Evaluating Machine Learning Approach for Regional Flood Frequency Analysis in Data-sparse Regions

Abstract

Accurate flood frequency analysis is essential for developing effective flood management strategies and designing flood protection infrastructure, but it is challenging due to the complex, nonlinear hydrological system. In regional flood frequency analysis (RFFA), the flood quantiles at ungauged sites can be estimated by establishing a relationship between interdependent physio-meteorological variables and observed flood quantiles at gauge sites in the region. However, this regional approach implies a loss of information due to the prior aggregation of hydrological data at gauged locations and can be difficult for data-sparse regions due to limited data. In this study, we evaluated an alternate approach or path for RFFA in two case studies: a data-sparse region in India and a data-dense region in the USA. In this approach, daily streamflow is predicted first using a deep learning-based hydrological model, and then flood quantiles are estimated from the predicted daily streamflow using statistical methods. We compared the results obtained using this alternate approach to those from the traditional RFFA technique, which used the Random Forest (RF) and eXtreme Gradient Boosting (XGB) algorithms to model the nonlinear relationship between flood quantiles and relevant physio-meteorological predictor variables such as meteorological forcings, topography, land use, and soil properties. The results showed that the alternate approach produces more reliable results with the least mean absolute error and higher coefficient of determination in the data-sparse region. In the data-dense region, both traditional and alternate approaches produced comparable results. However, the alternate approach has the advantage of being flexible and providing the complete time series of daily flow at the ungauged location, which can be used to estimate other flow characteristics, develop flow duration curves, or estimate flood quantiles of any return period without creating a separate traditional RFFA model. This study shows that the alternate approach can provide accurate flood frequency estimates in data-sparse regions, offering a promising solution for flood management in these areas.