Cascaded-ANFIS to simulate nonlinear rainfall–runoff relationship

Hydrologic models require atmospheric, dynamic and static models to simulate river flow from catchments. Thus the accuracy of hydrologic modelling highly depends on the data quality. Therefore, simulation is always challenging in data-scarcity environments. In addition, physical flow measurements are infeasible in the Spatiotemporal domain, and soft computing techniques are helpful in river flow simulation in data-scarcity environments. In this research paper, an efficient and accurate Cascaded-ANFIS-based model for rainfall–runoff was proposed and evaluated using five case studies in three countries: Japan, Vietnam, and Sri Lanka. The investigation focused on predicting streamflow by the influence of past data, with each river's dataset examined to determine the best configuration of past rainfalls affecting streamflow volume. The proposed algorithm was compared against six state-of-the-art regression algorithms. The results showed that it outperformed the other algorithms in every case study except the Kalu River dataset, with zero bias calculated. The developed R-R model can be considered a generic model for streamflow prediction in data-scarcity environments, with excellent acceptability of simulated river flows against measured river flows observed across different geographic and climatic regions.