Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.

This study makes a unique contribution by evaluating the effectiveness of the Switch Prediction Method (SPM) in integrating ensemble rainfall forecasts, significantly improving the accuracy of long-term inflow forecasting for reservoirs—a crucial aspect of hydrological forecasting. The proposed approach combines Numerical Weather Prediction (NWP) data with advanced machine learning techniques, specifically Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models, to develop a robust forecasting framework. The study utilizes comprehensive datasets from the ShihMen Reservoir in Taiwan to assess the performance of the proposed model. The SPM dynamically integrates multiple meteorological forecasts to reduce uncertainty and improve rainfall input accuracy. These enhanced inputs are then used in multi-step forecasting (MSF) models with a 72-hour lead time. The results demonstrate that the LSTM-based model, combined with SPM-integrated forecasts, delivers accurate and stable inflow predictions. For instance, in the case of Typhoon Soudelor—the test event with the highest observed peak inflow (5,634.1 cms)—the proposed SPM-LSTM-MSF model achieved a Mean Absolute Error (MAE) of 178.8 cms and a Coefficient of Efficiency (CE) of 0.87, demonstrating superior accuracy and temporal stability compared to the SVM-based approach. These findings highlight the potential of SPM and machine learning techniques in enhancing reservoir management and flood control strategies, offering a robust and adaptable solution for complex hydrological forecasting tasks.

This study makes a unique contribution by evaluating the effectiveness of the Switch Prediction Method (SPM) in integrating ensemble rainfall forecasts, significantly improving the accuracy of long-term inflow forecasting for reservoirs—a crucial aspect of hydrological forecasting. The proposed approach combines Numerical Weather Prediction (NWP) data with advanced machine learning techniques, specifically Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models, to develop a robust forecasting framework. The study utilizes comprehensive datasets from the ShihMen Reservoir in Taiwan to assess the performance of the proposed model. The SPM dynamically integrates multiple meteorological forecasts to reduce uncertainty and improve rainfall input accuracy. These enhanced inputs are then used in multi-step forecasting (MSF) models with a 72-hour lead time. The results demonstrate that the LSTM-based model, combined with SPM-integrated forecasts, delivers accurate and stable inflow predictions. For instance, in the case of Typhoon Soudelor—the test event with the highest observed peak inflow (5,634.1 cms)—the proposed SPM-LSTM-MSF model achieved a Mean Absolute Error (MAE) of 178.8 cms and a Coefficient of Efficiency (CE) of 0.87, demonstrating superior accuracy and temporal stability compared to the SVM-based approach. These findings highlight the potential of SPM and machine learning techniques in enhancing reservoir management and flood control strategies, offering a robust and adaptable solution for complex hydrological forecasting tasks.

This study makes a unique contribution by evaluating the effectiveness of the Switch Prediction Method (SPM) in integrating ensemble rainfall forecasts, significantly improving the accuracy of long-term inflow forecasting for reservoirs—a crucial aspect of hydrological forecasting. The proposed approach combines Numerical Weather Prediction (NWP) data with advanced machine learning techniques, specifically Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models, to develop a robust forecasting framework. The study utilizes comprehensive datasets from the ShihMen Reservoir in Taiwan to assess the performance of the proposed model. The SPM dynamically integrates multiple meteorological forecasts to reduce uncertainty and improve rainfall input accuracy. These enhanced inputs are then used in multi-step forecasting (MSF) models with a 72-hour lead time. The results demonstrate that the LSTM-based model, combined with SPM-integrated forecasts, delivers accurate and stable inflow predictions. For instance, in the case of Typhoon Soudelor—the test event with the highest observed peak inflow (5,634.1 cms)—the proposed SPM-LSTM-MSF model achieved a Mean Absolute Error (MAE) of 178.8 cms and a Coefficient of Efficiency (CE) of 0.87, demonstrating superior accuracy and temporal stability compared to the SVM-based approach. These findings highlight the potential of SPM and machine learning techniques in enhancing reservoir management and flood control strategies, offering a robust and adaptable solution for complex hydrological forecasting tasks.

This study makes a unique contribution by evaluating the effectiveness of the Switch Prediction Method (SPM) in integrating ensemble rainfall forecasts, significantly improving the accuracy of long-term inflow forecasting for reservoirs—a crucial aspect of hydrological forecasting. The proposed approach combines Numerical Weather Prediction (NWP) data with advanced machine learning techniques, specifically Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models, to develop a robust forecasting framework. The study utilizes comprehensive datasets from the ShihMen Reservoir in Taiwan to assess the performance of the proposed model. The SPM dynamically integrates multiple meteorological forecasts to reduce uncertainty and improve rainfall input accuracy. These enhanced inputs are then used in multi-step forecasting (MSF) models with a 72-hour lead time. The results demonstrate that the LSTM-based model, combined with SPM-integrated forecasts, delivers accurate and stable inflow predictions. For instance, in the case of Typhoon Soudelor—the test event with the highest observed peak inflow (5,634.1 cms)—the proposed SPM-LSTM-MSF model achieved a Mean Absolute Error (MAE) of 178.8 cms and a Coefficient of Efficiency (CE) of 0.87, demonstrating superior accuracy and temporal stability compared to the SVM-based approach. These findings highlight the potential of SPM and machine learning techniques in enhancing reservoir management and flood control strategies, offering a robust and adaptable solution for complex hydrological forecasting tasks.

This study makes a unique contribution by evaluating the effectiveness of the Switch Prediction Method (SPM) in integrating ensemble rainfall forecasts, significantly improving the accuracy of long-term inflow forecasting for reservoirs—a crucial aspect of hydrological forecasting. The proposed approach combines Numerical Weather Prediction (NWP) data with advanced machine learning techniques, specifically Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models, to develop a robust forecasting framework. The study utilizes comprehensive datasets from the ShihMen Reservoir in Taiwan to assess the performance of the proposed model. The SPM dynamically integrates multiple meteorological forecasts to reduce uncertainty and improve rainfall input accuracy. These enhanced inputs are then used in multi-step forecasting (MSF) models with a 72-hour lead time. The results demonstrate that the LSTM-based model, combined with SPM-integrated forecasts, delivers accurate and stable inflow predictions. For instance, in the case of Typhoon Soudelor—the test event with the highest observed peak inflow (5,634.1 cms)—the proposed SPM-LSTM-MSF model achieved a Mean Absolute Error (MAE) of 178.8 cms and a Coefficient of Efficiency (CE) of 0.87, demonstrating superior accuracy and temporal stability compared to the SVM-based approach. These findings highlight the potential of SPM and machine learning techniques in enhancing reservoir management and flood control strategies, offering a robust and adaptable solution for complex hydrological forecasting tasks.