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dc.contributor.authorAli, Zeenat
dc.contributor.authorZiaei, Dorsa
dc.contributor.authorSleeman, Jennifer
dc.contributor.authorYang, Zhifeng
dc.contributor.authorHalem, Milton
dc.date.accessioned2021-04-07T17:58:16Z
dc.date.available2021-04-07T17:58:16Z
dc.date.issued2021-03-22
dc.descriptionCombining Artificial Intelligence and Machine Learning with Physical Sciences, AAAI-MLPS 2021en_US
dc.description.abstractIn 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.en_US
dc.description.sponsorshipThis work has been funded by the following grants: NASA grant NNH16ZDA001-AIST16-0091 and NSF CARTA grant 17747724.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/972/LSTMs-for-Inferring-Planetary-Boundary-Layer-Height-PBLH-en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proccedings preprintsen_US
dc.identifierdoi:10.13016/m2md2b-sbnc
dc.identifier.citationZeenat Ali, Dorsa Ziaei, Jennifer Sleeman, Zhifeng Yang, and Milton Halem, LSTMs for Inferring Planetary Boundary Layer Height (PBLH),https://ebiquity.umbc.edu/paper/html/id/972/LSTMs-for-Inferring-Planetary-Boundary-Layer-Height-PBLH-en_US
dc.identifier.urihttp://hdl.handle.net/11603/21293
dc.language.isoen_USen_US
dc.publisherAAAIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Physics Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleLSTMs for Inferring Planetary Boundary Layer Height (PBLH)en_US
dc.typeTexten_US


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