EVALUATION OF THE EFFICIENCY OF SUPPORT VECTOR REGRESSION, MULTI-LAYER PERCEPTRON NEURAL NETWORK AND MULTIVARIATE LINEAR REGRESSION ON GROUNDWATER LEVEL PREDICTION (CASE STUDY: SHAHREKORD PLAIN)

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RAMEZANI CHARMAHINEH ABDOLLAH; ZOUNEMAT KERMANI MOHAMMAD

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Journal: JOURNAL OF WATERSHED MANAGEMENT RESEARCH Year:2017 | Volume:8 | Issue:15 Page(s): 1-12

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Title

EVALUATION OF THE EFFICIENCY OF SUPPORT VECTOR REGRESSION, MULTI-LAYER PERCEPTRON NEURAL NETWORK AND MULTIVARIATE LINEAR REGRESSION ON GROUNDWATER LEVEL PREDICTION (CASE STUDY: SHAHREKORD PLAIN)

Author(s)

RAMEZANI CHARMAHINEH ABDOLLAH | ZOUNEMAT KERMANI MOHAMMAD | Issue Writer Certificate

Keywords

GROUNDWATER LEVEL PREDICTIONQ1

MULTIVARIATE REGRESSIONQ1

PERCEPTRON NEURAL NETWORKQ1

SHAHREKORD PLAINQ2

SUPPORT VECTOR REGRESSIONQ1

Abstract

Accurate and reliable simulation and prediction of the groundwater level variation is significant and essential in water resources management of a basin. Models such as ANNs and SUPPORT VECTOR REGRESSION (SVR) have proved to be effective in modeling nonlinear function with a greater degree of accuracy. In this respect, an attempt is made to predict monthly groundwater level fluctuations using Multivariate Linear Regression, Multi-Layer PERCEPTRON NEURAL NETWORK models and two SVRs with RBF and linear function. In the present study, monthly data (from 2000 to 2010) of 18 observational wells in SHAHREKORD PLAIN were used for simulating and predicting the groundwater level. Regarding to NS efficiency and RMSE criteria, MLP model in 56% and SVR in 44%, have the best performance in comparison with other models. For an instance, in well No.1, SVR-RBF using input parameters of groundwater level, temperature, evaporation and precipitation is superior to other models. General efficiency of MLP, SVR-RBF, and SVR-Linear for NS criteria is 0.703, 0.656 and 0.655, respectively; and for RMSE criteria is 0.857, 0.905 and 0.914 meter, respectively. Results indicate that MLP and SVR models give better accuracy in predicting groundwater levels in the study area when compared to the linear model.

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APA: Copy

RAMEZANI CHARMAHINEH, ABDOLLAH, & ZOUNEMAT KERMANI, MOHAMMAD. (2017). EVALUATION OF THE EFFICIENCY OF SUPPORT VECTOR REGRESSION, MULTI-LAYER PERCEPTRON NEURAL NETWORK AND MULTIVARIATE LINEAR REGRESSION ON GROUNDWATER LEVEL PREDICTION (CASE STUDY: SHAHREKORD PLAIN). JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 8(15), 1-12. SID. https://sid.ir/paper/230365/en

Vancouver: Copy

RAMEZANI CHARMAHINEH ABDOLLAH, ZOUNEMAT KERMANI MOHAMMAD. EVALUATION OF THE EFFICIENCY OF SUPPORT VECTOR REGRESSION, MULTI-LAYER PERCEPTRON NEURAL NETWORK AND MULTIVARIATE LINEAR REGRESSION ON GROUNDWATER LEVEL PREDICTION (CASE STUDY: SHAHREKORD PLAIN). JOURNAL OF WATERSHED MANAGEMENT RESEARCH[Internet]. 2017;8(15):1-12. Available from: https://sid.ir/paper/230365/en

IEEE: Copy

ABDOLLAH RAMEZANI CHARMAHINEH, and MOHAMMAD ZOUNEMAT KERMANI, “EVALUATION OF THE EFFICIENCY OF SUPPORT VECTOR REGRESSION, MULTI-LAYER PERCEPTRON NEURAL NETWORK AND MULTIVARIATE LINEAR REGRESSION ON GROUNDWATER LEVEL PREDICTION (CASE STUDY: SHAHREKORD PLAIN),” JOURNAL OF WATERSHED MANAGEMENT RESEARCH, vol. 8, no. 15, pp. 1–12, 2017, [Online]. Available: https://sid.ir/paper/230365/en

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