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	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/972/LSTMs-for-Inferring-Planetary-Boundary-Layer-Height-PBLH-">
		<rdfs:label><![CDATA[LSTMs for Inferring Planetary Boundary Layer Height (PBLH)]]></rdfs:label>
		<pub:title><![CDATA[LSTMs for Inferring Planetary Boundary Layer Height (PBLH)]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2021-03-22T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[In this paper, we describe new work which is part of a larger study to understand how machine learning could be used to augment existing methods for calculating and estimating the Planetary Boundary Layer Height (PBLH). We describe how a Long Short-Term Memory (LSTM) Network could be used to learn PBLH changes over time for different geographical locations across the United States, used in conjunction with the WRF-Chem model. If the machine learning method could achieve accuracy levels similar to the model-based calculations, then it is feasible for the deep learning model to be used as an embedded method for the WRF-Chem model. The paper shows promising results that warrant more exploration. We describe results for two experiments in particular.  The first experiment used 20 geographical locations for a two-month period of hourly WRF-Chem calculated PBLH.  In this experiment, we evaluated how well the LSTM could learn PBLH by using limited data across a set of nearby locations.  This model achieved RMSE of .11 on predicted PBLH. The second experiment used one year of hourly PBLH calculations from the WRF-Chem model to evaluate the LSTM prediction for a selection of three locations with separate LSTM models, achieving RMSE scores of 0.04, 0.01 and 0.05, respectively. We describe these results and the future plans for this work.]]></pub:abstract>
		<pub:organization><![CDATA[AAAI-MLPS 2021]]></pub:organization>
		<pub:counter>874</pub:counter>
		<pub:booktitle><![CDATA[AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences]]></pub:booktitle>
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													<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Zhifeng/Yang"><person:name><![CDATA[Zhifeng Yang]]></person:name><rdfs:label><![CDATA[Zhifeng Yang]]></rdfs:label></person:Person>
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