Difference between revisions of "2018 CSDMS meeting-090"

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{{CSDMS meeting abstract title temp2018
{{CSDMS meeting abstract title temp2018
|CSDMS meeting abstract title=Physics-informed machine learning approach for predicting post-fire debris flows based on physical thresholds
|CSDMS meeting abstract title=Physics-informed machine learning approach for predicting post-fire debris flows based on thresholds
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{{CSDMS meeting abstract template 2018
{{CSDMS meeting abstract template 2018

Revision as of 20:47, 5 May 2018





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Physics-informed machine learning approach for predicting post-fire debris flows based on thresholds

Hui Tang, University of Arizona Tucson Arizona, United States. huitang@email.arizona.edu


Post-fire debris flows are a common hazard in the western United States. However, after decades of efforts in the debris flow research community, universally applicable post-fire debris flow predict methods are still lacking. Large discrepancies in the post-fire debris flow initiation mechanism are the main source that limits the predictive accuracy of debris flow. Improve and understanding these discrepancies is significant to possibly improve the debris flow modeling. In this work, we propose a data-driven, physics-informed machine learning approach for reconstructing and predicting debris flows. By using a classic supervising modern learning technique based on logistics regression, the logistics regression functions are trained by existing direct field measurements and debris flow numerical simulations from Las Lomas after 2016 Fish fire and then used to predict debris flow in different drainage basin where data are not available. The proposed method is evaluated by two classes of simulations: sediment transport model and runoff model. In runoff simulations, five drainage basins are considered: Las Lomas, Arroyo Seco, Dunsmore 1, Dunsmore 2, Big Tujunga. In sediment transport model, Las Lomas and Arroyo Seco watersheds are applied. Excellent predictive performances were observed in both scenarios, demonstrating the capabilities of the proposed method.