Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India

摘要: Abstract Study region Mahanadi Basin, India. focus Flood is one of the most common hydrologic extremes which are frequently experienced in basin, During flood times it becomes difficult to collect information from all rain gauges. Therefore, important find out key gauge (RG) networks capable forecasting with desired accuracy. In this paper a procedure for design network particularly discussed and demonstrated through case study. New hydrological insights This study establishes different possible RG using Hall’s method, analytical hierarchical process (AHP), self organization map (SOM) clustering (HC) characteristics each occupied Thiessen polygon area. Efficiency tested by artificial neural (ANN), Fuzzy NAM rainfall-runoff models. Furthermore, has been carried three effective uses only 7 RGs instead 14 gauges established Kantamal sub-catchment, basin. The logic applied on derived AHP shown best result efficiency 82.74% 1-day lead period. demonstrates when there difficulty gathering RGs.