Evolutionary Neural Network-Based Online Ecological Governance Monitoring of Industrial Water Pollution

Introduction

Industrial water pollution has emerged as one of our most pressing environmental challenges, presenting complex threats to aquatic ecosystems and human populations worldwide (Boguniewicz-Zablocka et al., 2022; Li et al., 2022; Nie et al., 2024). The intricate nature of modern industrial manufacturing processes introduces a diverse array of pollutants into water systems, each with unique chemical properties, dispersion patterns, and environmental impacts (Chereji & Munteanu, 2024; Inostroza et al., 2023). Traditional water quality monitoring approaches, which rely primarily on periodic sampling and simple threshold-based warning systems, have proven inadequate for addressing these multifaceted challenges. These conventional methods often fail to capture the dynamic interactions between different pollutants and their evolving dispersion patterns in aquatic environments (Yoon et al., 2023). As a result, current monitoring systems struggle to provide timely and accurate predictions of potential ecological impacts, leading to significant delays in detecting and responding to pollution events (Viciano-Tudela et al., 2023). This reactive rather than proactive approach hampers the effectiveness of environmental protection efforts and results in inefficient allocation of limited resources, making it increasingly difficult to prevent and mitigate the adverse effects of industrial water pollution on ecosystems and public health (Shao et al., 2022; Wojcik et al., 2023).

The rapid advancement and widespread deployment of Internet of Things technologies, coupled with sophisticated sensor networks, has revolutionized the field of industrial water pollution monitoring, enabling unprecedented continuous surveillance of water quality parameters at remarkably high temporal and spatial resolutions (Cruz-Mata et al., 2024; Lu, 2024; Prabowo et al., 2022). These advanced monitoring systems now generate vast quantities of complex, multivariate time series data encompassing numerous pollution indicators, from fundamental parameters like chemical oxygen demand (COD) and pH levels to detailed measurements of heavy metal concentrations and diverse organic compounds (Kenchannavar et al., 2022; Pramono et al., 2024). However, this wealth of high-dimensional data presents its own set of challenges for environmental scientists and policymakers. The inherent complexity stems from the intricate spatiotemporal relationships between various pollutants, further complicated by ever-changing environmental conditions such as temperature, rainfall, and water flow patterns. While traditional statistical analyses and basic machine learning methods have been employed to process this data, they often fail to uncover the subtle, interconnected patterns that characterize real-world pollution dynamics, making it difficult to derive meaningful, actionable insights for effective ecological governance and environmental protection strategies.