Using Metaheuristics Algorithms (MHAs) to Optimize Water Supply Operation in Reservoirs: a Review

Almubaidin, Mohammad Abdullah Abid; Ahmed, Ali Najah; Sidek, Lariyah Bte Mohd; El‐Shafie, Ahmed

doi:10.1007/s11831-022-09716-9

Cited by 16 publications

(7 citation statements)

References 158 publications

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“…These findings further support the hypothesis reported in the literature [60,61] that hybrid-based algorithms can achieve higher accuracy, better stability, and dependability for addressing real-world issues without getting stuck in local minima. Further research should be done to investigate the hybrid-based metaheuristic algorithms with other ML models such as RF and ELM.…”

Section: Discussionsupporting

confidence: 89%

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Khairan,

Zubaidi,

Al-Mukhtar

et al. 2023

Sustainability

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Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), maximum absolute error (MAE), and normalised mean standard error (NMSE). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R2 = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique.

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Section: Discussionsupporting

confidence: 89%

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Khairan,

Zubaidi,

Al-Mukhtar

et al. 2023

Sustainability

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“…In the field of water resources, various metaheuristic algorithms have been employed to address this issue. These methods often involve modifying existing algorithms or creating hybrid algorithms, ultimately contributing to the reduction of water deficits in reservoirs 75 . In this study, we employ and integrate three algorithms, which are described below.…”

Section: Methodsmentioning

confidence: 99%

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Martinho,

Hippert,

Goliatt

2023

Sci Rep

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Accurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioinspired optimization algorithms (BOA) for short-term multistep streamflow forecasting. Specifically, we focus on applying XGB, MARS, ELM, EN, and SVR models and various BOA, including PSO, GA, and DE, for selecting model parameters. The performances of the resulting hybrid models are compared using performance statistics, graphical analysis, and hypothesis testing. The results show that the hybridization of BOA with ML models demonstrates significant potential as a data-driven approach for short-term multistep streamflow forecasting. The PSO algorithm proved superior to the DE and GA algorithms in determining the optimal hyperparameters of ML models for each step of the considered time horizon. When applied with all BOA, the XGB model outperformed the others (SVR, MARS, ELM, and EN), best predicting the different steps ahead. XGB integrated with PSO emerged as the superior model, according to the considered performance measures and the results of the statistical tests. The proposed XGB hybrid model is a superior alternative to the current daily flow forecast, crucial for water resources planning and management.

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“…The shortage of available water resources daily with the increased need for them due to population increase, climate change, the development of agriculture, massive industry and pollution of available water resources are among the important problems that trouble decision-makers in the world. The shortage of available water resources encourages the e cient management and operation of reservoirs to achieve maximum bene t from them through a management approach planning for the current and future situation (Almubaidin et al, 2022;Chong et al, 2021). The main task of operating the reservoir is to determine the amount of water that should be released in the current situation and the amount of water that should be retained under certain conditions in the future for various purposes using the information available or expected.…”

Section: Introductionmentioning

confidence: 99%

“…The optimum operating plan for the reservoir is based on the desired objective of the reservoir, such as water supply, power generation, ood control, shipping, and environmental purposes. The optimal rule curve for reservoirs may be found using the average water shortage as the objective function (Almubaidin et al, 2022;Kangrang et al, 2009). The rule curves (upper and lower) simulate the water released from the reservoir in response to changing demands caused by hydraulic conditions or changing water demand.…”

Section: Introductionmentioning

confidence: 99%

Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms

Almubaidin,

Ahmed,

Sidek

et al. 2024

Water Resour Manage

Self Cite

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Recently, there has been increased interest in using optimization techniques to nd the optimal operation for reservoirs by applying them to various aspects of the reservoir operating system, such as nding the optimal rule curves for reservoirs. The use of different algorithms (arti cial bee colony (ABC), particle swarm optimization (PSO), genetic algorithm (GA), re y algorithm (FA), invasive weed optimization (IWO), teaching learning-based optimization (TLBO), and harmony search (HS)) was investigated in this study by integrated every algorithm to a reservoir simulation model to search for the optimal rule curves for the Mujib reservoir in Jordan from the year 2004 to 2019. To evaluate the long-term impact of water shortages and excess releases on the Mujib reservoir's operation and the effect of water demand management when reducing it by 10%, 20%, and 30% in the reservoir. The ndings showed that the algorithms effectively reduced cases of water shortage and excess release compared to the current operation. The best solutions using the TLBO algorithm reduced the frequency and average of the water shortage to 55.09 % and 56.26 %, respectively, and reduced the frequency and the average of the excess release to 63.16 % and 73.31 %, respectively. The ndings highlight the impact of water demand management of the reservoir on the decrease in frequency and average of the water shortage, explaining the inability of the reservoir to supply water in some months and the possibility of exposure to a shortage of water for long periods. Highlights 1. Simulation of the reservoir system model (In ow, Evaporation, Release, Seepage).2. Find the optimal operating policy using optimizing algorithms.3. Comparison of the performance of the algorithms used in terms of nding the optimal rule curves. 4. Evaluation of the effect of the optimal rule curves in reducing drought and ood cases.

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Using Metaheuristics Algorithms (MHAs) to Optimize Water Supply Operation in Reservoirs: a Review

Cited by 16 publications

References 158 publications

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms

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