Modeling of groundwater level in coastal aquifers using artificial neural networks : Gaza costal aquifer as case study

المؤلفون المشاركون

Nassar, Ahmad I.
al-Agha, Jawad S.
Mughir, Yunus

المصدر

The fifth International Conference on Engineering and Sustainability : is scheduled on November 4-5, 2014

الناشر

الجامعة الإسلامية كلية الهندسة

تاريخ النشر

2014-11-30

دولة النشر

فلسطين (قطاع غزة)

عدد الصفحات

16

التخصصات الرئيسية

العلوم الهندسية والتكنولوجية (متداخلة التخصصات)
علوم الأرض و المياه و البيئة

الملخص الإنجليزي

Gaza coastal aquifer (GCA) is the most precious natural source in Gaza Strip where it is the only source of water for different uses.

The extraction of groundwater currently exceeds the aquifer recharge rate ; as a result, the groundwater level (GWL) is falling continuously.

Therefore, forecasting of GWL is one of the most important requirements for effective management of groundwater.

The undertaken research is concerned with the development of GWL model using Artificial Neural Networks (ANNs).

The applicability of the developed ANNs models in simulating GWL was investigated for a part GCA.

In this study, dependent variable used in the developed ANNs model were the initial GWL, recharge from different source such as rainfall, recharge from return flow from both water and wastewater networks systems, recharge from return flow from irrigation water, abstraction from both municipal wells and agricultural wells.

The aforementioned input variables were used to predict GWL at 17 monitoring wells which distributed over all study area.

The performance of the best ANNs model was satisfactory where the correlation between the observed and predicted values of GWL was 0.993.

Moreover, ANNs model was utilized as a decision support tool by considering two future abstraction scenarios.

نوع البيانات

أوراق مؤتمرات

رقم السجل

BIM-775034

• APA
• MLA
• AMA