Advanced Hydrological Forecasting with Machine Learning

Increasing climate variability in real-time river discharge prediction with appropriate flood management is nowadays considered essential. Traditional hydrological models encounter problems with handling high-dimensional data, non-linear dependencies, and time-related variations associated with river flows, thus limiting their efficiency in controlling such unpredictable river flows. The detail an elaborate multi-stage modeling pipeline for enhancing predictive accuracy together with adaptive response to facilitate effective real-time discharge management. Feature extraction is based on mutual information (MI) analysis with autoencoder that captures and selects hydrological patterns with the greatest dimensionality reduction level. Finally, the optimized set of features is submitted for PCA and t-SNE with the objective of enhancing further the structure and visualization of data. These enhanced features are fed to an Extreme Gradient Boosting (XGBoost) model for initial predictions and an augmentation with Gaussian Processes (GP) to quantify uncertainty, ensuring improvements in reliability for discharge forecasts. Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN) capture the dependencies with time; together, they improve accuracy in short- as well as long-term flow predictions, which are essential in the handling of flood peaks. It is conclude the paper with a real-time reservoir management model in the form of Deep Deterministic Policy Gradient reinforcement learning, dynamically updating the water release policies according to the present and forecasted flow conditions. This model results in 30–60% feature reduction, > 0.85 R² prediction accuracy, 20% MAE reduction, and increases flood control efficiency by 15–20% with response times reduced by ~ 10% for peak events. It considerably enhances the forecasting of discharge, providing sound, scalable solutions to mitigate floods and manage water resources.