Advances in Industrial Engineering

Advances in Industrial Engineering

Evapotranspiration monthly using fuzzy neural network model and multiple regression model and comparing the results with real data of FAO Penman Monteith

Document Type : Research Paper

Authors
Nohammad Ehsanifar 1
Reza Ehtesham Rasi 2
1 گروه مهندسی صنایع، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران
2 گروه مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین
10.22059/jieng.2019.207333.1125
Abstract
In recent decades, due to the importance of the water issue and increased tendency to calculate the amount of runoff from precipitation, development and implementation of appropriate methods for predicting the precipitation of the data has become essential issue. Knowledge of evapotranspiration and crop water, forms the basis for a proper planning of irrigation-up. Without knowing the amount of water that the plants are or less of the water requirements of plants and reduce yields and cause other problems in agriculture or more of the required amount of plant and waste water and drainage issues such as drain.One of the most important ways to improve the management of water use, especially in agriculture, the major share of water consumption in the country. Precise estimates of water use, which directly depends on the "ET " in plants. The evapotranspiration due to the application of proper management is inevitable water resources. There are many ways to predict evapotranspiration of the reference can be cited methods FAO Penman-Monteith method. Several research is in this field within the country that most of these predictions have been based on empirical methods and new methods have been used less. This research aims to predict the results of the two methods using artificial neural networks and regression trees to predict paid evapotranspiration and as well as to assess the effectiveness of its common Forecast.
Keywords
Prediction evapotranspiration
Fuzzy Network
Regression tree
Water management
Subjects
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Advances in Industrial Engineering
Volume 53, Issue 1
Winter 2019
Pages 483-494