Performance Forecasting of Sugarcane Fields using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Document Type : Research Paper

Authors

1 M.Sc Graduated Student,Department of Irrigation & Drainage. Shahid Chamran,University, Ahvaz,Iran

2 Assistant Professor, Department of Irrigation & Drainage. Shahid Chamran,University, Ahvaz,Iran

3 Professor, Department of Irrigation & Drainage. Shahid Chamran,University, Ahvaz,Iran

Abstract

     Sugarcane fields are affected by different parameters and factors such as ground water table, salinity of saturated soil, depth of irrigation, variety and age of plants and etc. Evaluating effects of  these parameters, it is possible to propose solutions to maximize sugarcane fields performance.In this paper Adaptive Neuro - Fuzzy Inference System (ANFIS) is used to model the performance of sugarcane fields. This study is performed based on three years data of "Mirza koochak khan cultivation and industry". Results showed that the proposed model has a correlation factor of 0.978, RMSE of 1.35 and error of 3.2 The proposed model has a very high accuracy in performance forecasting of sugarcane fields.
 
 

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History

  • Receive Date: 06 October 2010
  • Revise Date: 30 September 2014
  • Accept Date: 16 February 2013
  • Publish Date: 19 February 2013

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