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.
Ahmadvand, M., Hoshmand, A. & Naseri, A. (2013). Performance Forecasting of Sugarcane Fields using Adaptive Neuro-Fuzzy
Inference System (ANFIS). Irrigation Sciences and Engineering, 35(4), 1–9.
MLA
Ahmadvand, M., Hoshmand, A., & Naseri, A. "Performance Forecasting of Sugarcane Fields using Adaptive Neuro-Fuzzy
Inference System (ANFIS)", Irrigation Sciences and Engineering, 35, 4, 2013, 1–9.
HARVARD
Ahmadvand, M., Hoshmand, A., & Naseri, A. (2013). 'Performance Forecasting of Sugarcane Fields using Adaptive Neuro-Fuzzy
Inference System (ANFIS)', Irrigation Sciences and Engineering, 35(4), pp. 1–9.
CHICAGO
Ahmadvand, M., Hoshmand, A. & Naseri, A., "Performance Forecasting of Sugarcane Fields using Adaptive Neuro-Fuzzy
Inference System (ANFIS)." Irrigation Sciences and Engineering, 35 4 (2013): 1–9,
VANCOUVER
Ahmadvand M., Hoshmand A., Naseri A. Performance Forecasting of Sugarcane Fields using Adaptive Neuro-Fuzzy
Inference System (ANFIS). JISE. 2013;35(4):1–9 (In Persian).
