Accurate real-time forecasts of inundation depth and extent during typhoon flooding are crucial to disaster emergency response. To manage disaster risk, the development of a flood inundation forecasting model has been recognized as essential. In this paper, a forecasting model by integrating a hydrodynamic model, k-means clustering algorithm and support vector machines (SVM) is proposed. The task of this study is divided into four parts. First, the SOBEK model is used in simulating inundation hydrodynamics. Second, the k-means clustering algorithm classifies flood inundation data and identifies the dominant clusters of flood gauging stations. Third, SVM yields water level forecasts with 1–3 h lead time. Finally, a spatial expansion module produces flood inundation maps, based on forecasted information from flood gauging stations and consideration of flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we present an application to the Yilan River basin, Taiwan. The forecasting results indicate that the simulated water level forecasts from the point forecasting module are in good agreement with the observed data, and the proposed model yields the accurate flood inundation maps for 1–3 h lead time. These results indicate that the proposed model accurately forecasts not only flood inundation depth but also inundation extent. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent neural network, long short-term memory, and gated recurrent unit—were applied for prediction. Among them, the prediction of random forest resulted in an R² of 88% ahead of the first month and 81% ahead of the third month. These experimental results demonstrate that the prediction model is reliable and significantly accurate. Building owners could further enrich the model for energy conservation and management.

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.

Accurate real-time forecasts of inundation depth and area during typhoon flooding is crucial to disaster emergency response. The development of an inundation forecasting model has been recognized as essential to manage disaster risk. In the past, most researchers used multiple single-point forecasts to obtain surface flooding depth forecasts with spatial interpolation. In this study, a forecasting model (QPF-RIF) integrating a hydrodynamic model (SOBEK), support vector machine–multi-step forecast (SVM-MSF), and a self-organizing map (SOM) were proposed. The task of this model was divided into four parts: hydrodynamic simulation, point forecasting, inundation database clustering, and spatial expansion. First, the SOBEK model was used in simulating inundation hydrodynamics to construct the flooding maps database. Second, the SVM-MSF yields water level (inundation volume) forecasted with a 1 to 72 h lead time. Third, the SOM clustered the previous flooding maps database into several groups representing different flooding characteristics. Finally, a spatial expansion module produced inundation maps based on forecasting information from forecasting flood volume and flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we presented an application to the Yilan River basin in Taiwan. Our forecasting results indicated that the proposed model yields accurate flood inundation maps (less than 1 cm error) for a 1 h lead time. For long-term forecasting (46 h to 72 h ahead), the model controlled the error of the forecast results within 7 cm. In the testing events, the model forecasted an average of 83% of the flooding area in the long term. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

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.