Screening Tool for Dam Hazard Classification Using Machine Learning and Multi-Objective Parameter Tuning

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Affiliated institutions: University of Colorado Boulder

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Category: Analysis

Description: This paper takes a new look at dam hazard potential classification via machine learning algorithms by proposing a novel geospatial model to estimate new predictors. We take a multi-objective approach to the process of machine learning hyperparameter tuning. We show that such an approach gives analysts more insights into the classification problem as well as justification for hyperparameter selection. We demonstrate this framework on dams in Massachusetts, United States.

License: MIT License

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Publication This work is published in the Journal of Water Resources Planning and Management. Link here. Presentation This work was presented at the 2020 International Environmental Modelling and Software Society Conference which was held virtually due to the COVID-19 pandemic. Here is a link to the presentation. Blog Post This blog post walks through a very simple example of a multi-objective ap...

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  • Spatial Features Model


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