• Volume: 1
• Issue:03
• Date: 26-03-2024

Title : Review of Prediction of Sediment In The Mahanadi River Basin Using Machine Learning

Abstract: - This review paper provides a comprehensive analysis of the prediction of sediment transport in the Mahanadi River Basin using machine learning techniques. The increasing sediment load in river basins poses significant challenges to water resource management, flood control, and dam operations. Traditional methods of sediment prediction, such as empirical models, lack the ability to capture the complex, nonlinear relationships between hydrological variables and sediment transport. Machine learning models, including artificial neural networks (ANN), support vector machines (SVM), and decision trees, offer powerful tools to overcome these limitations. This review explores various studies that have employed machine learning algorithms to predict sediment yield, highlighting their accuracy, performance, and adaptability to large datasets. By evaluating the strengths and limitations of these models, the paper aims to identify future research directions and practical applications of machine learning in river sediment prediction. This study is particularly relevant for hydrologists, environmental scientists, and water resource managers seeking innovative solutions for managing sedimentation in river basins like the Mahanadi.

Key Words: Sediment Prediction, Mahanadi River Basin, Machine Learning, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees, Sediment Transport, Hydrological Modelling

Area: Engineering

• Approved ISSN: ----
• Paper Id: IJREISTU10
• Page No: 12-16

• Author: Arti Kumari

---

• Co- Author: