Prediction of Sediment in The Mahanadi River Basin Using Machine Learning

Abstract:-This paper integration of Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) for predicting sediment load in river basins, such as the Mahanadi River Basin, presents a powerful hybrid modeling approach. ANNs are highly effective in capturing the complex and nonlinear relationships characteristic of sediment transport processes in river systems. However, determining the optimal architecture and parameters for ANNs can be a challenging task, which directly impacts the model's accuracy and reliability. To address this, the use of GAs provides a robust solution by optimizing the hyperparameters of the ANN, such as network architecture, learning rate, and the number of hidden layers and neurons. By simulating the process of natural selection, GAs explore the parameter space to find the best-performing configurations, thereby enhancing the learning and generalization capabilities of the ANN. This hybrid ANN-GA model not only reduces the likelihood of overfitting but also improves prediction accuracy and model stability compared to traditional methods and standalone ANNs. The results from this study highlight the effectiveness of the ANN-GA combination, which significantly improves the accuracy of sediment load predictions in river basins. This method offers valuable insights for water resource management and environmental planning by enabling more precise forecasting of sediment loads, facilitating better management of river systems like the Mahanadi Basin. The ANN-GA hybrid model, therefore, represents a promising advancement in sediment prediction, contributing to improved decisionmaking in environmental and hydrological management

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