EcoVision: AI-Powered Environmental Risk Prediction and Sustainability Dashboard Using Explainable Machine Learning

Abstract

Increased urbanization and climate change have led to environmental problems, including the rising temperature, frequent flooding, and plastic wastes in large quantities in the urban areas. These problems need to be resolved with the help of data-driven tools in order to analyze the environmental trends and facilitate sustainable urban planning. The current paper will introduce EcoVision, which is an artificial intelligence-based environmental risk forecasting and sustainability dashboard that uses urban data to detect areas at risk regarding Urban Heat Island (UHI) intensity, flood threat, and plastic waste hotspots. The system employs machine learning models to act on the environmental data and urban data such as temperature, rainfall, population density, and land cover data, as well as air quality indicators. In order to enhance transparency and interpretability, Explainable Artificial Intelligence models like SHAP are incorporated to point out the major determinants of model predictions. The results forecasted are presented in the form of an interactive dashboard that gives maps, graphs, and analytical insights to facilitate an understanding of the environmental risks. EcoVision will help planners, environmental agencies and policymakers in the better understanding of the city development with sustainable development process through the combination of predictive analytics, explainable AI, and visual decision support. The proposed system is a contribution to the implementation of Sustainable Development Goals of climate action, sustainable cities, and careful management of resources.

Introduction

The text explains that rapid urbanization and population growth are creating serious environmental challenges in cities, including rising temperatures (urban heat islands), flooding, and plastic waste accumulation. Traditional monitoring systems are limited because they focus on single issues and lack predictive capabilities.

To address this, the proposed system EcoVision uses Artificial Intelligence and Machine Learning to analyze multiple environmental risks together. It predicts urban heat, flood risk, and plastic waste hotspots using data such as temperature, rainfall, population density, land cover, and air quality. It also applies Explainable AI (SHAP) to show which factors influence predictions.

EcoVision presents results through an interactive dashboard with maps and charts, helping planners and policymakers make informed decisions for sustainable urban development. Compared to previous research, it integrates multiple environmental issues, provides predictive insights, and offers a practical, user-friendly system for real-world use.

Conclusion

EcoVision is an AI-based environmental risk prediction system that is created to consider major urban sustainability issues, including Urban Heat Island intensity, flood risks, and plastic waste hotspots. It combines environmental data and machine learning to detect the high-risk regions and provide predictive information. Explainable AI models, including SHAP and LIME, are included to interpret AI model predictions and indicate the most significant environmental variables. The findings are provided in an interactive dashboard that represents the visualization of the risks to the environment through the use of maps and analytical charts thus making the data more comprehensible to the planners and decision makers. In general, the EcoVision shows the potential of AI-driven analytics in ensuring sustainable city development through the introduction of data-driven decisions and assisting authorities in proactive action to mitigate environmental risks and increase the resilience of the city.

References

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Copyright

Copyright © 2026 Dr. G Kavitha, Konreddy Himasri, Katta Soumith Kumar, Chennojwala Amrutha Varshini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.