Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm

Banadkooki, Fatemeh Barzegari; Ehteram, Mohammad; Ahmed, Ali Najah; Teo, Fang Yenn; Ebrahimi, Mahboube; Fai, Chow Ming; Huang, Yuk Feng; El-Shafie, Ahmed

doi:10.1007/s11356-020-09876-w

Cited by 80 publications

(12 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Banadkooki et al . 19 performed research investigating SSL estimation in the Goorganrood basin, Iran using an ANN model hybridized with the ant lion optimization algorithm (ALO). Two other hybrid ANN models were also studied: ANN-PSO and ANN-BA, which are ANNs hybridized with the particle swarm optimization (PSO) and the bat algorithm (B.A.).…”

Section: Literature Reviewmentioning

confidence: 99%

Suspended sediment load prediction using long short-term memory neural network

AlDahoul

Essam

Kumar

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Suspended sediment load prediction using long short-term memory neural network

AlDahoul

Essam

Kumar

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…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]. Therefore, future work could be carried out to develop these recent optimization techniques and explore their performances in optimizing the release from the reservoir to meet the downstream demand.…”

Section: Reliability and Risk Analysismentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…It is necessary to consider two technical aspects in order to integrate the optimization algorithms with MLP, namely, the method for encoding the agents/solutions and the procedure for determining the objective function. Although the standalone MLP models have high ability, their training algorithms may have slow convergence or may trap in local optimums [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. erefore, it is essential to improve the accuracy of the MLP models.…”

Section: Optimization Algorithms For Training Mlpsmentioning

confidence: 99%

“…Optimization algorithms are considered as suitable alternatives for traditional training algorithms due to their advanced operators, which avoid trapping in local optimums. e optimization algorithms are widely used for training soft computing models [19][20][21][22][23][24][25][26]. e genetic algorithm (GA) and particle swarm optimizations (PSO) are powerful optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

2021

View full text Add to dashboard Cite

The aim of this study is to evaluate the ability of soft computing models including multilayer perceptron- (MLP-) water wave optimization (MLP-WWO), MLP-particle swarm optimization (MLP-PSO), and MLP-genetic algorithm (MLP-GA), to simulate the daily and monthly reference evapotranspiration (ET) at the Aidoghmoush basin (Iran). Principal component analysis (PCA) was used to find the best input combination including the lagged ETs. According to the results, the ET values with 1, 2, and 3 (days) lags as well as those with 1, 2, and 3 (months) lags were the most effective variables in the formation of the PCs. The total variance proportion of inputs and eigenvalues was used to identify the most important variables. The accuracy of the models was assessed based on multiple statistical indices such as the mean absolute error (MAE), Nash–Sutcliff efficiency (NSE), and percent bias (PBIAS). The results showed that the performance of hybrid MLP models was better than that of the standalone MLP. The findings confirmed that the MLP-WWO could precisely predict ET.

show abstract

Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm

Cited by 80 publications

References 43 publications

Suspended sediment load prediction using long short-term memory neural network

Suspended sediment load prediction using long short-term memory neural network

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

Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

Contact Info

Product

Resources

About