yousseftfifha/Groundwater-Management-under-Climate-Change

This project aims to study the impact of climate change on groundwater level in Mornag plain in Tunisia. Indeed, in the last few decades, aquifers all over the world have experienced notable water level variability due to the spatiotemporal variability of rainfall and temperature. Therefore, for a reliable groundwater management under climate change context, it is mandatory to analyze and estimate its level variability. In this study, we focus on the plain of Mornag, located in the southeast of Tunisia, since it represents 33% of the national agricultural production. From this plain, we have collected historical piezometric and pluviometric data covering the period 2005-2017. Knowing the pluviometric data, our goal is to predict the piezometric one. This issue has been already studied using classical numerical groundwater modeling such as Modflow and Feflow. Despite unsatisfactory results, these techniques are data and time consuming. To overcome all these drawbacks, we propose to use two Artificial Intelligence (AI) approaches: the Extreme Gradient Boosting (XGBoost) approach, that has shown great performances in literature, and the well used one in our context which involves the use of Long-Short Term Memory (LSTM) Neural Network. For better results, we have added supplementary features to our dataset such as the cluster zone (zones with same characteristics) and the Standardized Precipitation Index (SPI) which can identify drought at different time scales. Both approaches have been executed entirely on GPU for time acceleration. Compared with traditional existing methods, they both have shown a high level of accuracy which confirms their adequacy for groundwater level forecasting. The proposed prediction models will be used for evaluating the repercussions of climate change on groundwater levels under the different scenarios RCP 4.5 and RCP 8.5 for the period of 2017-2090. It will be evaluated for three future periods: 2017-2040 (short term), 2041-2065 (medium term) and 2066-2090 (long term). The analysis of the future results using AI will be considered as a new Decision Support System used to optimize the management of our limited resources in order to satisfy the needs of the population in terms of drinking water and agriculture production. # Groundwater-Management-under-Climate-Change This project aims to study the impact of climate change on groundwater level in Mornag plain in Tunisia. Indeed, in the last few decades, aquifers all over the world have experienced notable water level variability due to the spatiotemporal variability of rainfall and temperature. Therefore, for a reliable groundwater management under climate change context, it is mandatory to analyze and estimate its level variability. In this study, we focus on the plain of Mornag, located in the southeast of Tunisia, since it represents 33% of the national agricultural production. From this plain, we have collected historical piezometric and pluviometric data covering the period 2005-2017. Knowing the pluviometric data, our goal is to predict the piezometric one. This issue has been already studied using classical numerical groundwater modeling such as Modflow and Feflow. Despite unsatisfactory results, these techniques are data and time consuming. To overcome all these drawbacks, we propose to use two Artificial Intelligence (AI) approaches: the Extreme Gradient Boosting (XGBoost) approach, that has shown great performances in literature, and the well used one in our context which involves the use of Long-Short Term Memory (LSTM) Neural Network. For better results, we have added supplementary features to our dataset such as the cluster zone (zones with same characteristics) and the Standardized Precipitation Index (SPI) which can identify drought at different time scales. Both approaches have been executed entirely on GPU for time acceleration. Compared with traditional existing methods, they both have shown a high level of accuracy which confirms their adequacy for groundwater level forecasting. The proposed prediction models will be used for evaluating the repercussions of climate change on groundwater levels under the different scenarios RCP 4.5 and RCP 8.5 for the period of 2017-2090. It will be evaluated for three future …