A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem

Ahmed, Ali Najah; Lam, To Van; Hùng, Nguyễn Duy; Thieu, Nguyen Van; Kişi, Özgür; El‐Shafie, Ahmed

doi:10.1016/j.asoc.2021.107282

Cited by 71 publications

(21 citation statements)

References 94 publications

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“…Four performance measures are selected to evaluate the models’ performances, namely the mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R 2 ), and ranking mean (RM). MAE, RMSE, and R 2 have been commonly used in SSL prediction studies 2 , 8 – 10 , 19 , 21 , 22 , 24 , 37 , 38 , 69 , while RM was used by Ahmed et al 70 as a method to rank overall model performance.…”

Section: Methodsmentioning

confidence: 99%

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Essam

Huang

Birima

et al. 2022

Sci Rep

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High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia.

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Section: Methodsmentioning

confidence: 99%

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Essam

Huang

Birima

et al. 2022

Sci Rep

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“…For PSO, the inertia weight was set to linearly decrease from 0.9 to 0.4. Two coefficient values were both set to 2 (Ahmed et al 2021). For WCA, the total number of rivers and seas and the maximum allowable distance between the river and sea were set to 10 and 1e-3 (Eskandar et al 2012;Zhang et al 2021b), respectively.…”

Section: Cv-metaheuristic-svr and Nonparametric Friedman Test For Enh...mentioning

confidence: 99%

“…As listed in Table 7 in the Appendix, in previous performance comparisons, only the deterministic optimal estimation was considered, and only a single-run comparison was conducted. However, due to the inherent stochastic nature of these algorithms (Gao et al 2020;Ahmed et al 2021), the same metaheuristic algorithm may yield different optimal solutions in multiple runs (Ahmed et al 2021). The solutions in even superior models deviate strongly for a given case, which means that ideal results from a single run are hard to replicate on similar cases.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Xia

Guo

et al. 2022

Landslides

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Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and water cycle algorithm (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), metaheuristic support vector regression (SVR), and the nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, and convergence. The results obtained for the Shuping and Baishuihe landslides demonstrate that the hybrid approach can be utilized to determine the optimum hyperparameters and present statistical significance, thus enhancing accuracy and reliability in ML-based prediction. Significant differences were observed among the five metaheuristics. Based on the Friedman test, which was performed on the root mean square error (RMSE), Kling-Gupta efficiency (KGE), and computational time, PSO is recommended for hyperparameter tuning for SVR-based displacement prediction due to its ability to maintain a balance between precision, computational time, and robustness. The nonparametric Friedman test is promising for presenting statistical significance, thus enhancing reproducibility.

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“…They found that the JA technique outperformed other selected algorithms compared with searching for the best solution. In addition to that, recently, different advanced optimization techniques are proposed as robust techniques to be used in searching for the best solution in dealing with water resources issues [26]. For example, the waterdrop optimization technique [38], the whale optimization algorithm [39], the ant lion optimization algorithm [40], the nomadic people optimization algorithm [41], the Harris hawks optimization algorithm [42], and the grey wolf optimization technique [27].…”

Section: Reliability and Risk Analysismentioning

confidence: 99%

“…Metaheuristic techniques proved to be more reliable for dam operation [24,25]. However, some of the developed optimization algorithms have some limitations, such as a low rate of converging, and require extensive parameters for isolating and preventing problems [26]. Hence, there is a need to investigate reliable optimization techniques, overcoming the drawbacks of other optimization techniques, to ensure no overdraw from dams occurs and improve flood control planning and design requirements for the respective basins; optimizing operation release management strategies for dams must be achieved to resolve the issue.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia

et al. 2021

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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.

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A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem

Cited by 71 publications

References 94 publications

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia

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