Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

Bouzeria, Housseyn; Ghenim, Abderrahmane Nekkache; Khanchoul, Kamel

doi:10.1515/jwld-2017-0018

Cited by 27 publications

(6 citation statements)

References 18 publications

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“…The multi-layer perceptron consists of an output layer, hidden layers and input layers. Depending on the type of the dataset, topology and statistical errors in the ANN, the count of the hidden layers may be one or more [88]. Each layer in the network involves a precise number of neurons associated with connection weights and activation functions.…”

Section: Annmentioning

confidence: 99%

Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India

Yadav

Joshi

Kumar

et al. 2022

Water

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Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial intelligence models for SSY prediction in Godavari River Basin, India. The genetic algorithm (GA), hybridized with an artificial neural network (ANN) (GA-ANN), is a suitable artificial intelligence model for SSY prediction. The GA is used to concurrently optimize all ANN’s parameters. The GA-ANN was developed using daily water discharge, with water level as the input data to estimate the daily SSY at Polavaram, which is the farthest gauging station in the downstream of the Godavari River Basin. The performances of the GA-ANN model were evaluated by comparing with ANN, sediment rating curve (SRC) and multiple linear regression (MLR) models. It is observed that the GA-ANN contains the highest correlation coefficient (0.927) and lowest root mean square error (0.053) along with lowest biased (0.020) values among all the comparative models. The GA-ANN model is the most suitable substitute over traditional models for SSY prediction. The hybrid GA-ANN can be recommended for estimating the SSY due to comparatively superior performance and simplicity of applications.

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Section: Annmentioning

confidence: 99%

Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India

Yadav

Joshi

Kumar

et al. 2022

Water

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show abstract

“…It is also applicable in all the situations where there is a nonlinear relation between a predictive variable and a predicted variable. (Bouzeria et al, 2017). The ANNs are generally made up three layers as input, hidden and output which are linked to artificial neurons.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Modeling daily suspended sediment load of rivers using artificial intelligence approaches

Shabani

2022

Preprint

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Knowing the amount of suspended sediment loda (SSL) carried by rivers is an important factor in watershed management. Hence, it is necessary to measure or estimate its amount accurately. In this paper, it was attempted to apply three artificial intelligence approaches including artificial neural network (ANN), artificial neural network combined with particle swarm optimization (ANN-PSO) and long short-term memory (LSTM) to predict the daily SSL, using the data of Jamal-Beig hydrometric station in Kharestan watershed, Fars province, Iran. To achieve this goal, the daily data of SSL (Qs) and flow (Qf) were collected during 30 years (1992-2020). Eighty percent of data was considered for the training phase and 20% for the validation phase. The performance of the models was evaluated using the RMSE, R, NSE and PBIAS criteria according to the estimated and measured SSL values. The results showed that the ANN-PSO model with lower values of PBIAS = -1.048% and RMSE = 26.494 ton/day and high values of NSE = 0.827 and R = 0.912 provides the best performance compared to ANN and LSTM models for estimating daily SSL in Kharestan watershed.

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“…An extensive review of ANN applications in the hydrological field for the estimation and prediction of numerous hydrological parameters has been acknowledged by the ASCE Task Committee [30,31]. In the last two decades, studies have shown that artificial neural networks (ANNs) have promising results in terms of modelling and forecasting streamflow [32,33], reservoir water level [34,35], and suspended sediment in rivers [2,[36][37][38]. The ANN model is especially employed where basic physical interactions are not entirely known, but there are sufficient data to train a network.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

2021

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A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirna and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R2 of 0.92 and a PBIAS of −0.59%. Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal.

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Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

Cited by 27 publications

References 18 publications

Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India

Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India

Modeling daily suspended sediment load of rivers using artificial intelligence approaches

Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

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