Application of Bayesian Networks and Support Vector Machine Model to Predict Changes in Water Level (Case Study: Ardebilplain)

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Akhoni Pourosseini F.; ASADI E.

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Journal: IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING Year:2017 | Volume:11 | Issue:36 Page(s): 33-42

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Title

Application of Bayesian Networks and Support Vector Machine Model to Predict Changes in Water Level (Case Study: Ardebilplain)

Author(s)

Akhoni Pourosseini F. | ASADI E. | Issue Writer Certificate

Keywords

Bayesian networksQ1

Gamma TestQ1

Groundwater levelQ1

Plain ArdebilQ1

Support Vector MachineQ1

Abstract

Groundwater has beenraised asone of themajorsources ofwater supplyfor drinking andagriculture, especially inarid andsemi-arid. Simulation ofgroundwatersystembecause of the complexityof these systemsis a difficult task. Inthispaper, usingdataArdabilplainwater levelin the period(1972-2011), theevaluationand selection ofappropriate inputsfor processinggammatestperformanceandefficiency ofthe least squaresSupport Vector Machines andBayesian networkmodelswere discussed. Monthlywater levelas inputparameterswithdifferentdelaysGammatestwas considered. Gamma Test results showed that the water level by 6 latency, offers better results to predict. Water level simulation using least squares Support Vector Machines and Bayesian network models also showed that the input structure to predict the water level the next month will be delayed until six. The two models with the same input structure, least squares Support Vector Machine model, better performance, according to the coefficient of determination 0. 977, mean absolute error0. 204 and root mean square error 0. 307, compared to Bayesian networks have. The results showed that Gamma Test compound in the appropriate input soft computing can have a better performance.

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APA: Copy

Akhoni Pourosseini, F., & ASADI, E.. (2017). Application of Bayesian Networks and Support Vector Machine Model to Predict Changes in Water Level (Case Study: Ardebilplain). IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, 11(36 ), 33-42. SID. https://sid.ir/paper/134724/en

Vancouver: Copy

Akhoni Pourosseini F., ASADI E.. Application of Bayesian Networks and Support Vector Machine Model to Predict Changes in Water Level (Case Study: Ardebilplain). IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING[Internet]. 2017;11(36 ):33-42. Available from: https://sid.ir/paper/134724/en

IEEE: Copy

F. Akhoni Pourosseini, and E. ASADI, “Application of Bayesian Networks and Support Vector Machine Model to Predict Changes in Water Level (Case Study: Ardebilplain),” IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, vol. 11, no. 36 , pp. 33–42, 2017, [Online]. Available: https://sid.ir/paper/134724/en

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