Typhoon-Induced Hourly Inundation Forecasting by Integrating Long Short-Term Memory and Multi-Objective Genetic Algorithms

Typhoon-induced inundation is a critical issue in Taiwan, particularly under the intensifying impacts of extreme climate events. This study focuses on developing an AI-based hourly inundation forecasting model for real-time applications. Observational data, including rainfall, inundation depth, and sewer water levels from different typhoon events, were utilized as input factors. A traditional input factor selection method was employed to identify input variables for Support Vector Machine (SVM)-based models. Nine inundation reference points were selected, and an SVM-based forecasting model was developed for each point. To enhance forecasting accuracy and address potential overfitting issues, a novel model integrating Long Short-Term Memory (LSTM) networks with Multi-Objective Genetic Algorithm (MOGA), referred to as LSTM-MOGA, was proposed. This model automates the selection of influential input factors while optimizing forecasting performance. The study was conducted in Yilan County, Taiwan, and model validation was performed using cross-validation methods. The results indicate that, although SVM models with traditional input selection methods performed better in 3 out of 9 inundation reference points, the LSTM-MOGA model demonstrated superior forecasting accuracy in the remaining 6 points. Moreover, SVM models exhibited significant overfitting issues, with negative CE values during the testing phase, suggesting substantial underestimation in forecasting inundation depths during typhoon events. Conversely, the LSTM-MOGA model avoided overfitting, maintaining stable and reliable performance across both training and testing phases. The proposed LSTM-MOGA framework provides a robust solution for real-time inundation forecasting during typhoon events, with potential applications for disaster management and water resource planning. The outcomes of this study are expected to serve as valuable references for hydrological disaster mitigation and decision-making by water resource management agencies.