Predict Saturated Thickness using TensorBoard Visualization

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dc.contributor.authorNguyen, Vinh Theen_US
dc.contributor.authorDang, Tommyen_US
dc.contributor.authorJin, Fangen_US
dc.contributor.editorKarsten Rink and Dirk Zeckzer and Roxana Bujack and Stefan Jänickeen_US
dc.date.accessioned2018-06-02T18:01:51Z
dc.date.available2018-06-02T18:01:51Z
dc.date.issued2018
dc.description.abstractWater plays a critical role in our living and manufacturing activities. The continuously growing exploitation of water over the aquifer poses a risk for over-extraction and pollution, leading to many negative effects on land irrigation. Therefore, predicting aquifer water level accurately is urgently important, which can help us prepare water demands ahead of time. In this study, we employ the Long-Short Term Memory (LSTM) model to predict the saturated thickness of an aquifer in the Southern High Plains Aquifer System in Texas, and exploit TensorBoard as a guide for model configurations. The Root Mean Squared Error of this study shows that the LSTM model can provide a good prediction capability using multiple data sources, and provides a good visualization tool to help us understand and evaluate the model configuration.en_US
dc.description.sectionheadersHydrosphere
dc.description.seriesinformationWorkshop on Visualisation in Environmental Sciences (EnvirVis)
dc.identifier.doi10.2312/envirvis.20181135
dc.identifier.isbn978-3-03868-063-5
dc.identifier.pages35-39
dc.identifier.urihttps://doi.org/10.2312/envirvis.20181135
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/envirvis20181135
dc.publisherThe Eurographics Associationen_US
dc.subjectInformation systems
dc.subjectInformation systems applications
dc.subjectInformation systems
dc.subjectInformation retrieval
dc.subjectInformation systems applications
dc.subjectData mining
dc.titlePredict Saturated Thickness using TensorBoard Visualizationen_US
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