Abstract：
Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data\'s abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.
摘要：
地下水监测数据可能容易出现错误和偏差，由于各种因素，如钻孔和设备故障，或人为错误。这些不准确会危及地下水系统，导致效率降低，并可能导致监控系统的部分或完全故障。传统的异常检测方法,依赖于统计和时变技术，努力处理异常的复杂性和动态性。随着人工智能的进步以及不同部门对有效异常检测和预防的需求日益增长，人工神经网络方法正在出现，能够通过考虑时间和上下文方面来识别更复杂的异常。尽管如此，对地下水异常检测的综合研究仍然不足。来自地下水的顺序数据的复杂模式提出了许多挑战，需要结合数学的复杂建模技术，统计数据，和机器学习的可行解决方案。本文介绍了一种用于通过概率方法检测地下水监测数据异常的高精度和高效计算的模型。我们采用蒙特卡罗方法和SEAWAT数值模拟来确定地下水盐度的不确定性。随后，训练和评估了长短期记忆(LSTM)-自动编码器模型，形成异常检测框架的基础。通过LSTM-自动编码器使用负对数似然(NLL)评分和预定阈值来评估每条训练数据,以确定数据的异常百分比。对所提出的LSTM-Autoencoder算法的精度评估表明，该方法取得了良好的性能，异常检测准确率为98.47%。
