Improvement of Urban Flood Alert Criteria Prediction Model based on Neuro-Fuzzy Initial Function and Training Data

Abstract

In Korean metropolitan areas, the high density of residential and commercial buildings, highly impervious surfaces, and steep slopes contribute to floods that can occur within a short duration of heavy rainfall. To prepare for this, advance warning measures based on accurate flood alert criteria are needed. In our previous study, we demonstrated the applications of a Neuro-Fuzzy model that considersthe characteristics of the basin to predict flood alert criteria in areas with no damage. However, as the number of learning materials are low, at 27, the evaluation and verification of the model has not been sufficiently accomplished, and its application is limited. Therefore, in this study, we propose an improved model based on the initializing function of the Neuro-Fuzzy algorithm, the construction of training data, and preprocessing. Compared to the existing model, the improved model reduced the average error by 48.1%~65.4% and the RMSE by 50.7%~60.1%. The new model, when applied to actual floods, showed an improvement of 0.7%~19.1% in accuracy.