A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling

Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the world. In this paper, we reviewed recently published literature on ML and DL applications for flood modeling for various hydrologic and catchment characteristics. Our extensive literature review shows that DL models produce better accuracy compared to traditional approaches. Unlike physically based models, ML/DL models suffer from the lack of using expert knowledge in modeling flood events. Apart from challenges in implementing a uniform modeling approach across river basins, the lack of benchmark data to evaluate model performance is a limiting factor for developing efficient ML/DL models for flood inundation modeling.

Automated summary

Machine learning (ML) and deep learning (DL) methods have gained popularity in flood inundation modeling across river basins. DL models have shown better accuracy compared to traditional ML approaches. However, ML/DL models lack the incorporation of expert knowledge in modeling flood events and the lack of benchmark data to evaluate model performance poses a challenge in developing efficient models for flood inundation modeling.