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 world is currently facing structural and deeply uncertain crises. Countries face paradoxical situations, where risks and opportunities, still poorly understood, coexist to accentuate uncertainty. Consequently, the challenges of growth and development are increasingly complex and interdependent. The role of science is becoming just as important in providing optimized solutions to these challenges. Thus, the reconciliation of scientific (and technical) capacities with socioeconomic needs is of crucial importance. This consists of specifying the scientific fields, subjects or questions where a reasonable research effort would have the best potential impact in meeting the needs, given the means available. In the face of complexity, this exercise must follow rigorous, but not strictly deterministic, approaches. In this sense, bibliometrics is often used to determine the state of knowledge in a field of research. Its ability to search large amounts of data and crosscheck analyses makes it a powerful decision-making tool. Used as a heuristic technique, it would stimulate, at different levels, the process of identifying and negotiating research priorities. Using the example of desalination research, the present work attempts to explore some uses of bibliometrics to inform the decision-making process in research prioritization. The theoretical underpinning of bibliometrics allows for heuristic approaches to data mining and provides an undeniable advantage in improving the effectiveness of traditional consensus-building methods for priority setting.

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.

This bibliometric analysis aims to map the evolution, disciplinary structure, and collaboration dynamics of green hydrogen (GH) research in North Africa from 2019 to 2025. Drawing on a corpus of ~39,000 global publications, indexed in Scopus and analysed through SciVal, we isolate and examine the contributions of Egypt, Morocco, Algeria, Tunisia, and Libya. Egypt leads the region with 842 publications and a field-weighted citation impact of 2.42, followed by Morocco (232 Pubs., FWCI 2.30) and Algeria (184 Pubs., FWCI 1.65). Notably, Tunisia exhibits the highest growth factor (41 times since 2019), while Libya remains marginal with only 18 publications in the GH field. The region is well represented in Energy and Environmental fields but is underrepresented in trendy areas such as Materials and Chemical Engineering, highlighting critical gaps in consistency, sophistication, and technical infrastructure. While international collaboration exceeds 69% for most countries, it rarely translates into a high impact compared to the global average. Conversely, the limited industrial collaboration shows the highest citation impact (e.g., Tunisia: 68 citations/publications). A thematic analysis reveals shared strengths in electrolytic hydrogen production and renewable energy integration, with Egypt showing diversification into microalgae and nanocomposites and Morocco excelling in techno-economic assessments and ammonia-based systems. By revealing patterns in research quality, collaboration, and thematic positioning, this study offers evidence-based insights to inform national science strategies, enhance regional cooperation, and position North Africa more strategically in the emerging global green hydrogen economy.