INTRODUCTION AND APPLICATION OF LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) FOR SIMLULATION OF REFERENCE EVAPOTRANSPIRATION AND UNCERTAINTY ANALYSIS OF RESULTS, A CASE STUDY OF THE KERMAN CITY

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SEEFI A.; MIRLATIFI M.; REAHI H.

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Journal: Journal of Irrigation and Water Engineering Year:2013 | Volume:4 | Issue:13 Page(s): 67-79

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Title

INTRODUCTION AND APPLICATION OF LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) FOR SIMLULATION OF REFERENCE EVAPOTRANSPIRATION AND UNCERTAINTY ANALYSIS OF RESULTS, A CASE STUDY OF THE KERMAN CITY

Author(s)

SEEFI A. | MIRLATIFI M. | REAHI H. | Issue Writer Certificate

Keywords

LYSIMETER EVAPOTRANSPIRATIONQ2

MONTE-CARLO UNCERTAINTY ANALYSISQ2

ADAPTIVE NEURO FUZZY INFERENCE SYSTEMQ2

ARTIFICIAL NEURAL NETWORKSQ2

LEAST SQUARE SUPPORT VECTOR MACHINEQ2

Abstract

Estimating reference evapotranspiration (ETo) is one of the most important variables at water supply and distribution, irrigation management, irrigation systems design, agriculture and hydrological operations. The need for accurate estimates of ETo, complexity of ETo, unknowing mathematical of phenomenon, lack of reliable meteorological data, the cost of using lysimeters and their absent in most areas magnifies the need for developing new data mining methods. In this paper, the LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) model with three kernels function of RBF, Linear and Polynomial based on Gamma test used for estimating ETo and their results compared with other methods including ARTIFICIAL NEURAL NETWORKS (ANN) and ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) models. In this analysis, annual daily meteorological data of Kerman synoptic station and ETo lysimeter data used. In this research, Gamma test was used for selecting the best combinations of input parameters for various models used instead of using trial and error classic methods. The combination including maximum and dew point temperatures, relative humidity, wind speed, and solar radiation selected as the best combination for estimating ETo and modeling was based on this combination. The LSSVM model with RBF kernel performed better than the SVM model with polynomial and linear kernels. Additionally, the distribution of prediction error was calculated that the ANFIS and LSSVM-RBF created less error in train and test steps, respectively. At the end study, MONTE-CARLO UNCERTAINTY ANALYSIS was performed on results of different models that were used in this study. According to the results, predictions of LSSVM models showed less uncertainty than the ANFIS and ANN models were used.

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APA: Copy

SEEFI, A., MIRLATIFI, M., & REAHI, H.. (2013). INTRODUCTION AND APPLICATION OF LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) FOR SIMLULATION OF REFERENCE EVAPOTRANSPIRATION AND UNCERTAINTY ANALYSIS OF RESULTS, A CASE STUDY OF THE KERMAN CITY. IRANIAN OF IRRIGATION & WATER ENGINEERING, 4(13), 67-79. SID. https://sid.ir/paper/247263/en

Vancouver: Copy

SEEFI A., MIRLATIFI M., REAHI H.. INTRODUCTION AND APPLICATION OF LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) FOR SIMLULATION OF REFERENCE EVAPOTRANSPIRATION AND UNCERTAINTY ANALYSIS OF RESULTS, A CASE STUDY OF THE KERMAN CITY. IRANIAN OF IRRIGATION & WATER ENGINEERING[Internet]. 2013;4(13):67-79. Available from: https://sid.ir/paper/247263/en

IEEE: Copy

A. SEEFI, M. MIRLATIFI, and H. REAHI, “INTRODUCTION AND APPLICATION OF LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) FOR SIMLULATION OF REFERENCE EVAPOTRANSPIRATION AND UNCERTAINTY ANALYSIS OF RESULTS, A CASE STUDY OF THE KERMAN CITY,” IRANIAN OF IRRIGATION & WATER ENGINEERING, vol. 4, no. 13, pp. 67–79, 2013, [Online]. Available: https://sid.ir/paper/247263/en

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