Atmosphere & Environment Sciences, Earth
Title : | Hybrid-AI Quantile Regression Combining Nonparametric Statistical Methods and Physics-Informed Neural Networks for Analysing Spatio-temporal Climate Data |
Area of research : | Atmosphere & Environment Sciences, Earth |
Focus area : | Climate Change, Digital technologies |
Principal Investigator : | Prof. Soudeep Deb, Indian Institute Of Management, Bangalore, Karnataka (560076) |
Timeline Start Year : | 2024 |
Timeline End Year : | 2026 |
Contact info : | soudeep@iimb.ac.in |
Details
Executive Summary : | 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. |
Total Budget (INR): | 22,38,766 |
Organizations involved