EVENT-BASED RAINFALL-RUNOFF MODELING USING ADAPTIVE NETWORK-BASED FUZZY INFERENCE SYSTEM

Population growth and transformation of agricultural or forest landscapes to built-up areas are the common phenomenon in the fast developing countries. Such changes have significant impact on hydrologic processes in the catchment which in turn may end up with an increase in both magnitude and frequency of floods in urban areas. Therefore, reliable rainfall-runoff models that are able to estimate discharge of a catchment accurately are in need. To date, several physically-based models are developed to capture the rainfall-runoff process; however, they require significant number of parameters which could be difficult to be measured or estimated. Beside these models, the artificial intelligence techniques have shown their ability to identify a direct mapping between inputs and outputs with less number of physical parameters. Adaptive network-based fuzzy inference system (ANFIS) is one of the well-practiced techniques in hydrological time series modeling. The aim of this study was to check the capability of ANFIS in event-based rainfall runoff modeling for a tropical catchment. A total of 70 rainfall-runoff events were extracted from twelve years hourly rainfall and runoff data of Semenyih River catchment where 50 of them were chosen for training and the remaining 20 for testing. An input selection method based on correlation analysis and mutual information was developed to identify the proper input combinations of rainfall and discharge antecedents. The results obtained by ANFIS model were then compared with an autoregressive model with exogenous inputs (ARX) as a bench mark. Results showed that ANFIS outperforms ARX model and has capabilities to be used as a reliable rainfall-runoff modeling tool.