Flood calculation trials using a generative AI, coupled with physical information

Abstract

We attempted to use a generative AI that may predict natural disasters. Focusing on river flooding and inland water inundation caused by recent climate change, we used an existing large-scale language model (LLM) to create a system that can output images and texts of flood waveforms, inundation conditions, etc. by querying the generation AI with pictures or text (hereinafter, referred to as prompt input). We verified whether better flood calculations could be made by linking a tank model capable of flood calculations based on physical laws to the LLM and by having LLM refer to the precipitation and runoff volumes related to floods as additional information. We confirmed that an internal parameter of the tank model can be appropriately adjusted based on flood information from prompt input and further confirmed that continuous interaction with the LLM can suggest better estimates of the internal parameter.