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Accurate prediction of the water level will help prevent overexploiting groundwater and help control water resources. On the other hand, water level predicting is a highly dynamic and non-linear process dependent on complex factors. Therefore, developing models to predict water levels to optimize water resources management in the reservoir is essential. Thus, this work recommends various supervised machine learning algorithms for predicting water levels with groundwater level correlation. The predicting models have Linear Regression (LR), Support Vector Machines (SVM), Gaussian Processes Regression (GPR), and Neural Network (NN). This study includes four scenarios; The first scenario (SC1) uses lag 1; second scenario (SC2) uses lag 1 and lag 2; third scenario (SC3) uses lag 1, lag 2, and lag 11 and the fourth scenario (SC4) uses lag 1, lag 2, lag 11 and lag 12. These scenarios have been determined using the autocorrelation function (ACF), and these lags represent the month. The results showed that for SC1, SC2, and SC4, all model performance in GPR gave good results where the highest R equal to 0.71 in SC1, 0.78 in SC2, and 0.73 in SC4 using the Matern 5/2 GPR model. For SC3, the Stepwise LR model gave a better result with an R of 0.79. It can be concluded that Matern 5/2 of Gaussian Processes Regression Models is a reliable model to predict water level as the method gave a high performance in each scenario (except SC3) with a relatively fastest training time. The NN model had the worst performance to the other three models since it has the highest MAE values, RMSE, and lowest value of R in almost all four scenarios of input combinations. These results obtained in this study serves as an excellent benchmark for future water level prediction using the GPR and LR with four scenarios created. [ABSTRACT FROM AUTHOR]

Depletion and pollution of groundwater, Earth's largest and most accessible freshwater stock, is a global sustainability concern. A changing climate, marked by more frequent and intense hydrologic extremes, poses threats to groundwater recharge and amplifies groundwater use. However, widespread human development and contamination of groundwater reservoirs pose an immediate threat of resource extinction with impacts in many regions with dense population or intensive agriculture. A rapid increase in global groundwater studies has emerged, but this has also highlighted the extreme paucity of data for substantive trend analyses and assessment of the state of the global resource. Noting the difficulty in seeing and measuring this typically invisible resource, we discuss factors that determine the current state of global groundwater, including the uncertainties accompanying data and modeling, with an eye to identifying emerging issues and the prospects for informing local to global resource management in critical regions. We comment on some prospective management strategies. [ABSTRACT FROM AUTHOR]

Groundwater management is important for all urban systems. Thus, data needs to be available on request for various decision-makers and stakeholders. This article presents conceptual and implemented framework for collecting, analyzing and sharing of groundwater data for various purposes. It allows controlled access to data that is continuously collected from different feeds and transformed into a common format. With this approach, the latest data as well as historical records are always available for real-time queries and further analysis. The proposed system can be extended to cover other areas of data collection in the future.

The paper presents the results of three-dimensional modelling of an area extending over approximately 800 square kilometres along the southern margin of the Po Delta. The geological model describes the Holocene sequence, here approximately 35-40 m thick. Developed on a framework of existing knowledge, it allows a more in-depth analysis of the subsurface stratigraphy of this sector of the Emilia-Romagna plain, identifying five units (including the Pleistocene substrate) that record the transgressive-regressive sedimentary cycle of the last 10 000 years. The geological model is used as base structure for developing 3D thematic mapping by processing cone penetration tests (CPT) data, their derived parameters and data related to the water quality of the coastal aquifer. Integration and correlation of the onshore geological data withmarine geophysics and seabed core drillings has enabled the creation of a detailed model of recent deltaic and littoral morpho- depositional elements. This study shows that 3D modelling yields suitable tools for the insight and representation of the subsurface architecture and affords a better understanding of processes such as subsidence, saltwater intrusion into aquifers and the dynamics of coastal sands, processes, which are currently a matter of concern for the Emilia-Romagna regional government [ABSTRACT FROM AUTHOR]