Predict Saturated Thickness using TensorBoard Visualization
Predict Saturated Thickness using TensorBoard Visualization
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DOI：
10.2312/envirvis.20181135
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
V. Nguyen;Tommy Dang;Fang Jin
中科院分区：
文献类型：
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作者：
V. Nguyen;Tommy Dang;Fang Jin
水在我们的生活和生产活动中发挥着至关重要的作用。含水层水的不断增长的开采带来了过度开采和污染的风险，导致对土地灌溉产生许多负面影响。因此，准确预测含水层水位迫在眉睫，这可以帮助我们提前准备用水需求。在本研究中，我们采用长短期记忆 (LSTM) 模型来预测德克萨斯州南部高平原含水层系统中含水层的饱和厚度，并利用 TensorBoard 作为模型配置的指南。本研究的均方根误差表明LSTM模型可以利用多个数据源提供良好的预测能力，并提供良好的可视化工具来帮助我们理解和评估模型配置。
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.