Regional flood frequency analysis using data-driven models (M5, random forest, and ANFIS) and a multivariate regression method in ungauged catchments

Flooding is recognized worldwide joined of the most expensive natural hazards. To adopt proper structural and nonstructural measurements for controlling and mitigating the rising flood risk, the availability of streamflow values along a river is essential. This raises concerns in the hydrological assessment of poorly gauged or ungauged catchments. In this regard, several flood frequency analysis approaches have been conducted in the literature including index flow method (IFM), square grids method (SGM), hybrid method (HM), as well as the conventional multivariate regression method (MRM). While these approaches are often based on assumptions that simplify the complex nature of the hydrological system, they might not be able to address uncertainties associated with the complexity of the system. One of the powerful tools to deal with this issue is data-driven model that can be easily adopted in complex systems. The objective of this research is to utilize three different data-driven models: random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and M5 decision tree algorithm to predict peak flow associated with various return periods in ungauged catchments. Results from each data-driven model were assessed and compared with the conventional multivariate regression method. Results revealed all the three data-driven models performed better than the multivariate regression method. Among them, the RF model not only demonstrated the superior performance of peak flow prediction compared to the other algorithms but also provided insight into the complexity of the system through delivering a mathematical formulation.