Groundwater is an essential source of freshwater to meet the growing water needs of agricultural, industrial, and domestic activities. the management of these resources is of crucial importance to ensure their sustainability and abundance over time and space. this major challenge requires extensive research for the prediction and simulation of groundwater level variations The new approach used in the field of hydrology and hydrogeology for the simulation and prediction of groundwater levels is based on machine learning techniques; these techniques are essential to improve the planning and management of groundwater resources. In this context, several researchers in this field have developed machine learning models capable of estimating the fluctuation of groundwater levels to protect these aquifers. This study launched word queries in several databases: Scopus, Science Direct, and IEEE; we selected 155 references and after the realization of the word clouds we find many topics: piezometer; groundwater; machine learning models, and parameters. In addition, the most recurrent machine learning models used by researchers in this field are Random Forest (RF), Decision Tree (DT), Support vector regression (SVM), artificial neural network (ANN), and SVR. But each of these researchers has based on some number of parameters as inputs; the most common are temperature; precipitation and groundwater level history. In this paper, we present a comparison of the most common machine learning models and parameters for assessing and predicting groundwater fluctuation.

The influx of foreign investment in Morocco necessitates a robust and integrated land information system to support informed decision-making and ensure sustainable growth. The fit-for-purpose cadastre emerges as a promising solution, offering transparency, efficiency, and data-driven land management. However, its full potential remains untapped due to persistent challenges: fragmented data across government agencies, opaque information exchange mechanisms, and outdated infrastructure. This research delves into these challenges by investigating the perspectives of diverse stakeholders crucial to successful cadastre implementation: land tenure specialists, public policymakers, spatial planners, and investors. Through in-depth interviews, we illuminate the current state of land information management in Morocco, pinpoint critical roadblocks hindering transparency, efficiency, and sustainability, and propose a multi-pronged approach to unlock the transformative potential of a fit-for-purpose cadastre system.

Machine learning is emerging as a powerful tool across many scientific fields, including water resource management. In Morocco, growing challenges such as climate change, population growth, and high water demand—especially in agriculture—have led researchers to apply these techniques to water-related issues. This study reviews recent research conducted in Morocco, highlighting major trends, scientific contributions, and progress in machine learning applications for hydrological challenges. Following the PRISMA framework, a systematic search was carried out in the Scopus database, resulting in 103 relevant publications affiliated with Moroccan institutions. Using NVIVO and SPSS software, key themes were identified, including water quality, groundwater management, and groundwater level prediction. The most frequently used models include Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). This article presents a comparative analysis of nine highly cited Moroccan studies, focusing on application areas, models, parameters, and performance. Findings show a clear rise in AI-related hydrological research in Morocco, especially in water quality monitoring, smart irrigation optimization, and groundwater level forecasting.