genetic algorithm Particle swarm algorithm

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Novel reservoir system simulation procedure for gap minimization between water supply and demand

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JOURNAL OF CLEANER PRODUCTION 2019年 206卷 928-943页

作者： Allawi, Mohammed Falah Jaafar, Othman Hamzah, Firdaus Mohamad El-Shafie, Ahmed Univ Kebangsaan Malaysia Fac Engn & Built Environm Civil & Struct Engn Dept Bangi 43600 Selangor Darul Malaysia Univ Malaya Dept Civil Engn Fac Engn Jalan Univ Kuala Lumpur 50603 Wilayah Perseku Malaysia

In recent years, with the quick growth of the economy and living standards in Malaysia, keeping up with the water demand is essential for the growth of cultivation, domestic and industrial. With the merits of having dams and reservoirs, water releases from dams are usually used to respond to the water requirements of downstream dams. To match the practical water requirement considering spatial and temporal conditions, a novel optimization operation model has been formulated for minimizing the gap between the water release from a dam and the water requirement. In this context, there is a need to develop an optimization model to alleviate the complexity and multidimensionality of a dam and reservoir as water supplies and the water demand system. In this research, an optimization algorithm, namely, the shark machine learning algorithm (SMLA) that has high inertia for obtaining its targets, is proposed that mimics the natural shark process. The major objective for the proposed model is attaining the minimum difference between the water demand volume and water release. To examine the proposed model, SMLA has been utilized in determining the optimal operation policies for Timah Tasoh Darn, located in Malaysia. A new procedure to evaluate the performance of optimization models by integrating reservoir inflow forecasting with operational rules generated by optimization models has been proposed. Accordingly, two predictive models, namely, radial basis function neural network (RBF-NN) and support vector regression (SVR), have been developed to forecast monthly reservoir inflow. The test results revealed that the SVR forecasts monthly reservoir inflow better than the RBF-NN model. Additionally, the SMLA attained more reliable, resilient and less vulnerable results in the operation of the reservoir system compared to that of other optimization models. In addition, SMLA has demonstrated a significant change in the performance indicator values when using forecasted reservoir infl

关键词： Reservoir operation Artificial intelligent Optimization shark algorithm Water management

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Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply

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JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA 2018年第1期67卷 54-67页

作者： Ehteram, Mohammad Karami, Hojat Mousavi, Sayed-Farhad Farzin, Saeed Kisi, Ozgur Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Ilia State Univ Sch Nat Sci & Engn Tiblisi Georgia

This study evaluates three contemporary evolutionary algorithms, namely, shark, genetic, and particle swarm algorithms, for optimization in reservoir operation and water supply. The Klang gate dam in Malaysia is selected as the case study to optimize reservoir operation. The key objective of this study is the minimization of water deficits based on demands and released water. The global solution of the problem is computed based on software Lingo and the average solution of the shark algorithm is able to attain 99% of global solution. As well, the shark algorithm can furnish demand values at a faster convergence rate than both genetic and particle swarm algorithms. The reliability index and resiliency index, as useful indices in water resource management, are used and the values of these indices have the highest percent for the shark algorithm, indicating its superiority over other evolutionary algorithms.

关键词： genetic algorithm particle swarm algorithm shark algorithm water resource management

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Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

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ENERGIES 2019年第14期12卷 2730页

作者： Ehteram, Mohammad Ahmed, Ali Najah Fai, Chow Ming Afan, Haitham Abdulmohsin El-Shafie, Ahmed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan *** Iran Univ Tenaga Nas IEI Kajang 43000 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia

The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash-Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorith

关键词： renewable energy forecasting solar radiation shark algorithm particle swarm optimization ANFIS

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Optimization of Chain-Reservoirs' Operation with a New Approach in Artificial Intelligence

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WATER RESOURCES MANAGEMENT 2017年第7期31卷 2085-2104页

作者： Ehteram, Mohammad Allawi, Mohammed Falah Karami, Hojat Mousavi, Sayed-Farhad Emami, Mohammad EL-Shafie, Ahmed Farzin, Saeed Semnan Univ Dept Water Engn & Hydraul Struct Semnan Iran Univ Kebangsaan Malaysia Fac Engn & Built Environm Civil & Struct Engn Dept Bangi 43600 Selangor Malaysia Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur Malaysia

Operation of reservoirs and power plants for better management of water resources and production of hydro-electric energy has been the objective of many studies. In this research, shark algorithm is used for management of water resources and hydro-electric plants. After the introduction of this procedure, the algorithm is applied to some complex cases such as Karun-4 reservoir, 4-reservoir system, 10-reservoir system and another one including 26 power plants. In the Karun-4 case, the aim was to reduce water shortages and the results obtained from shark algorithm were in 100% compliance with the absolute optimum answer obtained from Lingo software and non-linear method. This was the best solution to the problem to date in the published researches. In the 4-reservoir system, the objective was to increase the profit from the reservoirs. The shark algorithm yielded a value of 1194.64, which is the best answer to date to the question. In regard to the energy production by the 26 power plants, the shark algorithm yielded 40% more energy, compared to genetic algorithm.

关键词： Artificial intelligence water resources management Optimization Cascade reservoirs shark algorithm

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