Multifaceted anomaly detection framework for leachate monitoring in landfills.

ABSTRACT

The imperative to preserve environmental resources has transcended traditional conservation efforts, becoming a crucial element for sustaining life. Our deep interconnectedness with the natural environment, which directly impacts our well-being, emphasizes this urgency. Contaminants such as leachate from landfills are increasingly threatening groundwater, a vital resource that provides drinking water for nearly half of the global population. This critical environmental threat requires advanced detection and monitoring solutions to effectively safeguard our groundwater resources. To address this pressing need, we introduce the Multifaceted Anomaly Detection Framework (MADF), which integrates Electrical Resistivity Tomography (ERT) with advanced machine learning models-Isolation Forest (IF), One-Class Support Vector Machines (OC-SVM), and Local Outlier Factor (LOF). MADF processes and analyzes ERT data, employing these hybrid machine learning models to identify and quantify anomaly signals accurately via the majority vote strategy. Applied to the Chaling landfill site in Zhuzhou, China, MADF demonstrated significant improvements in detection capability. The framework enhanced the precision of anomaly detection, evidenced by higher Youden Index values (≈ 6.216%), with a 30% increase in sensitivity and a 25% reduction in false positives compared to traditional ERT inversion methods. Indeed, these enhancements are crucial for effective environmental monitoring, where the cost of missing a leak could be catastrophic, and for reducing unnecessary interventions that can be resource-intensive. These results underscore MADF's potential as a robust tool for proactive environmental management, offering a scalable and adaptable solution for comprehensive landfill monitoring and pollution prevention across varied environmental settings.