Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Tarate Suryakant Bajirao,Pravendra Kumar,Manish Kumar,Ahmed Elbeltagi,Alban Kuriqi

doi:10.3390/su13020542

Abstract

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

Highlights

To prevent soil degradation and improve the water quality, the soil and water management treatments must be carried out at the watershed level
It was observed that the performance of the Coiflet wavelet-coupled Adaptive Neuro-Fuzzy Inference System (ANFIS) model is superior to other models and can be applied for daily
Different statistical parameters of original rainfall, runoff, and suspended sediment concentration (SSC) time series used in thisTable analysis are presented

Summary

Introduction

To prevent soil degradation and improve the water quality, the soil and water management treatments must be carried out at the watershed level. Sediment loss measurement is crucial to assess soil and water conservation treatments on sediment flow through the river [1,2,3]. Sediment transport information is essential for designing and planning soil and water conservation structures on the river. The sediment movement’s magnitude depends on rainfall volume and intensity, Land Use/Land Cover (LULC), topography, and soil physical properties [4]. Sediment concentration flow prediction is important because the accumulation of sediments reduces the reservoir capacity. It reduces the productivity of such land over which sedimentation takes place. Sedimentation is responsible for increasing the flood hazard due to sediment mass trapping over the river bed [2,5]

Methods

Discussion

Conclusion