Main Article Content
Abstract
Groundwater resources are one considered as one of the most common and important resources of drinking, agriculture and industry water. Due to the lowering of groundwater levels and its volatility, groundwater quality is of utmost importance. The aim of this study is to identify the predictive ability of artificial neural network of Multi-Layer Perceptron (MLP) and Support Vector Machine model and adaptive neuro-fuzzy inference system in which the quality of groundwater in Sirjan Plain has been predicted. A case study was conducted on the Sirjan Plain located in the city of Sirjan in Kerman province. For this purpose, the data of rainfall, the water level in wells and UTM coordinates of intended wells have been used as input combinations and qualitative parameters of the water of wells as output parameters. After initial processes such as normalization, for double-layer neural network, 85% of data were used for training and 15% for validation, and the same ration were applied to ANFIS and SVM. After reviewing the fitness statistical criteria such as correlation coefficient (R), and Root Mean Square Error (RMSE), it was observed that neural network presented an acceptable result compared to SVM and ANFIS models.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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- Jalali,M. (2005). Groundwater geochemistry in the Alisadr, Hamadan, western Iran. Environ Monit Assess DOI 10.1007/s1066100910075.
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- Jalalkamali, Amir., Moradi, Mehdi., Moradi, Nasrin. 2015. Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index, International Journal of Environmental Science and Technology, Volume 12, Issue 4, pp 1201-1210.
- Jalalkamali, A., Sedghi, H., Manshouri, M., 2011. Monthly groundwater level prediction using ANN and neuro-fuzzy models. Journal of Hydroinformatics 13.4, 867-876.
- Kaluli,J.W. C.A.,Madramootoo, and Y.,Djebbar. 1998. Modeling nitrate leaching using neural networks. Water Science Technology. 38(7): 127134.
- Khalil, A., Almasri, M. N., McKee, M., and Kaluarachchi, J. J., 2005. “Applicability Of Statistical Learning Algorithms In Groundwater Quality Modeling”,Water Resources Research, 41, W 05010, doi: 10.1029/2004WR003608.
- Office of Groundwater Studies, 2005, the report of continue reading the hydrogeologic studies plains piezometric network (1996-2004), Department of Water Resources Studies and Research, Tehran Regional Water Company, Ministry of Energy.
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References
Almasri, M.N. and J.J.,Kaluarachchi. 2000. Modular neural networks to predict the nitrate distribution in ground water using the onground nitrogen loading and recharge data. Environmental Modelling & Software 20: 851871.
Bray, M., Han, D. 2004. Identification of support vector machines for runoff modeling. Journal of Hydroinformatics 6, 265-280.
Cortes, C., and Vapnik, V. N., 1995. “Support Vector Networks”, Machine Learning, 20, pp. 273–297.
Diamantopoulou,M. J., V.Z.,Antonopoulos and D.M.,Papamichail. 2005. The use of a neural network technique for the prediction of water quality parameters of Axios River in Northern Greece. European Water. 11/12: 5562.
Jalali,M. (2005). Groundwater geochemistry in the Alisadr, Hamadan, western Iran. Environ Monit Assess DOI 10.1007/s1066100910075.
Jalalkamali, Amir. 2015. Using of Hybrid Fuzzy Models to Predict Spatiotemporal Groundwater Quality Parameters, Earth Science Informatics, Volume 8, Issue 4, Page 885-894.
Jalalkamali, Amir., Moradi, Mehdi., Moradi, Nasrin. 2015. Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index, International Journal of Environmental Science and Technology, Volume 12, Issue 4, pp 1201-1210.
Jalalkamali, A., Sedghi, H., Manshouri, M., 2011. Monthly groundwater level prediction using ANN and neuro-fuzzy models. Journal of Hydroinformatics 13.4, 867-876.
Kaluli,J.W. C.A.,Madramootoo, and Y.,Djebbar. 1998. Modeling nitrate leaching using neural networks. Water Science Technology. 38(7): 127134.
Khalil, A., Almasri, M. N., McKee, M., and Kaluarachchi, J. J., 2005. “Applicability Of Statistical Learning Algorithms In Groundwater Quality Modeling”,Water Resources Research, 41, W 05010, doi: 10.1029/2004WR003608.
Office of Groundwater Studies, 2005, the report of continue reading the hydrogeologic studies plains piezometric network (1996-2004), Department of Water Resources Studies and Research, Tehran Regional Water Company, Ministry of Energy.
Office of Basic Studies of Water Resources, 2003, The reported figures of water resources study area of the country, Department of Research and Basic Studies, Iran Water Resources Management Company, Ministry of Energy- Code: 1461 - 410 – 430.