Assessing the effective spatial characteristics of input features through physics-informed machine learning models in inundation forecasting during typhoons

Abstract

This study aimed to assess the effective spatial characteristics of input features by using physics-informed, machine learning (ML)-based inundation forecasting models. To achieve this aim, inundation depth data were simulated using a numerical hydrodynamic model to obtain training and testing data for these ML-based models. Effective spatial information was identified using a back-propagation neural network, an adaptive neuro-fuzzy inference system, support vector machine, and a hybrid model combining support vector machine and a multi-objective genetic algorithm. The conventional average rainfall determined using the Thiessen polygon method, rain gauge observations, radar-based rainfall data, and typhoon characteristics were used as the inputs of the aforementioned ML models. These models were applied in inundation forecasting for Yilan County, Taiwan, and the hybrid model had the best forecasting performance. The results show that the hybrid model with crucial features and appropriate lag lengths has the best performance.