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Machine Learning and Remote Sensing for Soil Moisture Prediction: A Smart Agriculture Approach in Morocco
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Author(s): Abdessamad Elmotawakkil (Department of Computer Science, Ibn Tofaïl University, Kenitra, Morocco), Saad Jaldi (Faculty of Humanities and Social Sciences, Ibn Tofail University, Kenitra, Morocco), Mohammed Bouhassane (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco), Adil Moumane (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco)and Nourddine Enneya (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco)
Copyright: 2026
Pages: 26
Source title:
Advancing Environmental Research Through Applied GIS and Remote Sensing
Source Author(s)/Editor(s): Jamal Al Karkouri (Ibn Tofail University, Morocco), Adil Moumane (Ibn Tofail University, Morocco), Abdessamad Elmotawakkil (Ibn Tofail University, Morocco)and Mouhcine Batchi (Ibn Tofail University, Morocco)
DOI: 10.4018/979-8-3373-6608-1.ch007
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Abstract
Artificial intelligence (AI) plays a crucial role in soil moisture prediction, essential for sustainable agriculture in arid regions where water scarcity and climate variability threaten crop yields. This study introduces an AI-driven framework to forecast soil moisture across five sites in Morocco's Draa Valley (Agdz, Tagounite, Tamegroute, Tansikht, Zagora). A dataset (2003–2024) was built by integrating historical climate records with remote sensing indicators using Google Earth Engine (GEE). Six models Random Forest (RF), XGBoost, CatBoost, k-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN) were assessed with RMSE, NSE, MSE, and MAPE. Results showed that tree-based models clearly outperformed deep learning, with RF, XGBoost, and CatBoost achieving RMSE of 2.89%–9.11% and NSE > 0.965. Findings highlight the potential of AI-based soil moisture prediction to enhance irrigation scheduling, optimize water allocation, and support climate-resilient farming, offering a scalable solution for precision agriculture.
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