Estimation of Water Balance Components in the Gaza Strip with GIS Based WetSpass Model

This study was initiated to estimate the water balance components in the Gaza Strip for the year 2013 using the WetSpass spatially distributed water balance model. Relevant input data for the model is prepared in the form of digital maps using GIS tools such as rainfall, air temperature, wind speed, potential evapotranspiration, soil, water depth, Topography, slope and land-use. The model produces digital maps of long-term average annual surface runoff, evapotranspiration and groundwater recharge. Results of the model show that 77% of the precipitation in the Gaza Strip is lost through evapotranspiration, 11% becomes surface runoff and 12% recharges the groundwater system. Analysis of the simulated results shows that WetSpass model is good enough to simulate the hydrological water balance components of the study area. Keywords: Water balance, WetSpass model, GI

Potential of Phosphorus Pollution in the Soil of the Northern Gaza Strip, Palestine

The damage and negative consequences of the Israeli Cast Lead on Gaza in the period between December 2008 - January 2009 is not only limited to the number of martyrs and wounded people, the destruction of houses and the infrastructure, but it also reached the environment. This paper investigates the occurrence of phosphorus (P) in the soil of the northern gover-norate of the Gaza Strip which has been shaped as a result of the heavily bombing of white phosphorus on Gaza during the war. We have measured soil Phosphorus concentrations in three different areas; agricultural, non-agricultural and urban areas. The obtained Olsen P values in most of the soil samples were ranked very high. The maximum value of phosphorous determined in agricultural areas was about 110.9 mg/ kg, in the non-agricultural areas adjacent to boarders 63.3 mg/ kg, and in urban areas 85.2 mg/ kg. The results show that the potential of phosphorus in the northern of the Gaza Strip is becoming higher than the allowed Olsen P values

Prediction of groundwater quality index in the Gaza coastal aquifer using supervised machine learning techniques

This paper investigates the performance of five supervised machine learning algorithms, including support vector machine (SVM), logistic regression (LogR), decision tree (DT), multiple perceptron neural network (MLP-NN), and K-nearest neighbours (KNN) for predicting the water quality index (WQI) and water quality class (WQC) in the coastal aquifer of the Gaza Strip. A total of 2,448 samples of groundwater were collected from the coastal aquifer of the Gaza Strip, and various physical and chemical parameters were measured to calculate the WQI based on weight. The prediction accuracy was evaluated using five error measures. The results showed that MLP-NN outperformed other models in terms of accuracy with an R value of 0.9945–0.9948, compared with 0.9897–0.9880 for SVM, 0.9784–0.9800 for LogR, 0.9464–0.9247 for KNN, and 0.9301–0.9064 for DT. SVM classification showed that 78.32% of the study area fell under poor to unsuitable water categories, while the north part of the region had good to excellent water quality. Total dissolved solids (TDS) was the most important parameter in WQI predictions while and were the least important. MLP-NN and SVM were the most accurate models for the WQI prediction and classification in the Gaza coastal aquifer. HIGHLIGHTS Machine learning (ML) algorithms are used for predicting water quality index.; Prediction performance of LogR, DT, KNN, SVM, and MLP-NN are compared.; MLP-NN and SVM-based prediction and quality classification models performed better than other ML-developed models.; Gaza coastal aquifer is experiencing a severe deterioration in water quality, as it is currently unsafe for drinking purposes without adequate treatment.