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Islamic University – Gaza (2018)

Medium-Term Forecasting for Rain Amounts and Groundwater Production (Dear El-Balah City as A Case Study)

Ihsan Abd Al-Majeed Solaiman Abu Amra

Titre : Medium-Term Forecasting for Rain Amounts and Groundwater Production (Dear El-Balah City as A Case Study)

Auteur : Ihsan Abd Al-Majeed Solaiman Abu Amra

Université de soutenance : Islamic University – Gaza

Grade : Master of Information Technology 2018

Résumé
Forecasting is a data mining technique which benefits from numerous sources of time-series data to derive value from historical data and helps business decision-makers for effective planning. Groundwater is the main water source in Gaza that decreasing due to population growth. A real water crisis is found because of the lack of rainfall. Moreover, an increase in demand for groundwater and reduced rainfall, which is the main source of groundwater recharge, will lead to the depletion of groundwater wells. As a result, mixing seawater with groundwater increases the salinity rate, especially in areas where wells are close to the Mediterranean Sea in Gaza. Wells digging without governmental control, increasing salinity percentages. Therefore, it is necessary to focus on the relationship between rainfall - which feeds the groundwater reservoir and reduces its salinity - and the percentages of the production for the groundwater. In this thesis, we conducted the forecasting techniques on two real data sets : the groundwater production amounts we gained from the Ministry of Agriculture and the rain amounts from the Coastal Municipalities Water Utility (CMWU) of Dear El-Balah City in the Gaza Strip. The following forecasting algorithms are used : Auto-Regressive Integrated Moving Average (ARIMA), ARIMA combined with Neural Network (NN), Exponential Smoothing (ETS) and State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) and ETS. The best performance of applied algorithms on rainfall data according to Mean Absolute Percentage Error (MAPE) measure is (ARIMA+NN) which gave the MAPE = 21%. On the other hand, (ARIMA) is the best algorithm applied to wells’ production data which achieved MAPE= 4.9%. The results have shown that after five years the amounts of rainfall and groundwater production in comparison with the period from (2013 to 2017) will decrease by 8.4%, 1.05%, respectively. Based on these results, the salinity is expected to increase in the coming years making the groundwater unusable.

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Page publiée le 4 décembre 2019