Long Short-Term Memory Based Subsurface Drainage Control for Rainfall-Induced Landslide Prevention
Long Short-Term Memory Based Subsurface Drainage Control for Rainfall-Induced Landslide Prevention
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DOI：
10.3390/geosciences12020064
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发表时间：
2022-01
期刊：
影响因子：
2.7
通讯作者：
Aynaz Biniyaz;Behnam Azmoon;Ye Sun;Zhen Liu
中科院分区：
文献类型：
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作者：
Aynaz Biniyaz;Behnam Azmoon;Ye Sun;Zhen Liu
地下排水系统已被广泛接受，以减轻易受洪水影响地区的滑坡危险。具体而言，人们已认识到，使用带有抽水系统的排水威尔斯井是降低地下水位的一种有效的短期解决办法。然而，由于潜在的高劳动力成本，这种方法尚未被很好地考虑用于长期目的。本研究旨在通过利用传统的岩土工程解决方案和深度学习技术-长短期记忆（LSTM）-来研究用于地下排水的自主抽水系统的想法，以建立用于滑坡预防的岩土网络物理系统。为此目的，一个典型的土壤边坡配备了三个泵被认为是在计算机模拟。48例降雨事件与各种各样的持续时间，总降雨深度，和不同的降雨模式。对于每个降雨事件，使用新提出的Python代码进行瞬态渗流分析，以获得相应的泵的流量数据。将用于将地下水维持在期望水平的抽水策略分配给水泵以生成数据。LSTM将降雨事件数据作为输入，并预测所需的泵流速。使用均方根误差（RMSE）、平均绝对误差（MAE）和R2的评估指标对训练模型的结果进行验证。三台泵的预测流量的R2得分分别为0.958、0.962和0.954，显示出使用经过训练的LSTM模型进行预测的高准确性。这项研究的目的是朝着实现自主泵送系统和降低控制地质系统的运营成本迈出开创性的一步。
Subsurface drainage has been widely accepted to mitigate the hazard of landslides in areas prone to flooding. Specifically, the use of drainage wells with pumping systems has been recognized as an effective short-term solution to lower the groundwater table. However, this method has not been well considered for long-term purposes due to potentially high labor costs. This study aims to investigate the idea of an autonomous pumping system for subsurface drainage by leveraging conventional geotechnical engineering solutions and a deep learning technique—Long-Short Term Memory (LSTM)—to establish a geotechnical cyber-physical system for rainfall-induced landslide prevention. For this purpose, a typical soil slope equipped with three pumps was considered in a computer simulation. Forty-eight cases of rainfall events with a wide range of varieties in duration, total rainfall depths, and different rainfall patterns were generated. For each rainfall event, transient seepage analysis was performed using newly proposed Python code to obtain the corresponding pump’s flow rate data. A policy of water pumping for maintaining groundwater at a desired level was assigned to the pumps to generate the data. The LSTM takes rainfall event data as the input and predicts the required pump’s flow rate. The results from the trained model were validated using evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and R2. The R2-scores of 0.958, 0.962, and 0.954 for the predicted flow rates of the three pumps exhibited high accuracy of the predictions using the trained LSTM model. This study is intended to make a pioneering step toward reaching an autonomous pumping system and lowering the operational costs in controlling geosystems.