Characteristics of adaptive network-based fuzzy inference system for typhoon inundation level forecast

Abstract

Heavy rainfall brought in by a typhoon often causes severe inundation in a low-lying area. Due to budget constraints, inundation level monitoring programs often cease to continue after the project ends. In such cases, forecast models capable of predicting inundation levels solely based on rainfall data to provide supportive information for responding actions during typhoons are urged. This paper aims to explore two types of typhoon inundation level forecast models based on adaptive network-based fuzzy inference system (ANFIS): one employing only rainfall data as inputs (ANFIS-R) to cope with the situation where water level observation is lacking, and the other one using both rainfall and water level data as inputs (ANFIS-B). A methodology is proposed to identify the appropriate time interval of rainfall accumulation to be used as model inputs. The forecast capacities of the models are assessed in three aspects: prediction accuracy, peak level error, and time shift error. The proposed ANFIS models are compared with traditional ARX-based models. The results show that ANFIS-B models outperform ARX-based models on all three aspects. ANFIS-R models display comparable prediction accuracy and superior performance on peak level forecast and time shift error. This renders ANFIS-R models promising in areas lacking water level observations.