Research Projects

Climate change and rapid urbanization pose significant threats to urban environments, leading to unpredictable patterns and extreme events. Researchers aim to develop advanced artificial intelligence AI models for spatio-temporal data analysis and prediction to contribute to the United Nations Sustainable Development Goal 11 Sustainable Cities and Communities. The project aims to develop a versatile and generic approach for estimating and predicting spatio-temporal quantiles in climate data, combining nonparametric methods with physics-informed machine learning techniques. The methodology will quantify and account for uncertainties in data and models, providing reliable prediction intervals for estimated quantiles. The methodology will demonstrate applicability to real-world data, aiding policymakers and urban planners in making data-driven decisions for sustainable development and climate change adaptation. The research will begin with preprocessing and analyzing historical spatio-temporal rainfall data, focusing on nonparametric quantile regression techniques and deep learning-based methods. The proposed method will be adapted and applicable across various domains, enabling stakeholders to monitor spatio-temporal patterns and explore what-if scenarios to mitigate climate change and urbanization impacts.