Tackling climate change problems with machine learning
Channel: Rohitash Chandra
Date: 12/07/2017

Seminar at UniSyd EarthByte Group

Introduction by Prof. Dietmar Mullar

Machine learning methods such as neural networks and deep learning are increasingly becoming popular with innovations in social media, e-commerce, and autonomous driving systems. This motivates for its application for climate change-related problems that involve time series data and models that try to model biological or geophysical processes, for instance, predicting the trend in the growth of corals and landscape dynamics. This seminar will introduce basics of machine learning and neural networks to ecologists and geoscientists. It will begin with some recent applications of neural networks for cyclone track and wind intensity prediction. It will also present uncertainty quantification in neural networks via Bayesian learning for tackling climate change problems. Moreover, it will provide a roadmap for using ensemble, multi-task and transfer learning methodologies via neural networks for cyclone prediction and related applications that deal with multiple sources of data. The prospects of machine learning and Bayesian optimization in Basin and Landscape Dynamics (Badlands) and PyReef software that predicts reef evolution will also be discussed.

Download presentation: https://github.com/rohitash-chandra/research

https://www.researchgate.net/profile/Rohitash_Chandra