COMPARISON OF ARTIFICIAL INTELLIGENCE SYSTEMS (ANN & ANFIS) FOR REFERENCE EVAPOTRANSPIRATION ESTIMATION IN THE EXTREME ARID REGIONS OF IRAN

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AHMADZADEH GHARAH GOUIZ K.; MIRLATIFI S.M.; MOHAMMADI KOUROSH

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Journal: Journal of Water and Soil Year:2010 | Volume:24 | Issue:4 Page(s): 679-689

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Title

COMPARISON OF ARTIFICIAL INTELLIGENCE SYSTEMS (ANN & ANFIS) FOR REFERENCE EVAPOTRANSPIRATION ESTIMATION IN THE EXTREME ARID REGIONS OF IRAN

Author(s)

AHMADZADEH GHARAH GOUIZ K. | MIRLATIFI S.M. | MOHAMMADI KOUROSH | Issue Writer Certificate

Keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMQ3

EMPIRICAL MODELSQ3

EVAPOTRANSPIRATIONQ2

EXTRA ARID CLIMATEQ1

IRANQ2

Abstract

Evapotranspiration is one of the basic components of the hydrological cycle and is essential for estimating irrigation water requirement. In recent years, the use of intelligent systems for estimation of hydrological related parameters has increased dramatically. The objective of this study was to evaluate the possibility of estimating daily reference EVAPOTRANSPIRATION by using artificial intelligence systems, and to compare these systems together. The potential of using the ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) and the artificial neural network (ANN) techniques was investigated for estimating daily reference EVAPOTRANSPIRATION (ETo). Various daily climatic data including daylight hours, air temperature, relative humidity, and wind speed from three synoptic weather stations (Esfahan, Kerman and, Yazd) located in the extreme arid climatic regions of IRAN were used as inputs to the ANFIS and ANN techniques to estimate ETo as calculated by the FAO56 Penman-Monteith (F-P-M) equation. A comparison was made between the estimates provided by the ANN and ANFIS and those of the following EMPIRICAL MODELS: The Makkink, Priestley-Taylor, Hargreaves-Samani, FAO Blaney-Criddle, and Ritchie. Root mean squared errors, mean bias errors, and determination coefficient statistical indices were used for the evaluation of the performance of the models. Results revealed that the ANN and ANFIS techniques could be employed successfully for modeling daily ETo process. The ANFIS85 model with three input parameters including solar radiation, maximum air temperature, and wind speed estimated ETo with a higher degree of a accuracy than EMPIRICAL MODELS. The FAO Blaney-Criddle model was found to perform better than other EMPIRICAL MODELS included in this study.

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References

ASSESSMENT OF ARTIFICIAL NEURAL NETWORKS REVENUE IN REDUCING REQUIRED PARAMETERS FOR ESTIMATION OF REFERENCE EVAPOTRANSPIRATION

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APA: Copy

AHMADZADEH GHARAH GOUIZ, K., MIRLATIFI, S.M., & MOHAMMADI, KOUROSH. (2010). COMPARISON OF ARTIFICIAL INTELLIGENCE SYSTEMS (ANN & ANFIS) FOR REFERENCE EVAPOTRANSPIRATION ESTIMATION IN THE EXTREME ARID REGIONS OF IRAN. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 24(4), 679-689. SID. https://sid.ir/paper/141941/en

Vancouver: Copy

AHMADZADEH GHARAH GOUIZ K., MIRLATIFI S.M., MOHAMMADI KOUROSH. COMPARISON OF ARTIFICIAL INTELLIGENCE SYSTEMS (ANN & ANFIS) FOR REFERENCE EVAPOTRANSPIRATION ESTIMATION IN THE EXTREME ARID REGIONS OF IRAN. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2010;24(4):679-689. Available from: https://sid.ir/paper/141941/en

IEEE: Copy

K. AHMADZADEH GHARAH GOUIZ, S.M. MIRLATIFI, and KOUROSH MOHAMMADI, “COMPARISON OF ARTIFICIAL INTELLIGENCE SYSTEMS (ANN & ANFIS) FOR REFERENCE EVAPOTRANSPIRATION ESTIMATION IN THE EXTREME ARID REGIONS OF IRAN,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 24, no. 4, pp. 679–689, 2010, [Online]. Available: https://sid.ir/paper/141941/en

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