As cities expand and climate patterns shift, urban environments are becoming increasingly vulnerable to extreme heat. The Urban Heat Island phenomenon, where urban areas experience significantly higher temperatures than surrounding regions, poses serious risks to public health, infrastructure, and environmental sustainability. Addressing this challenge requires solutions that are not only technologically advanced but also transparent, interpretable, and grounded in real-world data.
In this context, JK Business School, Gurugram takes pride in the research contribution of its faculty members Dr. Sukhamaya Swain and Dr. Siddhartha Bhattacharya, who have co-authored an IEEE-indexed research paper titled “Implementation of Explainable AI (XAI) Approach for Combating Urban Heat Island in a Changing Climate.” The study was published in IEEE Xplore in January 2026, marking a significant academic milestone for the institution.
Why Explainable AI Matters in Climate Research
Artificial Intelligence is increasingly used to model complex environmental systems. However, traditional machine learning models often function as black boxes, offering predictions without clear explanations. In climate-sensitive applications, such opacity can limit trust, adoption, and policy relevance.
The research introduces Explainable AI, an approach that prioritizes transparency and interpretability alongside predictive performance. By enabling stakeholders to understand how models arrive at specific outcomes, XAI bridges the gap between advanced analytics and actionable decision making.
A Data-Driven Framework Using Geospatial Intelligence
The study presents a comprehensive AI framework that integrates geospatial data, climatic indicators, and explainable machine learning techniques to identify the key drivers of urban heat vulnerability. Rather than focusing solely on prediction, the framework highlights why certain urban regions are more susceptible to heat stress.
Key aspects of the research include
- use of spatial data to map heat exposure patterns
- identification of urban features contributing to heat accumulation
- application of explainable models to enhance interpretability
- alignment of AI insights with climate adaptation planning
This approach ensures that the outputs are not only accurate but also meaningful for planners, policymakers, and sustainability professionals.
From Algorithms to Real-World Impact
One of the defining strengths of this research lies in its real-world applicability. The framework supports climate-resilient urban planning by enabling authorities to prioritize interventions, allocate resources efficiently, and design mitigation strategies informed by transparent evidence.
By making AI insights understandable, the study empowers decision makers to move beyond abstract models and adopt solutions grounded in both data and context.
Advancing the Mission of AI for Good
This publication reflects JK Business School’s broader commitment to interdisciplinary research, sustainability, and AI for social good. The work demonstrates how advanced technologies can be responsibly applied to address pressing global challenges such as climate change and urban resilience.
At JK Business School, research is encouraged not just for academic recognition but for its ability to inform practice, influence policy, and contribute meaningfully to society.
Looking Ahead
As cities continue to face climate uncertainty, transparent and explainable AI models will play a critical role in shaping sustainable futures. Research efforts like this reinforce the importance of combining technological innovation with accountability and clarity.
Through such contributions, JK Business School, Gurugram continues to strengthen its position as a hub for impactful research that connects AI, sustainability, and real-world decision making.
Publication Details
The research paper is published in IEEE Xplore and is available for citation and academic reference
