USING THE NEURAL NETWORK AND EXPONENTIAL REGRESSION TECHNIQUES IN THE PREDICTION OF THE EFFECTIVE RAINFALL

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TAHERI P.; AFZAL A.; TAHERI P.

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Paper Information

Journal: JOURNAL OF WATER ENGINEERING (JWE) Year:2010 | Volume:1 | Issue:1 Page(s): 75-83

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Title

USING THE NEURAL NETWORK AND EXPONENTIAL REGRESSION TECHNIQUES IN THE PREDICTION OF THE EFFECTIVE RAINFALL

Author(s)

TAHERI P. | AFZAL A. | TAHERI P. | Issue Writer Certificate

Keywords

NEURAL NETWORKSQ3

REGRESSIONQ3

EFFECTIVE RAINQ3

PLANT WATER REQUIREMENTQ3

Abstract

Given the present water crisis and since more than 94 percent of the country's water is spent in the agriculture sector, it seems logical not only to mechanize the irrigation systems, but also to re-estimate the plants' actual water requirements. This becomes possible by predicting the amount of snow and rainfall in the plants' growth seasons. In designing irrigation systems, it must be taken into account that the total amount of rainfall is not usable for the plant, and that a portion of this rain and is used as runoff and a part of it penetrates deep into the soil, leaving the plant with only a part of the rain water for its water needs. It is this portion of water that is effective in the plants' growth. In the present study, the best REGRESSION analysis model was obtained using field data including rainfall, evapotranspiration and EFFECTIVE RAIN, and the results were compared with the output of NEURAL NETWORKS. Results of the study showed that natural mathematical models (NEURAL NETWORKS) have a higher degree of accuracy compared to the pure mathematical models (regression models). As a result, use of the networks in predicting the EFFECTIVE RAIN not only decreases water consumption expenses, but also prevents the imposition of water stress on plants and the reduction of agricultural production.

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APA: Copy

TAHERI, P., AFZAL, A., & TAHERI, P.. (2010). USING THE NEURAL NETWORK AND EXPONENTIAL REGRESSION TECHNIQUES IN THE PREDICTION OF THE EFFECTIVE RAINFALL. JOURNAL OF WATER ENGINEERING (JWE), 1(1), 75-83. SID. https://sid.ir/paper/201935/en

Vancouver: Copy

TAHERI P., AFZAL A., TAHERI P.. USING THE NEURAL NETWORK AND EXPONENTIAL REGRESSION TECHNIQUES IN THE PREDICTION OF THE EFFECTIVE RAINFALL. JOURNAL OF WATER ENGINEERING (JWE)[Internet]. 2010;1(1):75-83. Available from: https://sid.ir/paper/201935/en

IEEE: Copy

P. TAHERI, A. AFZAL, and P. TAHERI, “USING THE NEURAL NETWORK AND EXPONENTIAL REGRESSION TECHNIQUES IN THE PREDICTION OF THE EFFECTIVE RAINFALL,” JOURNAL OF WATER ENGINEERING (JWE), vol. 1, no. 1, pp. 75–83, 2010, [Online]. Available: https://sid.ir/paper/201935/en

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