Advances in Long-term Water Quality Monitoring through Data Fusion

This project aims to develop a machine learning (ML) model that fuses satellite imagery and in-situ data for water monitoring and analysis for the benefit of water and environmental management. The result of this study will enable national monitoring of water bodies by using data fusion from satellite images and in-situ data; thus, limiting the risk of bush fires damaging ground-based water monitoring infrastructure.

This research aligns well with the AquaWatch Demonstrator as it develops artificial intelligence (AI) algorithm that will provide accurate forecasting of water degradation in Australia. The AI will map major environmental events such as algae blooms, bushfires, and other meteorological events to the degradation of Australian reservoirs, waterways and coastal environments. The proposed models will combine space and ground segments into an integrated metric for both near real-time decision-making as well as long term analysis and future forecasting.


Project Leader:
Professor Wei Xiang, La Trobe University

PhD Student:
Trung (Alex) Nguyen, La Trobe University