Integrated real-time flood forecasting and inundation analysis in small-medium streams

Byunghyun Kim, Seng Yong Choi, Kun Yeun Han

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish
Article number919
JournalWater (Switzerland)
Volume11
Issue number5
DOIs
StatePublished - 1 May 2019

Keywords

  • Adaptive neuro-fuzzy inference system (ANFIS)
  • Flood forecasting
  • Inundation analysis
  • Real-time
  • Small-medium stream

Fingerprint

Dive into the research topics of 'Integrated real-time flood forecasting and inundation analysis in small-medium streams'. Together they form a unique fingerprint.

Cite this