In the arid landscapes of Morocco, where water scarcity is a daily reality, researchers are turning to machine learning to navigate one of the country’s most pressing challenges. A new study by Rachid El Ansari and his team at the Laboratory of Engineering Sciences at Ibn Tofaïl University is shedding light on how artificial intelligence is transforming water resource management. Their systematic review, published in *Engineering Proceedings* (formerly known as *Proceedings*), highlights a surge in AI-driven solutions to tackle water quality, groundwater management, and irrigation efficiency—key issues for a nation where agriculture consumes nearly 80% of its water supply.
El Ansari notes that the research team identified 103 Moroccan-affiliated studies in the Scopus database, revealing a clear trend: “There’s a growing recognition that traditional hydrological models alone can’t keep up with the pressures of climate change and population growth.” Among the most promising tools are machine learning models like Random Forest, Support Vector Machine, and Artificial Neural Networks, which are being deployed to predict groundwater levels, optimize irrigation, and monitor water quality in real time.
For industries dependent on water—especially energy producers—these advancements could be transformative. Power plants, desalination facilities, and even renewable energy projects like solar farms often face water constraints. “Improved groundwater forecasting could help energy companies plan more efficiently, reducing downtime and operational costs,” El Ansari suggests. Smart irrigation systems, powered by AI, could also cut water waste in agriculture, freeing up resources for other sectors.
The study’s findings suggest that Morocco is at the forefront of integrating AI into water management, but the implications extend beyond its borders. As climate variability intensifies, the techniques developed here could serve as a blueprint for other water-stressed regions. The shift from reactive to predictive water management—enabled by machine learning—could redefine how industries and governments approach sustainability.
For now, El Ansari’s team is focused on refining these models, but the message is clear: the future of water management won’t just be about pipes and pumps—it’ll be about algorithms and data. And in a world where water is the new oil, that’s a game-changer.