|تعداد مشاهده مقاله||111,651,710|
|تعداد دریافت فایل اصل مقاله||86,269,660|
Simulation of groundwater quality parameters using ANN and ANN+PSO models (Case study: Ramhormoz Plain)
|مقاله 3، دوره 3، شماره 2، تیر 2017، صفحه 191-200 اصل مقاله (808.52 K)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.7508/pj.2017.02. 003|
|Amir Soltani Mohammadi* ؛ Atefeh Sayadi Shahraki؛ Abd Ali Naseri|
|Irrigation and Drainage Department, Faculty of Water Sciences Engineering, Shahid Chamran University, Ahvaz, Iran|
|One of the main aims of water resource planners and managers is to estimate and predict the parameters of groundwater quality so that they can make managerial decisions. In this regard, there have many models developed, proposing better management in order to maintain water quality. Most of these models require input parameters that are either hardly available or time-consuming and expensive to measure. Among them, the Artificial Neural Network (ANN) Models, inspired from human brain, are a better choice. The present study has simulated the groundwater quality parameters of Ramhormoz Plain, including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), and Total Dissolved Solids (TDS), via ANN and ANN+ Particle Swarm Optimization (PSO) Models and at the end has compared their results with the measured data. The input data for TDS quality parameter is consisted of EC, SAR, pH, SO4, Ca, Mg, and Na, while for SAR, it includes TDS, pH, Na, and Hco3, and as for EC, it involves So4, Ca, Mg, SAR, and pH; all of them, gathered from 2009 to 2015. Results indicate that the highest prediction accuracy for SAR, EC, and TDS is related to the ANN + PSO model with the tangent sigmoid activation function so that both MAE and RMSE statistics have the minimum and R2 the maximum value for the model. Also the highest prediction accuracy is respectively related to EC, TDS, and SAR parameters. Considering the high efficiency of artificial neural network model, by training the PSO algorithm, it can be used in order to make managerial decisions and ensure monitoring and cost reduction results.|
|Artificial Neural Network؛ Particle Swarm Optimization Algorithm؛ Ramhormoz؛ Water quality|
Affadi, A., Watanabe, K. and Tirtomihardjo, H. (2007). Application of an artificial neural network to estimate groundwater level fluctuation, J. of Spatial Hydrology, 7(2), 23-46.
Ahmadi, Z., Zekri, M. and Beyjami, A. (2015). Predict the depth of the groundwater table using particle swarm optimization. In: Proceedings of 10th International Congress of Civil Engineering, 5-7 May, Tabriz, Abstract. [in Persian]
Alizadeh, A. (2001). Principles of applied hydrology. 3Th ed. Mashhad: Astan Qods Razavi Publishing. [in Persian]
Banejad, H., Kamali, M., Amirmoradi, K. and Olyaie, F. (2013). Forecasting some of the qualitative parameters of rivers using Wavelet Artificial Neural Network hybrid (W-ANN) model (Case of study: Jajroud river of Tehran and Gharaso river of Kermanshah). J. of Health and Environ., 6(3), 277-294. [in Persian]
Eberhart, R. and Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm, in: Proceedings of the Congress on Evolutionary Computation, 16-19 Jul 2000, La Jolla, 84-88.
Kuo, Y.M., Liu, C.W. and Lin, K.H. (2004). Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Research, 38(1), 148-158.
Mirzavand, M., Sadati Nrjad, M. and Akbari, M. (2015). Simulation changes in groundwater quality with artificial neural network model (Case study: Kashan aquifer). Iranian J. of Natural Resources, 68 (1), 159-171. [in Persian]
Musavi-Jahromi, S.H. and Golabi, M. (2008). Application of artificial neural networks in the river water quality modeling: Karoon river. Iran. J. of Appl. Sci., 8, 2324-2328.
Najah, A., Elshafie, A., Karim, O.A. and Jaffar, O. (2009). Prediction of Johor river water quality parameters using artificial neural networks. European J. of Scientific Research, 28(3), 422-435.
Noorani, V. and Salehi, K. (2008). Modeling of rainfall– runoff using fuzzy neural network and adaptive neural networks and fuzzy inference methods compare. Proceedings of 4Th National Congress on Civil Engineering; Tehran. [in Persian]
Noori, R., Karbassi, A.R., Moghaddamnia, A., Han, D., Zokaei-Ashtiani, M.H., Farokhnia, A. and Ghafari Gousheh, M. (2011). Assessment of input variables determination on the SVM model performance using PCA, Gamma test and forward selection techniques for monthly stream flow prediction. J. of Hydrol., 401(3-4), 177-189.
Noori, R., Khakpour, A., Omidvar, B. and Farokhnia, A. (2010). Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications, 37(8), 5856-5862.
Velayati, S. (2008). Hydrogeological formations in soft and hard. 1ed Edition, University of Mashhad Press, 396p. [in Persian]
Wenxian, G. and Hongxiang, W. (2010). PSO optimizing neural network for the Yangtze river sediment entering estuary prediction. Sixth International Conference on Natural Computation.
تعداد مشاهده مقاله: 2,662
تعداد دریافت فایل اصل مقاله: 1,462