Long lead forecasting of spring peak runoff using Mamdani-type fuzzy logic systems at Hay River, NWT

Abstract

In northern riverside communities, breakup ice jam flooding is an annual threat to properties and human lives. In this study, the peak snowmelt runoff during breakup was assessed as an indicator of breakup flood severity. Due to the sparse network of hydrometeorolocial data in remote northern regions, a Mamdani-type fuzzy logic system (FLS) was developed and tested with the limited historical data. Three input variables were defined from the precipitation, air temperature and daily water level data. All of these variables are known ∼3 to 4 weeks before breakup enabling a long lead-time forecast. The process of system development is demonstrated by a case study of the Town of Hay River, NWT Canada. A series of experiments were designed to select the best system configuration, which also provided a way to conduct a sensitivity analysis for different choices in each system component. It was found that the system shows very good performance on the historical data using the qualitative error index. The results of the sensitivity analysis suggest the system performance is dependent on the choices of fuzzy logic inference operators and defuzzification method. As a long lead system, the short-term meteorological factors that would affect the system output were analyzed and the possible error range was assessed. Preliminary model validation, based on three years of testing, shows promising performance.