As climate change and urban expansion accelerate, effective adaptation is urgently required to counteract the increasing trend of flood damage. Amidst increasing calls for accelerated climate adaptation, a pivotal question remains: What are the status, effectiveness, and potential of adaptation efforts to reduce future flood risks? 

FloodChat aims to provide an answer to this question by developing an automated data processing framework for global assessment of flood adaptation based on Large Language Models (LLMs) and other machine learning techniques to process public flood adaptation documents and quantitatively assess the effects of adaptation measures on risk dynamics at the global scale. Recent developments in LLMs have revolutionized text processing and have demonstrated its unprecedented potential. LLMs can efficiently analyse and synthesise vast collections of documents, providing interpretable and concise results that support informed knowledge generation. 

The treatment effect of various adaptation measures on flood damage will be quantified. Damage in situations with and without an implemented measure are compared while controlling for confounding variables (i.e., hazard, exposure, vulnerability) that can also significantly impact flood damage. Our planned assessment of adaptation reports and the effectiveness of adaptation measures complements and enhances the efforts of the Global Adaptation Mapping Initiative in line with the priorities set for global adaptation research.

FloodChat is a PhD project in the framework of the HEIBRIDS - Helmholtz Einstein International Berlin Research School in Data Science and is co-supervised by PD Dr. Heidi Kreibich, Section Hydrology, GFZ and Prof. Andrea Cominola, Chair of Smart Water Networks, Technische Universität Berlin and Einstein Center Digital Future.