Application of Artificial Neural Networks Model as Analytical Tool for Groundwater Salinity

DOI: 10.4236/jep.2011.21006   PDF   HTML     5,202 Downloads   11,305 Views   Citations


The main source of water in Gaza Strip is the shallow coastal aquifer. It is extremely deteriorated in terms of salinity which influenced by many variables. Studying the relation between these variables and salinity is often a complex and nonlinear process, making it suitable to model by Artificial Neural Networks (ANN). Initially, it is assumed that the salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate, abstraction, abstraction average rate, life time and aquifer thickness. Data were extracted from 56 municipal wells, covering the area of Gaza Strip. After a number of modeling trials, the best neural network was determined to be Multilayer Perceptron network (MLP) with four layers: an input layer of 6 neurons, first hidden layer with 10 neurons, second hidden layer with 7 neurons and the output layer with 1 neuron which gives the final chloride concentration. The ANN model generated very good results depending on the high correlation between the observed and simulated values of chloride concentration. The correlation coefficient (r) was 0.9848. The high value of (r) showed that the simulated chloride concentration values using the ANN model were in very good agreement with the observed chloride concentration which mean that ANN model is useful and applicable for groundwater salinity modeling. ANN model was successfully utilized as analytical tool to study influence of the input variables on chloride concentration. It proved that chloride concentration in groundwater is reduced by decreasing abstraction, abstraction average rate and life time. Furthermore, it is reduced by increasing recharge rate and aquifer thickness.

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M. Seyam and Y. Mogheir, "Application of Artificial Neural Networks Model as Analytical Tool for Groundwater Salinity," Journal of Environmental Protection, Vol. 2 No. 1, 2011, pp. 56-71. doi: 10.4236/jep.2011.21006.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Metcalf & Eddy, “Costal Aquifer Management Program,” Final Report: Modeling of Gaza Strip Aquifer, The program is funded by US Agency for International Development (USAID) and owned by the Palestinian Water Authority (PWA). Gaza, Palestine, 2000.
[2] Coastal Aquifer Management Plan (CAMP), Gaza Coastal Aquifer Management Program, Vol. 1, Task 3, 2000.
[3] R. S. Govindaraju, “Artificial Neural Network in Hydrology,” Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp. 124-137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)
[4] United Nations Environment Programm (UNEP), “Desk Study on the Environment in the Occupied Palestinian Territories,” Switzerland, 2003.
[5] United Nations Environment Programm (UNEP), “Environmental Assessment of the Gaza Strip following the escalation of hostilities in December 2008-January 2009,” Gaza, 2009.
[6] Palestinian Water Authority (PWA), (2003). “Groundwater Levels Decline Phenomena in Gaza Strip Final Report,” Water Resources and Planning Department-Hy- drology Section, Palestinian National Authority, Gaza, February 2003.
[7] Coastal Municipality Water Utility (CMWU), “Annual Report of Wastewater Quality in Gaza Strip for years 2007 and 2008,” Gaza, Palestine, 2007.
[8] S. K. Jain, V. P. Singh and M. T. Van Genuchten, “Analysis of Soil Water Retention Data Using Artificial Neural Networks,” Journal Hydrologic Engineering, Vol. 9, No. 5, 2004, pp. 415-420. doi:10.1061/(ASCE)1084-0699(2004)9:5(415)
[9] S. Lee, S. Cho and M. Wong, “Rainfall Prediction Using Artificial Neural Networks,” Journal of Geographic Information and Decision Analysis, Vol. 2, No. 2, 1998, pp. 233-242.
[10] A. Ajith, “Artificial Neural Networks Oklahoma State,” Handbook of Measuring System Design, John Wiley & Sons, Ltd, University, Stillwater, USA, 2005.
[11] J. Hola and K. Schabowicz, “Application of Artificial Neural Networks to Determine Concrete Compressive Strength Based on Non-Destructive Test,” Journal of civil engineering and management, Vol. 11, No. 1, 2005, pp. 23-32.
[12] D. Jeng and H. Cha, “Application of Neural Network in Civil Engineering,” school of Engineering, Griffith University Gold Coast Campus, Australia, 2003.
[13] K. P. Sudheer, A. K. Gosain, R. D. Mohana and S. M. Saheb, “Modeling Evaporation Using an Artificial Neural Network Algorithm,” Hydrological Processes, Vol. 16, 2002, pp. 3189-3202. doi:10.1002/hyp.1096
[14] M. Claudius and K. Olaf, “Progress Report on Trend Analysis Methods, Tools and Data Preparation (in Particular Artificial Neural Networks (ANN)),” Center for Applied Geoscience, University of Tubingen, 2005.
[15] H. Jiang and W. R. Cotton, “Soil Moisture Estimation Using an Artificial Neural Network: A Feasibility Study,” Canadian Journal of Remote Sensing. Vol. 30, No. 5, 2004, pp. 827-839.
[16] Palestinian Water Authority (PWA), “National Water Plan,” Final Report. The study is funded by United Nation Development Program (UNDP) and owned by the Palestinian Water Authority (PWA). Gaza, Palestine, 2000.

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