Application of bayesian network and LS-SVM methods in predicting water surface level of Urmia Lake

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Karimzadegan S.; BEHMANESH J.; REZAIE H.

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Journal: Journal of Water and Soil Conservation Year:2018 | Volume:25 | Issue:3 Page(s): 193-207

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Title

Application of bayesian network and LS-SVM methods in predicting water surface level of Urmia Lake

Author(s)

Karimzadegan S. | BEHMANESH J. | REZAIE H. | Issue Writer Certificate

Keywords

Bayesian networkQ2

Ecological levelQ1

Least Square Support Vector MachineQ1

PredictionQ1

Surface levelQ1

Abstract

Background and Objectives: Urmia Lake, as an important water ecosystem, is located in the northwest of Iran. Over the past 14 years, the average of Urmia Lake water Surface level has decreased to 1272. 2 m and it means that the difference between the Ecological level of the lake and the present water level is 2-meter. Drying of the Urmia Lake will cause serious problems and crises for the watershed, adjacent provinces and the country. In this research, direct and indirect effective parameters in Prediction of the lake level such as evaporation, input runoff to the lake, precipitation, temperature, wind, mean air humidity and water level in the previous month have been employed. The comparison of the efficiency of two models namely the Bayesian network, which is a probabilistic model under conditions of uncertainty and the machine algorithm of Least Square Support Vector Machine (LS-SVM) is the main objective of the present research. Materials and Methods: In the present research, two methods including least squares support vector machine and Bayesian were employed in the modeling process. In this study, effective factors in predicting Urmia lake water level in the previous month as inputs for the used models and water level in the present month as outputs were investigated. Results: The analysis of hydrometric stations data showed that the data of only 4 stations among 13 stations are fitted to the normal distribution. Comparison and investigation of the results of two model by examining the coefficients R2, RMSE, MBE and Nash-Sutcliff indicated superiority of the least squared vector machine model compared to the Bayesian network. These values for the superior model were 92. 3%, 0. 082, 0. 122 and 0. 86, respectively. Conclusion: In this research, the criteria for evaluating and comparing the accuracy of the two model in Prediction based on R2, RMSE, MBE and Nash-Sutcliff criteria indicated that the least square vector machine is superior to the Bayesian model. However, the important point in comparing the two model is that the nature of the least squares support vector machine is a machine, however, the nature of the Bayesian network is a probabilistic model under uncertainty conditions in which the normal distribution is used to train network variables because the nature of events is random. Therefore, in this research, the use of Bayesian network model is recommended to the least-squares support vector machine model.

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APA: Copy

Karimzadegan, S., BEHMANESH, J., & REZAIE, H.. (2018). Application of bayesian network and LS-SVM methods in predicting water surface level of Urmia Lake. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 25(3 ), 193-207. SID. https://sid.ir/paper/156123/en

Vancouver: Copy

Karimzadegan S., BEHMANESH J., REZAIE H.. Application of bayesian network and LS-SVM methods in predicting water surface level of Urmia Lake. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2018;25(3 ):193-207. Available from: https://sid.ir/paper/156123/en

IEEE: Copy

S. Karimzadegan, J. BEHMANESH, and H. REZAIE, “Application of bayesian network and LS-SVM methods in predicting water surface level of Urmia Lake,” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 25, no. 3 , pp. 193–207, 2018, [Online]. Available: https://sid.ir/paper/156123/en

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