طريقة جديدة لإدارة نوعية المياه الجوفية

Abstract

The main source of water in Gaza Strip is the shallow aquifer, the quality of the aquifer's groundwater is extremely deteriorated in terms of salinity. Salinization of groundwater may be caused and influenced by many variables. Studying the relation of between these variables and salinity is often a complex and nonlinear process, making it suitable to model by Artificial Neural Networks (ANN). In order to model groundwater salinity in Gaza Strip using ANN it is necessary to gather data for training purposes. Initially, it is assumed that the groundwater salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate (R), abstraction (Q), abstraction average rate (Qr), life time (Lt), groundwater level Wl, aquifer thickness (Th), depth from surface to well screen (Dw), and distance from sea shore line (Ds). Data were extracted from 56 wells, most of them are municipal wells and they almost cover the total area of Gaza Strip. After a number of 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. 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. The ANN model proved that chloride concentration in groundwater is directly affected by abstraction (Q), abstraction average rate (Qr) and life time (Lt) and it was inversely affected by recharge rate (R) and aquifer thickness (Th). The approach is reasonable for the new planning and management of water resources through the attended reconstruction process in Gaza.