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Project Code [2022-CE-1093]
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Project title
Machine-learning-techniques towards understanding and predicting physical drivers and impact of climate-change at catchment-scale in Ireland
Primary Funding Agency
Irish Research Council
Co-Funding Organisation(s)
Environmental Protection Agency
Lead Organisation
National University of Ireland Galway (NUIG)
Lead Applicant
Ciara Wall
Project Abstract
If we want to better understand how rivers work, and especially how climate change and human intervention will impact them, then they must be studied in the context of the catchment. in addition to the scientific perspective, this is a
regulatory requirement under the EU Water Framework Directive. We need to collect data from rivers on an ongoing basis, and deploying instrumentation builds up the data base and helps us work out the hydrodynamics of the system, especially in smaller catchments. The ability to predict and manage these catchments is only possible upon studying a selection of test catchments and then applying suitable hydrological models to similar catchment types around the country.
The quantity of data that will be obtained (minimum of 24 months) will be sufficient for the application of Machine Learning (ML). ML is a method of data analysis that programs analytical model building. It is a branch of artificial intelligence based on the idea that computer systems can learn from data, identify patterns and make decisions with minimal human involvement.
ML techniques will help us to manage and understand the data that we collect from the catchments. ML will be used in the prediction of the physical drivers and impact of climate change.
In this study I want to collect new data, and make sense of existing data, from three rural river settings in the west of Ireland:
1. The Bundorragha River (Co. Mayo)
2. Newport River* (Co. Mayo)
3. River Erriff (Co. Mayo)
*Potentially link in with the Marine Institute for this catchment study. I may be able to obtain data to use to study further
back and continue to study the catchment upstream of their current study. This potential extended dataset could provide
even more enhanced Machine Learning predictions for this catchment.
Grant Approved
�110,000.00
Research Hub
Climate Change
Initial Projected Completion Date
31/08/2026