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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/9219
Title: Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India
Authors: Kar A.K.
Lohani A.K.
Goel, Narendra Kumar
Roy G.P.
Published in: Journal of Hydrology: Regional Studies
Abstract: Study region: Mahanadi Basin, India. Study focus: Flood 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 region: This 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. © 2015.
Citation: Journal of Hydrology: Regional Studies (2015), 4(): 313-332
URI: https://doi.org/10.1016/j.ejrh.2015.07.003
http://repository.iitr.ac.in/handle/123456789/9219
Issue Date: 2015
Publisher: Elsevier
Keywords: Analytical hierarchical process
ANN
Flood forecasting
Fuzzy logic
Hall's method
Key rain gauge network
NAM model
Prior network
ISSN: 22145818
Author Scopus IDs: 57197671444
6602080269
36190650800
36953176500
Author Affiliations: Kar, A.K., Department of Water Resources, Government of Orissa, Bhubaneswar, India
Lohani, A.K., National Institute of Hydrology, Roorkee, 247667, India
Goel, N.K., Department of Hydrology, Indian Institute of Technology, Roorkee, India
Roy, G.P., Department of Water Resources, Government of Orissa, Bhubaneswar, India
Corresponding Author: Lohani, A.K.; National Institute of HydrologyIndia
Appears in Collections:Journal Publications [HY]

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