ERI Seminar Series: "Leveraging AI for time series forecasting in forestry and flood prediction"

Abstract:

In plantation forests, where returns take more than a decade to materialize, proactive planning is crucial. Current technologies can often provide real-time spatio-temporal data, which require appropriate methods to successfully extract knowledge and use these large and complex databases. In this talk Guilherme will discuss the utilization of Machine Learning (ML) tools to optimize resource allocation with forestry hydrology data and the exciting opportunities for future research, particularly related to the analysis of intricate edaphoclimatic data collected throughout the forests.

Predicting when the next flood will happen is no longer a dream. Flood forecasting has been around for decades, but now artificial intelligence can really improve prediction. In this talk we will discuss some of the neural network advancements and the methods employed to improve the flood prediction horizon and the reliability of the forecasts.

Biographies:

Guilherme Weigert is a post-doctoral research fellow at the University of Waikato. He received his PhD degree from Federal University of São Carlos, Brazil, in 2021. His research focuses on machine learning for Evolving Data Streams and Distributed Systems.

Nick Lim is a post-doctoral research fellow doing research in machine learning and artificial intelligence. Nick obtained his MSc (Mathematics) and his PhD (Statistical Learning) from the University of Waikato and his doctoral dissertation is on “Ensemble Learning of High Dimensional Datasets”. Prior to doing his graduate studies, Nick worked in a semiconductors MNC as a component design engineer. Nick is passionate about technology, education, social justice and the welfare of children. In his free time, Nick enjoys photography, music, paper craft, cooking, and the mountains.