_____________________________________________________________________________________________________ *Corresponding author: E-mail: megha.aroligouda393@gmail.com; International Journal of Environment and Climate Change 12(11): 3725-3735, 2022; Article no.IJECC.92662 ISSN: 2581-8627 (Past name: British Journal of Environment & Climate Change, Past ISSN: 2231–4784) Pan Evaporation Estimation Using Artificial Neural Network (ANN) and Fuzzy Logic Models for Raichur Region, Karnataka: A Case Study Megha a* , G. V. Srinivasa Reddy b , Premanand B. Dashavant c , B. Maheshwara Babu a and G. Manoj Kumar d,e a Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur, India. b Department of Irrigation and Drainage Engineering, CAE, Raichur, India. c College of Agricultural Engineering, GKVK, UAS, Bangalore, India. d Department of Agricultural Economics, College of Agriculture, Raichur, India. e University of Agricultural Sciences, Raichur - 584104, Karnataka, India. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/IJECC/2022/v12i111423 Open Peer Review History: This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://www.sdiarticle5.com/review-history/92662 Received 10 August 2022 Accepted 12 October 2022 Published 07 November 2022 ABSTRACT Aims: Accurate estimates of evaporation by employing efficient and proven soft computing techniques that involve least number of influencing variables are important to tackle present water crisis. Place and Duration of Study: In the present study, Artificial Neural Network (ANN) and fuzzy logic models were developed to predict the pan evaporation (Ep) in Raichur, Karnataka, using six input parameters viz., maximum and minimum temperatures, maximum and minimum relative humidity, sunshine hours and wind speedfor the period of 30 years (1990-2019). Methodology: Comparison between models was done to select best suitable model to predict pan evaporation. The ANN models were trained withthree training algorithms. Gaussian membership function was used in fuzzy logic (FL) model. Results: The results revealed that, the ANN-GDX model performed better over ANN-LM, ANN-BR and fuzzy logic models during validation period. The correlation coefficient (r), coefficient of efficiency (CE), mean absolute error (MAE) and root mean square error (RMSE) were observed to be 0.7637, 0.5831, 1.3880 and 1.8541 respectively during validation period between actual and Original Research Article