Predicting from the past - identifying characteristics of invasion-resistant and invasion-prone waterbodies to aid horizon scanning
Description
Machine learning and statistical modeling will be used to leverage region-wide waterbody invasion histories and datasets on the physical, biological, chemical, anthropogenic, and geographic characteristics of these waterbodies to: a) identify those variables that increase or decrease invasion risk, b) categorize all waterbodies in the region based on their invasion risk, and c) provide a decision support tools for managers and policy makers to identify at-risk sites.
Detailed example
Waterbodies vulnerable to invasion
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
AI/ML will provide a decision support tool for managers and policy makers to identify waterbodies vulnerable to invasion by non-native fishes and potential actions that could be implemented to reduce risk of invasion.
Controls / human review
ATO: No; PIA: Not published