Hybrid Neural Network Architecture for Real-Time Groundwater Monitoring Using Multi-Sensor Systems

Abstract

Groundwater quality monitoring is a critical component of environmental sustainability and public health, necessitating advanced systems that can provide accurate and continuous assessments. Existing methods, while effective for periodic sampling, often fail to meet the demands for real-time, large-scale data analysis, leaving gaps in understanding dynamic environmental changes. This study aims to develop a hybrid intelligent system that integrates multi-sensor data collection and advanced neural network architectures. The system is designed to enhance the real-time monitoring of groundwater quality, detect anomalies, and provide actionable insights through spatial-temporal mapping. The proposed system employs a combination of Long Short-Term
Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and a multi-head attention mechanism to analyze temporal and spatial data. Data preprocessing techniques such as outlier detection, interpolation, and normalization ensure robustness against noise and missing values. The system
supports three learning modes: static supervision (offline training), online learning (operator-in-the-loop), and active learning (querying uncertain examples). Experimental evaluations were conducted using datasets collected from multiple monitoring wells over a six-month period. The hybrid neural network architecture achieved significant improvements in performance compared to standalone LSTM and CNN models. Metrics such as RMSE (0.10), precision (88.5%), and recall (90.2%) underscore the system’s ability to deliver accurate and reliable groundwater quality predictions. Visual outputs, including water quality maps and anomaly detection reports, demonstrate the system's capacity to identify critical trends and regions of concern effectively. The study highlights the efficacy of integrating advanced machine learning techniques with multi-sensor systems for real-time groundwater monitoring. The hybrid system’s scalability, adaptability, and robustness position it as a promising tool for environmental management. Future work will focus on incorporating additional data sources, such as meteorological and geological information, and enhancing the system’s online learning capabilities to further improve performance in dynamic and diverse environments.