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

• Clustering and multi criteria decision methods are used for rain gauge network design.
• Methodology is suggested for efficient/alternate rain gauge network for flood forecasting.
• Methodology is useful for deciding the key rain gauge station for flood forecasting.
• Methodology is also useful for floods forecasting during failure of some rain gauges.

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