Predict Saturated Thickness using TensorBoard Visualization

Vinh The Nguyen, Tommy Dang, Fang Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Water 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.

Original languageEnglish
Title of host publicationEnvirVis 2018 - Workshop on Visualisation in Environmental Sciences
EditorsDieter Fellner
PublisherThe Eurographics Association
Pages35-39
Number of pages5
ISBN (Electronic)9783038680635
DOIs
StatePublished - 2018
Event6th Workshop on Visualisation in Environmental Sciences, EnvirVis 2018 at EuroVis 2018 - Brno, Czech Republic
Duration: Jun 4 2018 → …

Publication series

NameEnvirVis 2018 - Workshop on Visualisation in Environmental Sciences

Conference

Conference6th Workshop on Visualisation in Environmental Sciences, EnvirVis 2018 at EuroVis 2018
Country/TerritoryCzech Republic
CityBrno
Period06/4/18 → …

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