Abstract
This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small–medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed rainfall and water level without extensive physical and topographic data, and can be performed in real-time by integrating point- and section flood forecasting and spatial inundation analysis.
Highlights
Since the frequency and scale of damage from torrential rain have increased due to climate change and global warming, flood forecasting has become critical for small–medium streams as well as large rivers
This study showed how to secure evacuation time by linking the point flood forecast of the neuro-fuzzy model based on MATLAB, section flood forecast of 1-D river model (FLDWAV), and spatial inundation analysis of 2-D finite volume model (FVM) to enable real-time flood forecast and inundation analysis in small–medium streams
The parameters of the membership functions (MFs) were determined in the processes of training and checking
Summary
Since the frequency and scale of damage from torrential rain have increased due to climate change and global warming, flood forecasting has become critical for small–medium streams as well as large rivers. In modeling the relations with rainfall and runoff by using a hydrological model, numerous nonlinear and uncertain elements are implied Such models show a lack of practicality and are difficult to use for real-time flood forecasting for small–medium streams in Korea, where watersheds are characterized by high mountains, steep. The data-driven model is usually developed and implemented quickly and This approach is very useful for real-time flood forecasting for the purpose of obtaining accurate predictions of river flow and water level at specific locations in a timely manner [6]. The purpose of this study is to construct a real-time flood forecasting and flood analysis system by applying ANFIS, 1-D, and 2-D hydrodynamic models to resolve the problems of hydrological rainfall runoff model currently used for flood forecasting in South Korea. This study showed how to secure evacuation time by linking the point flood forecast of the neuro-fuzzy model based on MATLAB, section flood forecast of 1-D river model (FLDWAV), and spatial inundation analysis of 2-D finite volume model (FVM) to enable real-time flood forecast and inundation analysis in small–medium streams
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