Analysis of the Utilization of Machine Learning to Map Flood Susceptibility

Abstract

This article provides an analysis of the utilization of Machine Learning (ML) models in Flood Susceptibility Mapping (FSM), based on selected publications from the past decade (2013–2023). Recognizing the challenge that some stages of ML modeling inherently rely on experience or trial-and-error approaches, this work aims at establishing a clear roadmap for the deployment of ML-based FSM frameworks. The critical aspects of ML-based FSM are identified, including data considerations, the model's development procedure, and employed algorithms. A comparative analysis of different ML models, alongside their practical applications, is made. Findings suggest that despite existing limitations, ML methods, when carefully designed and implemented, can be successfully utilized to determine areas at risk of flooding. We show that the effectiveness of ML-based FSM models is significantly influenced by data preprocessing, feature engineering, and the development of the model using the most impactful parameters, as well as the selection of the appropriate model type and configuration. Additionally, we introduce a structured roadmap for ML-based FSM, identification of overlooked conditioning factors, comparative model analysis, and integration of practical considerations, all aimed at enhancing modeling quality and effectiveness. This comprehensive analysis thereby serves as a critical resource for professionals in the field of FSM.