Coupling wavelet transform with multivariate adaptive regression spline for simulating suspended sediment load: Independent testing approach

Shiri, Naser; Shiri, Jalal; Nourani, Vahid; Karimi, Shahram

doi:10.1080/09715010.2020.1801528

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ISH Journal of Hydraulic Engineering

2020

DOI: 10.1080/09715010.2020.1801528

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Coupling wavelet transform with multivariate adaptive regression spline for simulating suspended sediment load: Independent testing approach

Naser Shiri

Jalal Shiri

Vahid Nourani

et al.

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Cited by 14 publications

References 55 publications

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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

Katipoğlu,

Kartal,

Pande

2024

Stoch Environ Res Risk Assess

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The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

show abstract

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

Katipoğlu,

Kartal,

Pande

2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction

Sahoo

Sankalp

Kisi

2023

Water Resour Manage

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Regression models for sediment transport in tropical rivers

Harun

Safari

Gül

et al. 2021

Environ Sci Pollut Res

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Coupling wavelet transform with multivariate adaptive regression spline for simulating suspended sediment load: Independent testing approach

Cited by 14 publications

References 55 publications

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction

Regression models for sediment transport in tropical rivers

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