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PCA-based multivariate LSTM model for predicting natural groundwater level variations in a time-series record affected by anthropogenic factors
Environmental Earth Sciences ( IF 2.8 ) Pub Date : 2021-09-21 , DOI: 10.1007/s12665-021-09957-0
Gyoo-Bum Kim 1 , Chan-Ik Hwang 2 , Myoung-Rak Choi 3
Affiliation  

Time series of natural groundwater level considering rainfall effects are usually used for the estimation of groundwater recharge, long-term trend analysis, and assessment of interactions between surface water and groundwater along rivers. However, anthropogenic activities, such as groundwater pumping, land excavation, and barrier construction, may induce abnormal changes in water levels. This study aimed to develop a universal long short-term memory (LSTM) model for predicting natural water level variations in a time-series record that has been affected by groundwater abstraction or other anthropogenic factors. This model uses past and present groundwater levels, rainfall and representative principal components of groundwater level time series as input variables. For this purpose, 17 cases of the developed LSTM model were tested using 13 monitoring wells, of which the case with the highest prediction performance was selected. Among the test cases, case 6 was found to achieve the highest performance, with average RMSE and MAE values of 0.061 and 0.027, respectively. The case 6 model used rainfall, groundwater level of monitoring wells, and four main principal components (1–4) as input variables. Also, its optimum window size was found to be 5. The accuracy of the LSTM model was found to be more strongly affected by window size than by input variables. Although the case 6 LSTM model may have errors for some monitoring wells, it has high potential as a universal model that can consistently determine natural groundwater levels in South Korea. This LSTM model makes it universally available for water level prediction, even with long missing periods in groundwater level time series or in the absence of adjacent observations for model input.



中文翻译:

基于 PCA 的多元 LSTM 模型,用于预测受人为因素影响的时间序列记录中的自然地下水位变化

考虑降雨效应的自然地下水位时间序列通常用于地下水补给估算、长期趋势分析以及沿江地表水与地下水相互作用的评估。然而,人为活动,如地下水抽取、土地开挖和屏障建设,可能会引起水位的异常变化。本研究旨在开发一种通用的长短期记忆 (LSTM) 模型,用于预测受地下水抽取或其他人​​为因素影响的时间序列记录中的自然水位变化。该模型使用过去和现在的地下水位、降雨量和地下水位时间序列的代表性主成分作为输入变量。以此目的,使用13口监测井对开发的LSTM模型的17个案例进行了测试,其中选择了预测性能最高的案例。在测试用例中,发现用例 6 实现了最高性能,平均 RMSE 和 MAE 值分别为 0.061 和 0.027。案例 6 模型使用降雨量、监测井地下水位和四个主要主成分 (1-4) 作为输入变量。此外,发现其最佳窗口大小为 5。发现 LSTM 模型的准确性受窗口大小的影响比受输入变量的影响更大。尽管案例 6 LSTM 模型对某些监测井可能存在误差,但它作为通用模型具有很高的潜力,可以一致地确定韩国的天然地下水位。这个 LSTM 模型使其普遍可用于水位预测,

更新日期:2021-09-22
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