Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models

The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use a variety widely used algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Descent (SGD), Extreme Model (XGBoost), to predict river inflow Garudeshwar watershed, key element planning flood control supply. substantial engineering feature study, which incorporates temporal lag contextual data based Indian seasons, leads it distinctiveness. concludes that CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Square (RMSE), R-squared (R2) values, both training testing datasets. was accomplished by an in-depth investigation model comparison. In contrast XGBoost LGBM higher percentage points with prediction errors exceeding 35% moderate numbers above 10,000. established itself reliable time-series modelling, easily managing categorical continuous variables, thereby greatly enhancing accuracy. results this highlight value promise algorithms hydrology offer valuable insights academics industry professionals.