PREDICTION OF GROUNDWATER LEVEL SHARIF ABAD CATCHMENT OF QOM USING WANN AND GP MODELS

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RAJAEI T.; ZEINIVAND A.; JAFARI H.

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Journal: JOURNAL OF GEOGRAPHICAL SCIENCES Year:2016 | Volume:16 | Issue:42 Page(s): 7-26

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Title

PREDICTION OF GROUNDWATER LEVEL SHARIF ABAD CATCHMENT OF QOM USING WANN AND GP MODELS

Author(s)

RAJAEI T. | ZEINIVAND A. | JAFARI H. | Issue Writer Certificate

Keywords

SHARIF ABAD PLAINQ1

GROUNDWATER LEVELQ1

NEURAL NETWORKQ1

WAVELET ANALYSISQ2

GENETIC PROGRAMMINGQ1

Abstract

In this study is predicted the GROUNDWATER LEVEL of Sharif Abad catchment using some artificial intelligence models. For this purpose used of monthly GROUNDWATER LEVELs for modeling in the three observed wells located in the Sharif Abad watershed of Qom. To compare the results of the hybrid model of WAVELET ANALYSIS-neural network (WNN), GENETIC PROGRAMMING (GP) multiple linear regression (MLR) and artificial NEURAL NETWORK (ANN), two criteria of root mean squared error (RMSE) and nash-sutcliffe coefficient of efficiency (E) is used. The results of the study indicated that the WNN models provide more accurate monthly GROUNDWATER LEVEL predicted in compared to the ANN, GP and MLR models so the nash-sutcliffe coefficient in WANN model for piezometers 1, 2 and 3 are 0. 98, 0. 98 and 0. 95, respectively.

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APA: Copy

RAJAEI, T., ZEINIVAND, A., & JAFARI, H.. (2016). PREDICTION OF GROUNDWATER LEVEL SHARIF ABAD CATCHMENT OF QOM USING WANN AND GP MODELS. JOURNAL OF GEOGRAPHICAL SCIENCES, 16(42 ), 7-26. SID. https://sid.ir/paper/102298/en

Vancouver: Copy

RAJAEI T., ZEINIVAND A., JAFARI H.. PREDICTION OF GROUNDWATER LEVEL SHARIF ABAD CATCHMENT OF QOM USING WANN AND GP MODELS. JOURNAL OF GEOGRAPHICAL SCIENCES[Internet]. 2016;16(42 ):7-26. Available from: https://sid.ir/paper/102298/en

IEEE: Copy

T. RAJAEI, A. ZEINIVAND, and H. JAFARI, “PREDICTION OF GROUNDWATER LEVEL SHARIF ABAD CATCHMENT OF QOM USING WANN AND GP MODELS,” JOURNAL OF GEOGRAPHICAL SCIENCES, vol. 16, no. 42 , pp. 7–26, 2016, [Online]. Available: https://sid.ir/paper/102298/en

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