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Reference evapotranspiration (ETo) is a major component of the hydrological cycle linking the irrigation water requirement and planning and management of water resources. In this research, the potential of co-active neuro-fuzzy inference system (CANFIS) was investigated against the multilayer perceptron neural network (MLPNN), radial basis neural network (RBNN), self-organizing map neural network (SOMNN) and multiple linear regression (MLR) to estimate the monthly ETo at Pantnagar and Ranichauri stations, located in the foothills of Indian central Himalayas of Uttarakhand State, India. The significant combination of input variables for implemented techniques was decided by the Gamma test (GT). The results obtained by CANFIS models were compared with MLPNN, RBNN, SOMNN and MLR models based on performance evaluation indicators and visual inspection using line, scatter and Taylor plots for both the stations. The results of comparison revealed that CANFIS-5/ CANFIS-9 models (RMSE1⁄4 0.0978/0.1394, SI1⁄4 0.0261/0.0475, COE1⁄4 0.9963/0.9846, PCC1⁄4 0.9982/ 0.9942 and WI1⁄4 0.9991/0.9959) with three and five input variables provide superior results for estimating monthly ETo at Pantnagar and Ranichauri stations, respectively. Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ETo at the study stations. doi: 10.2166/nh.2019.059 s://iwaponline.com/hr/article-pdf/doi/10.2166/nh.2019.059/610356/nh2019059.pdf Anurag Malik (corresponding author) Anil Kumar Department of Soil and Water Conservation Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, 263145 Uttarakhand, India E-mail: anuragmalik_swce2014@rediffmail.com Mohammad Ali Ghorbani Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran Mahsa H. Kashani Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Ozgur Kisi School of Technology, Ilia State University, Tbilisi, Georgia Sungwon Kim Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Republic of Korea

The viability of co-active fuzzy inference system model for monthly reference evapotranspiration estimation: case study of Uttarakhand State