Medium-term forecasts for salinity rates and groundwater levels

An increase in demand for groundwater associated with uncontrolled consumption has led to the depletion of groundwater wells. As a result, the mixing of seawater with groundwater increases the salinity rates, especially in areas where wells are close to the Mediterranean Sea in Gaza. In this paper, we use time-series data mining techniques to forecast the salinity rates and levels of groundwater in Deir El-Balah city-Gaza Strip. For this purpose, five forecasting techniques were applied on two data sets gained from the Palestinian Water Authority of Deir El-Balah City: salinity rates and groundwater levels (GL). The used forecasting algorithms are: exponential smoothing (ETS), state space model with Box-Cox transformation, ARMA errors, trend and seasonal components (TBATS), auto-regressive integrated moving average (ARIMA), ARIMA combined with: neural network (NN), ETS, and TBATS model. The best performance of the applied algorithms of salinity data according to Mean Absolute Percentage Error (MAPE) measure on the well S-69 was: ARIMA (MAPE = 4.2%), and (ARIMA + TBATS) for K-21 and K-20 which gave the MAPE = 5.4% and 4.0%, respectively. On the other hand, ARIMA was the most convenient algorithm to forecast the salinity rates of GL for S-50 (MAPE = 2.5) and ARIMA + NN (MAPE = 2.1) for J-103. The results demonstrated that in the period (2018-2023), the salinity rates will increase for S-69, K-20, and K-21 in comparison with the period (2012-2017) by 7.1%, 69.6%, and 55.7%, respectively. While the water levels of the wells: S-50 and J-103 will be lower than the sea water level in the period (2018-2023) by 1.89% and 6.37% in comparison with the period (2012-2017).