عنوان مقاله [English]
Groundwater is one of the most important water resources on earth, and water salinity studies are very important for the protection and planning of water resources, especially in arid and semiarid areas such as Iran. Groundwater currently accounts for more than 90 percent of Iran’s total drinking water consumption. This water resource is less susceptible to bacterial pollution and evaporation than surface water, and hence it is more important than surface water.
Materials and Methods
An ANN includes three layers, namely, input layer, hidden layer and output layer. A network can have more than one hidden layer. In this study, multi-layer perceptron (MLP) was applied to simulate groundwater salinity. MLP is generated through adding one or more hidden layers to one-layer perceptron and can solve complex problems. The feed-forward neural network was the first and simplest type of artificial neural network devised. In a feed-forward network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes and to the output nodes. In the first stage of simulation, all data were normalized and divided into three classes: training data (65% of all data), test data (25% of all data) and cross validation data (10 % of all data). The different transfer functions such as hyperbolic tangent and sigmoid transfer functions were evaluated. Based on the results of this study (through trial-and-error method), the hyperbolic tangent transfer function was the best transfer function. Artificial neural network (ANN) is an efficient tool in hydrologic studies. In this study, an integration of ANN and GIS (the geographic information system) was applied to simulate groundwater salinity. ANN and GIS were, indeed, used for simulation purposes and as a pre-processing and post-processing system of the applied data, respectively. Thus, GIS was applied as an efficient tool to provide the base maps and to estimate the model’s quantitative parameters. Different digital/base maps were provided in GIS environment including DEM, transmissivity of aquifer formations, water table depth, precipitation values and distance from Caspian Sea and water resources using topographic maps of the region and EC values using water salinity secondary data. Different piezometric wells were selected to simulate groundwater salinity (EC). In GIS pre-processing stage, raster layers of the input factors were provided and combined using overlay analysis with a pixel size 1×1 km. Therefore, the surface of study plain was separated to more than 10000 geo-referenced pixels (1×1km). These pixels had values of model inputs or groundwater salinity factors (transmissivity of aquifer formation, water table depth and the distance from water resource). We inserted the site coordinate for every pixel automatically in the GIS medium. Pixels data (networks inputs and coordinate) were exported from GIS and then imported to NeuroSolutions software. In ANN medium, groundwater salinity (EC) was simulated using the validated optimum network for all of the 10000 pixels (the whole study plain).
1- Anctil, F. and Rat, A., 2005. Evaluation of neural network streamflow forecasting on 47 watersheds. Journal of Hydrologic Engineering, 10(1), pp.85-88.
2- Chen, J. and Adams, B.J., 2006. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. Journal of Hydrology, 318(1), pp.232-249.
3- Daliakopoulos, I.N., Coulibaly, P. and Tsanis, I.K., 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(1), pp.229-240.
4- Ducci, D. and Sellerino, M., 2013. Vulnerability mapping of groundwater contamination based on 3D lithostratigraphical models of porous aquifers. Science of the Total Environment, 447, pp.315-322.
5- Gangopadhyay, S., Gautam, T.R. and Gupta, A.D., 1999. Subsurface characterization using artificial neural network and GIS. Journal of Computing in Civil Engineering, 13(3), pp.153-161.
6- Gholami, V. and Darvari, Z. 2013., Comparison of Performance of Multiple Regression and Artificial Neural Network (ANN) in Simulation of Groundwater Salinity on Mazandaran Provinces. Journal of Water Research in Agriculture. 26(1): 356-355. (In Persian).
7- Gholami, V., Yousefi, Z. and Rostami, H.Z., 2010. Modeling of ground water salinity on the Caspian southern coasts. Water Resources Management, 24(7), pp.1415-1424.
8- Ghosh, N.G. and Sharma, K.D. 2006. Groundwater Modeling and Management, Capital Publishing Company.Inter-basin of Odisha, India, Journal of Hydrology 495:38–51.
9- Jang, C.S. and Chen, S.K., 2015. Integrating indicator-based geostatistical estimation and aquifer vulnerability of nitrate-N for establishing groundwater protection zones. Journal of Hydrology, 523, pp.441-451.
10- Krishna, B., Satyaji Rao, Y.R. and Vijaya, T., 2008. Modelling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrological Processes, 22(8), pp.1180-1188.
11- Lallahem, S., Mania, J., Hani, A. and Najjar, Y., 2005. On the use of neural networks to evaluate groundwater levels in fractured media. Journal of Hydrology, 307(1), pp. 92-111.
12- Langford, R.P., Rose, J.M. and White, D.E., 2009. Groundwater salinity as a control on development of eolian landscape: An example from the White Sands of New Mexico. Geomorphology, 105(1), pp.39-49.
13- Li, X., Shu, L., Liu, L., Yin, D. and Wen, J., 2012. Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling. Hydrogeology Journal, 20(4), pp.727-738.
14- Mahdavi, M. 1999. Applied Hydrology, Tehran University Press. 324, pp. (In Persian).
15- Mohanty, S., Jha, M.K., Kumar, A. and Panda, D.K., 2013. Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India. Journal of Hydrology, 495, pp.38-51.
16- Mondal, N.C., Singh, V.P., Singh, V.S. and Saxena, V.K., 2010. Determining the interaction between groundwater and saline water through groundwater major ions chemistry. Journal of Hydrology, 388(1), pp.100-111.
17- Rajurkar, M.P., Kothyari, U.C. and Chaube, U.C., 2004. Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology, 285(1), pp.96-113.
18- Samani, N., Gohari-Moghadam, M. and Safavi, A.A., 2007. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology, 340(1), pp.1-11.
19- Shah, T., Roy, A.D., Qureshi, A.S. and Wang, J., 2003, May. Sustaining Asia’s groundwater boom: an overview of issues and evidence. In Natural Resources Forum (Vol. 27, No. 2, pp. 130-141). Blackwell Publishing Ltd.
20- Singh, C.K., Shashtri, S., Mukherjee, S., Kumari, R., Avatar, R., Singh, A. and Singh, R.P., 2011. Application of GWQI to assess effect of land use change on groundwater quality in lower Shiwaliks of Punjab: remote sensing and GIS based approach. Water Resources Management, 25(7), pp.1881-1898.
21- Stigter, T.Y., Ribeiro, L. and Dill, A.C., 2006. Application of a groundwater quality index as an assessment and communication tool in agro-environmental policies–Two Portuguese case studies. Journal of Hydrology, 327(3), pp.578-591.
22- Tokar, A.S. and Markus, M., 2000. Precipitation-runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering, 5(2), pp.156-161.