Machine Learning-based Hydrological Models for Flash Floods: A Systematic Literature Review

Background & Purpose: Flash flood modeling faces many challenges since physically-based hydrological models are unsuitable for a small spatiotemporal scale. With the increased availability of hydrological observed data, an alternative approach is to use machine learning (ML) techniques. This work conducts a Systematic Literature Review (SLR) to enhance our comprehension of the research landscape on ML applications for modeling flash floods. Methods: Starting with more than 1,200 papers published until January 2024 and indexed in Web of Science, SCOPUS/Elsevier, Springer/Nature, or Wiley databases, it was selected 50 for detailed analysis, following the PRISMA guidelines. The inclusion/exclusion criteria removed reviews, retractions, and papers that were not in the scope of this SLR and included only papers that used data with a temporal resolution finer than 6 hours. From each selected paper, among other information, data were extracted regarding the forecasting horizon, the size of the study area, the different input data, the chosen machine learning (ML) technique, and the type of outcome (whether regression or classification) in order to characterize the model applied to flash flood forecasting. Results and Discussion: There has been a notable increase in publications investigating ML techniques for flash flood modeling over the last few years. Most of the studies are performed in China (38%). In 49 out of 50 of the selected papers used as input data, just one or an exclusive combination of the following measurements: discharge, rainfall, and waterlevel. From this set, the combination of discharge and rainfall appears in almost 40% of the papers. Notably, 60% of the studies utilize the long short-term memory (LSTM) method. No method consistently outperforms all others in the selected papers. Unfortunately, only 10% of the selected articles provide access to their data. To further explore the potential of ML approaches in flood forecasting, we recommend their integration into early warning systems, development and dissemination of benchmarks, publication of successful case studies, and multidisciplinary collaboration.