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.

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 article will examine the data collection process, management flow, and decision-making process through interviews with public sector representatives responsible for water management.Natural resources have always been at the heart of the world’s most important government strategies. The development of economies is contingent upon the effective management of a country’s water resources, which in turn requires good water governance that depends on the collection of sufficient data and an effective data management system (1), especially with climate change continuing to impact this resource around the world.The management of natural resources, in general, necessitates collecting data and raw measurements, resulting in storing a large volume of data. Therefore, Big Data Analytics (BDA) technology is considered a primary means of processing such data. However, while Big Data technologies offer a suitable solution for ensuring optimal and rapid use of data, the success of functional and technical designs can only be guaranteed by maintaining complete control over the processing and decision-making processes.

This paper delves into the adoption of Big Data Analytics (BDA) within the Moroccan water management sector, specifically examining data collection, management flow, and decision-making processes. Through insightful interviews with representatives from the public sector responsible for water management, this study investigates the pivotal role of effective data governance in optimizing water resources[1]. Given the centrality of natural resources, particularly water, in national development strategies, the imperative for robust water governance is underscored. The impact of climate change further highlights the need for sufficient data collection and an efficient data management system[2]. In the management of natural resources, the accumulation of extensive data necessitates the application of BDA technology for streamlined processing. While BDA provides a solution for optimal data utilization, this article emphasizes the importance of maintaining control over processing and decision-making to ensure the success of functional and technical designs. The outcomes contribute to the formulation of an adoption model for BDA in the Moroccan water sector, facilitating well-informed and strategic decision-making for sustainable water resource management.