Nguyen, Vinh TheDang, TommyJin, FangKarsten Rink and Dirk Zeckzer and Roxana Bujack and Stefan Jänicke2018-06-022018-06-022018978-3-03868-063-5https://doi.org/10.2312/envirvis.20181135https://diglib.eg.org:443/handle/10.2312/envirvis20181135Water 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.Information systemsInformation systems applicationsInformation systemsInformation retrievalInformation systems applicationsData mining10.2312/envirvis.2018113535-39