Vol. 8, Special Issue 8, Part I (2024)

Abstract: The present work endeavours to tackle the paramount requirement of precise sediment load forecasts in diverse water resource management contexts, encompassing dam construction, riverine sediment conveyance, and environmental effect evaluation. Regression analysis has historically been used to connect silt content in rivers with water discharge while ignoring internal uncertainty. But this traditional method struggles to be applicable outside the constraints of the original data and lacks physical interpretability of parameters. Because of these drawbacks, scientists are increasingly using nonlinear models, such as Artificial Neural Networks (ANNs), to estimate sediment loads. ANNs are a more straightforward and economical option since they have been shown to be successful in simulating nonlinear system dynamics. With a focus on their potential to improve accuracy in rainfall-stream flow-suspended sediment connections, this paper analyses the literature on the use of ANNs in calculating suspended sediment loads. Rivers are significant components for sustainability through terms of quality of life, as well as environmental and socioeconomic progress, since they are formed by water streams that are subject to anthropogenic, microbial, and contaminant influences, which ultimately creates scope for study. Forecasting methods are useful for predicting the status of the river in the near future. The surface water, in the form of lakes and river discharge (runoff) is predominately obtained from rainfall after being generated by the rainfall-runoff process. In order to make decision for planning, design and control of water resource systems, long runoff series are required. The paper highlights the expanding relevance of artificial neural networks (ANNs) in delivering deep insights for civil and environmental engineering applications, underscoring the necessity of accurate sediment load estimation, checking water quality and Rainfall-Runoff prediction for watershed management, reservoir planning, and environmental impact assessments.