Dear Colleagues,
We would like to bring to your attention our proposed session:
H070 - Machine learning applications in catchment hydrology
Abstract:
Machine learning methods have shown tremendous potential in both process-understanding and prediction – two of the main focus areas in hydrology. New datasets with high spatial and temporal resolutions are emerging at an unprecedented rate, which has opened up various new avenues of research in the field. Machine learning algorithms are well suited to leverage these new datasets and solve hydrologic problems across a range of spatial and temporal scales. We hereby invite studies that use machine learning to solve problems of catchment hydrology, considering either the quantity or quality or both aspects of water. The studies with generalizable results are strongly encouraged.
Conveners:
Tirthankar Roy (University of Nebraska-Lincoln)
Antonio Meira-Neto (University of Arizona)
Paulo Tarso S. Oliveira (Federal University of Mato Grosso do Sul)
Peter Troch (University of Arizona)
Link to the session:
Consider submitting an abstract to our session! Note that the abstract submission deadline is August 4.
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Tirthankar Roy
Assistant Professor
Civil and Environmental Engineering
University of Nebraska-Lincoln, USA
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