Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China.

ABSTRACT: Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater l

SUBMITTER: Shi H 

PROVIDER: S-EPMC10485069 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature