Machine Learning Techniques for Time Series Regression in Unmonitored Environmental Systems

Abstract

This thesis provides a computer science audience with a review of machine learningtechniques for modeling time series in unmonitored environmental systems with no available target data that have been published in recent years, and further includes three distinct research efforts applying these methods to real-world water resources prediction scenarios. Additionally, we identify several open questions for time series prediction in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding, and explainable AI techniques in modern machine learning frameworks. This is motivated by the current state of environmental time series modeling seeing a vast increase in applications of various machine learning models, in particular deep learning models built using the growing availability of high performance computing resources. It remains difficult to predict environmental variables for which observations are concentrated in a minority of locations and most locations remain unmonitored, and although many machine learning-based approaches have been developed, there is often a lack of comparison between them. The increased attention to environmental prediction topics such as disaster response, water resources management, and climate change reveal a need to compare these approaches, and understand when and where they should be applied in unmonitored environmental prediction scenarios.