Artificial intelligence (AI), particularly machine learning algorithms, is redefining the process of watershed ecological security assessment, shifting from static statistical models to dynamic predictive models with high adaptability. In land management, AI acts as an analytical “brain” capable of processing complex patterns of land degradation, erosion, and desertification through the handling of large-scale datasets (Big Data). In terms of predictive capability, the application of models such as Random Forest or deep learning enables soil erosion prediction with accuracy exceeding 85% compared to traditional regression methods. This technology is capable of simultaneously analyzing composite indicators of ecosystem health (EH), ecosystem service value (ESV), and ecological risk (ER) from petabyte-scale datasets collected from satellites and sensors (Zhang et al., 2025).
This integration allows for the early detection of land degradation signals, helping to prevent long-term environmental consequences. In addition, AI-based decision support systems provide early forecasting scenarios of environmental changes within a 6 to 12-month timeframe. This enables policymakers to proactively adjust land-use planning, minimize ecological risks, and conserve biodiversity. AI not only automates repetitive tasks but also supports multi-objective analysis, thereby optimizing urban land allocation in conjunction with the conservation of ecosystem service values. Mastering machine learning technology is a key step toward intelligent and sustainable land governance, ensuring a balance between economic development and environmental security in the future. The integration of technical infrastructure and local knowledge further enhances land allocation efficiency, laying the foundation for evidence-based environmental governance (Ihwughwavwe & Aniebonam, 2025).
Authors: Hao Phu Dong, Binh Thanh Nguyen*
References:
Ihwughwavwe, S. I., & Aniebonam, S. O. (2025). Conceptual Framework for Developing Predictive Models for Environmental Risk Assessment in Agricultural Ecosystems.
Zhang, L., Li, Y., Wei, K., & Qi, S. (2025). Construction and Optimization of Ecological Security Pattern in Kunming: Insights From Machine Learning Algorithms and Circuit Theory. Land Degradation & Development, n/a(n/a). https://doi.org/https://doi.org/10.1002/ldr.70330
ⓒ 2026 BICE | Biochar-based Integrated Circular Economy for Paddy Rice.
