Leveraging Remote Sensing to Detect, Monitor and Predict Water Quality & Stormwater Events: An AI-Driven Approach

EMC Tahi Abstract for the Stormwater Conference 2024

Stormwater Innovation Presentation and Award

Leveraging Remote Sensing to Detect, Monitor and Predict Water Quality & Stormwater Events: An AI-Driven Approach

Muhammad A Khan, Brendan Bell, Ander Castelltort Schnaas

Effectively monitoring changes in freshwater systems and associated assets is pivotal in tackling stormwater issues. HydroSEER leverages high-resolution satellite imagery and time series analytics to generate comprehensive AI-driven insights on the health and status of freshwater systems.

This innovative approach enables near real-time detection of non-compliance or water pollution events. HydroSEER provides accessible data and insights for in-depth exploration, analysis, and understanding of interconnected stormwater components.

By predicting changes in freshwater systems, this comprehensive solution enables a proactive approach to mitigate the effects of such events and remediate damage, ultimately contributing to the sustainability of freshwater systems. Currently, efforts are being made to tackle contemporary challenges such as predicting flood events and the formation of algal blooms.

Data centralisation further enhances understanding by allowing for temporal and spatial trend extraction, aiding in pinpointing areas of concern. The integration of remote sensing, machine learning, and GIS empowers stormwater professionals to make informed, evidence-based decisions, thereby overcoming data insufficiency barriers and fostering improved management practices.