Multi-Sensor-Based Water Environment Monitoring System

Effective monitoring of the water environment is critical to ensuring sustainable water resource management and ecological balance, aligning closely with the themes of computational advancements in environmental systems presented by Frontiers in Computer Science.Traditional water monitoring systems often struggle with the complexity of spatiotemporal dynamics, limited sensor coverage, and inadequate anomaly detection, which hinder real-time decision-making and adaptive responses.This paper introduces an innovative Multi-Sensor-Based Water Environment Monitoring System that leverages advanced computational and data-driven approaches to address these challenges.The system integrates a Spatiotemporal Predictive Water Quality Model (SP-WQM) and an Adaptive Monitoring and Remediation Strategy (AMRS).The SP-WQM utilizes hybrid neural networks, combining CNNs for spatial representation and LSTMs for temporal prediction, ensuring accurate modeling of nonlinear and dynamic water quality parameters.Meanwhile, the AMRS enhances the system's practical utility by dynamically allocating sensors, employing real-time anomaly detection, and formulating multiscale response plans.Experimental results demonstrate the model's ability to accurately predict water quality metrics, detect anomalies effectively, and optimize resource allocation under varying environmental scenarios.The proposed system represents a scalable, adaptive, and robust solution for water environment monitoring, contributing significantly to sustainable environmental management practices.