Performance Forecasting of Sugarcane Fields using Adaptive Neuro-Fuzzy Inference System (ANFIS)
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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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Shahid Chamran University of Ahvaz
2013-02-01
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| Series: | علوم و مهندسی آبیاری |
| Subjects: | |
| Online Access: | http://jise.scu.ac.ir/article_10793_c4b2862e712286fe204e0f3bf356881a.pdf |
| Summary: | 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. <br /> <br /> |
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| ISSN: | 2588-5952 2588-5960 |