RANKING OF CMIP5-BASED GENERAL CIRCULATION MODELS USING COMPROMISE PROGRAMMING AND TOPSIS FOR PRECIPITATION: A CASE STUDY OF UPPER GODAVARI BASIN, INDIA B. DEEPTHI * and AKSHAY SUNIL † Department of Civil Engineering, IIT Bombay, Maharashtra, India * deepthibhadran2@gmail.com † akshaysunil172@gmail.com SARANYA C. NAIR ‡ and A. B. MIRAJKAR § Department of Civil Engineering Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India ‡ saranyacnair007@gmail.com § ashmirajkar@gmail.com S. ADARSH Department of Civil Engineering TKM College of Engineering, Kollam, Kerala, India adarsh_lce@yahoo.co.in Received 30 August 2020 Revised 6 October 2020 Accepted 19 November 2020 Published 14 December 2020 This study determines the suitable general circulation models (GCMs) for the prediction of future precipitation of Upper Godavari sub-basin, India. Five performance indicators (PIs) namely correlation coefficient (CC), normalized root mean square deviation (NRMSD), abso- lute normalized mean biased deviation (ANMBD), skill score (SS), Nash Sutcliffe efficiency (NSE), and three different combinations (Case 1: all performance indicators, Case 2: CC, SS and ANMBD, and Case 3: CC, SS, and NRMSD) were considered to evaluate the performance of 38 GCM models for the study area. The observed precipitation data for 12 grid points covering the Upper Godavari sub-basin along with eight districts of Maharashtra were used for the selection of the suitable GCMs. The weights of the indicators were determined by the entropy method. Compromise programming (CP) and the technique for order preference to the similarity to ideal solution (TOPSIS) methods were used for ranking the GCMs. The group decision-making approach was employed to make a collective decision about the rank of 38 GCMS considering all the grid points. In view of all the three combinations of PIs, the study suggests that the effect of the performance indicator NSE on the ranking of GCM models is the most significant (weights for the grid points varying in the range 22.75%–78%) followed by ANMBD, CC, NRMSD, and SS. Including the maximum number of PIs and considering their * Corresponding author. International Journal of Big Data Mining for Global Warming Vol. 2, No. 2 (2020) 2050007 (25 pages) © World Scientific Publishing Company DOI: 10.1142/S2630534820500072 2050007-1 Int. J. Big Data Mini. Glob. Warm. 2020.02. Downloaded from www.worldscientific.com by UNIVERSITY OF QUEENSLAND on 07/06/22. Re-use and distribution is strictly not permitted, except for Open Access articles.