An Overview of Machine Learning Techniques for Sediment Prediction

Most hydrological and water resources researchers prioritise the development of an accurate sediment prediction model. Several conventional techniques have failed to accurately predict suspended sediment. Because of the complexity, non-stationarity, and non-linearity of sediment transport behavior in rivers, many techniques fall short. Over the last few decades, there have been significant developments in the theoretical understanding of machine learning approaches, as well as algorithmic strategies for their implementation and applications of the approach to practical and hydrological problems. To produce the desired output, machine learning models and other algorithms have been employed to predict complicated non-linear connections and patterns of huge input parameters. This paper examines a number of key works of literature on sediment transport prediction while focusing on a variety of machine learning applications. Sediment transport models aided by machine learning have attracted a growing number of researchers in recent years. As a result, they must gain in-depth knowledge of their theory and modeling methodologies. Furthermore, this chapter includes an overview of the machine learning technique and other developing hybrid models that have produced promising outcomes. This overview also includes various examples of successful machine learning applications in sediment prediction.