ISSN : 0975-3710
EISSN : 0975-9107
P.M. KYADA1*, PRAVENDRA KUMAR2, M.A. SOJITRA3
1Subject Matter Specialist (Agri. Engg.), Krishi Vigyan Kendra, Lokbharti Gramvidhyapith Trust, Sanosara, Bhavnagar, 364230, Gujarat
2Department of Soil & Water Cons. Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, 263145, Uttarakhand, India
3ICAR-Krishi Vigyan Kendra, Targhadia, Rajkot, 360430, Junagadh Agriculture University, Junagadh, 362001, Gujarat, India
* Corresponding Author : pradip.caet.jau@gmail.com
Received : 09-05-2018 Accepted : 27-05-2018 Published : 30-05-2018
Volume : 10 Issue : 10 Pages : 6153 - 6159
Int J Agr Sci 10.10 (2018):6153-6159
Keywords : Artificial Neural Networks (ANN), Adaptive neuro-fuzzy inference system (ANFIS), Rainfall forecasting, Sensitivity Analysis
Conflict of Interest : None declared
Acknowledgements/Funding : Author thankful to G. B. Pant University of Agriculture and Technology, Pantnagar, 263145, Uttarakhand, India
Author Contribution : All author equally contributed
Accurate rainfall prediction is of great interest for water management in rainfed areas. The occurrence of rainfall as a physical process are uncertain, non-linier and highly complex. The present study investigates the ability of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models for rainfall forecasting of Junagadh region of Gujarat, India. Based on the past observations i.e., vapour pressure, mean temperature, wind velocity and rainfall. ANN model (4-6-4-1) is the best for prediction of rainfall among all the models. ANN models showed better performance than the ANFIS models in rainfall forecasting. The sensitivity analysis revealed that vapour pressure is the most sensitive parameter in rainfall prediction.
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