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, raingauge 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 gave the best performance.

KEYWORDS:

• typhoon
• inundation forecasting
• input feature
• spatial characteristics
• machine learning
• multi-objective genetic algorithm

Editor A. Castellarin Associate Editor O. Kisi

Editor A. Castellarin Associate Editor O. Kisi

Acknowledgements

We thank the editor and anonymous reviewers for their comments and suggestions to improve the quality of this manuscript. We extend special thanks to Professor Yasuto Tachikawa from the Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University, who discussed the process of model development and provided positive comments on this study. We also thank the personnel and students at the Hydrology and Water Resources Research Laboratory, Kyoto University.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/02626667.2022.2092406

Additional information

Funding

The authors reported there is no funding associated with the work featured in this article.