Deep Learning for Climate Research

Project description

Artificial Intelligence (AI) and Machine Learning (ML) can help in climate research with analyzing vast amounts of data, improving climate models, and developing policies for mitigating and adapting to climate change.

AI/ML algorithms help identify patterns in large datasets, that could either be recorded data or generated through large scale climate simulations. This is crucial for understanding complex climate systems and weather patterns. This data can be used by AI to uncover trends and anomalies that might be missed by traditional methods. For example, ML models can predict extreme weather events like hurricanes or heatwaves with greater accuracy by analyzing historical weather patterns and current data. This would be one of the parts for the project.

AI and ML are also used in climate adaptation strategies. They can optimize renewable energy sources, manage water resources, and improve agricultural practices by predicting climate impacts on various sectors. Additionally, AI-driven tools can help in carbon capture and storage by identifying optimal locations and methods for reducing atmospheric carbon dioxide. This would form another part of the project, wherein we’ll develop Reinforcement Learning based methods to devise policies that help reverse the effects of climate change, and help adhere to international agreements such as the Paris agreement, and the UN Sustainable Develop Goals (SDGs).

Buildings account for roughly 40% of the global energy consumption, hence energy-efficient and sustainable infrastructure is paramount towards an economic future of our planet. Existing methodologies involve using sensor data, linear and non-linear models of the building to devise control strategies for efficient cooling/ heating. However, these existing data and models are not exactly representative of the micro-climate within a building, as their accuracy is limited to the neighbourhood of a desired operating condition, and may fail to accurately predict over large range of operating conditions. As a part of this project, we would also (a) develop micro-climate models — motivated by the existing climate models — that replace the existing models used in the area of building energy management, and (b) develop novel learning based control strategies to reduce the energy consumption.

Outcomes

Outcomes and Deliverables

(1) An efficient AI/ML framework to improve existing climate models. This would entail developing a novel climate specific methodology/algorithm, which is rooted in the existing theory of deep learning.
(2) Improvement over the current state-of-the-art, data-based solutions to problems in climate research.
(3) Devising Reinforcement Learning based control policies to help reverse the effects of climate change.
(4) High quality research papers in leading AI/ML/Climate Science journals and conferences.
(5) Contribution to open-source algorithms in the area of Climate AI.
(5) Benchmarking against state-of-the-art solutions.

Essential capabilities

Mathematical optimization, Machine Learning, Programming, Reinforcement Learning

Desireable capabilities

Fundamentals of climate science

Expected qualifications (Course/Degrees etc.)

BTech (or equivalent) in relevant engineering stream, or MSc in Maths/Computer Science/Statistics or MTech in computer science, electrical engineering, mechanical engineering, and other relevant streams, or Masters in Climate Science