GROUNDWATER LEVEL DETERMINATION BY USING ART IFICIAL NEURAL NETWORK (CASE STUDY: BIRJAND AQUIEFER)

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MOHTASHAM M.; DEHGHANI A.A.; AKBARPOUR ABOU ALFAZL; MEFTAH HALAGHI M.; ETEBARI B.

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Journal: Iranian Journal of Irrigation and Drainage Year:2010 | Volume:4 | Issue:1 Page(s): 1-10

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Title

GROUNDWATER LEVEL DETERMINATION BY USING ART IFICIAL NEURAL NETWORK (CASE STUDY: BIRJAND AQUIEFER)

Author(s)

Keywords

GROUND WATER TABLE NEURAL NET WORKQ2

BIRJAND PLAIN ALGORITHEM EDUCATUONQ2

Abstract

The use of groundwater always in one of the main sources of the drinking water and agricultural demands in the arid and semiarid areas. Birjand aquifer is located in the arid region and ground water is the main sources of fresh water. The aim of this research study is the prediction of groundwater level by using of artificial neural networks. To reach such goal, the Birjand plain is divided to sixteen polygons (according to sixteen piezometric wells) by using Tisen's polygon approach. Then in each polygon the amount of recharge (due to precipitation) and discharge (due to pumping wells) were calculated and selected as input parameters in addition to groundwater level of previous month. The groundwater level in present time was selected as output parameter in artificial neural network. The innovation of this study is to account the precise value of discharge of each polygon by considering the volumetric flow meter and duration of pumping form wells. It must be noted that in previous studies, the evapotranspiration from the leaf area of reference plant was selected as an index of discharge from each polygon. From 130 months measuring data of each piezometric well, the 80 months were selected as a training data and the rest of them for verification and test of artificial neural network. The various artificial neural networks such as feed forward back propagation, cascade forward back propagation were tested and the best architectures of artificial neural networks for each piezometric well were found by trial and errors. The results show that artificial neural networks can simulate the decreasing trend of the groundwater level and provide acceptable predictions up to 12 months ahead (R2=0.99, MSE=0.032). The results also show that the accuracy of estimation for closer piezometric wells to weather station is more than the piezometric wells located far from it.

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References

WATER TABLE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

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APA: Copy

MOHTASHAM, M., DEHGHANI, A.A., AKBARPOUR, ABOU ALFAZL, MEFTAH HALAGHI, M., & ETEBARI, B.. (2010). GROUNDWATER LEVEL DETERMINATION BY USING ART IFICIAL NEURAL NETWORK (CASE STUDY: BIRJAND AQUIEFER). IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, 4(1), 1-10. SID. https://sid.ir/paper/131775/en

Vancouver: Copy

MOHTASHAM M., DEHGHANI A.A., AKBARPOUR ABOU ALFAZL, MEFTAH HALAGHI M., ETEBARI B.. GROUNDWATER LEVEL DETERMINATION BY USING ART IFICIAL NEURAL NETWORK (CASE STUDY: BIRJAND AQUIEFER). IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE[Internet]. 2010;4(1):1-10. Available from: https://sid.ir/paper/131775/en

IEEE: Copy

M. MOHTASHAM, A.A. DEHGHANI, ABOU ALFAZL AKBARPOUR, M. MEFTAH HALAGHI, and B. ETEBARI, “GROUNDWATER LEVEL DETERMINATION BY USING ART IFICIAL NEURAL NETWORK (CASE STUDY: BIRJAND AQUIEFER),” IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, vol. 4, no. 1, pp. 1–10, 2010, [Online]. Available: https://sid.ir/paper/131775/en

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