An Explainable Hybrid AI Framework for Climate-Driven Environmental Health Risk Prediction in Agro-Ecosystems

Climate change and environmental variability increasingly affect human health, particularly in agroecosystems exposed to fluctuating air quality and climatic conditions. Al-though recent advances in artificial intelligence have improved environmental risk prediction, many existing approaches operate as black-box systems and provide limited support for transparent decision-making and actionable interventions. This study presents an explainable hybrid artificial intelligence framework for climate-driven environmental health risk prediction. The proposed framework integrates environmental monitoring data, including Air Quality Index (AQI), temperature, and humidity measurements collected from publicly available environmental sources, with ensemble machine learning models (Random Forest, XGBoost, and LightGBM), SHAP-based explainability, and a Retrieval-Augmented Generation (RAG) module. Unlike conventional prediction systems, the proposed approach combines interpretable machine learning with evidence-grounded recommendation generation to enhance both transparency and practical usability. Experimental results indicate that XGBoost achieves the highest predictive performance, reaching an accuracy of 0.88 and an AUC of 0.91. SHAP analysis identifies AQI as the most influential factor affecting environmental health risk, followed by temperature and humidity. Furthermore, the RAG module was evaluated in terms of retrieval relevance and recommendation consistency, demonstrating its ability to generate context-aware recommendations supported by scientific knowledge sources. The proposed framework extends existing environmental health prediction approaches by jointly integrating predictive modeling, explainability, and knowledge-driven reasoning within a unified decision-support system. The results highlight its potential for supporting proactive environmental health management and climate-resilient decision-making in agroecosystems.

@article{Alaoui2026,
  title     = {An Explainable Hybrid AI Framework for Climate-Driven Environmental Health Risk Prediction in Agro-Ecosystems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Fatima-Zahra Alaoui and Laila El Jiani and Sanaa El Filali and Rachida Ait Abdelouahid and Zouheir Banou},
  doi       = {10.14569/IJACSA.2026.0170687},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170687}
}