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

This paper proposes ENNOEIGS, an evolutionary neural network-based online ecological industrial governance system that integrates advanced neural architectures with evolutionary optimization for robust pollution monitoring. The framework combines convolutional neural networks for dimensional reduction of sensor data, external attention mechanisms for discovering pollution pattern correlations, and convolutional long short-term memory networks for modeling the spatiotemporal evolution of contaminants. A genetic algorithm continuously optimizes the neural network parameters, enabling adaptation to changing industrial conditions. Experimental validation using industrial wastewater monitoring data demonstrates ENNOEIGS's superior performance, achieving a 94.8% anomaly detection rate with 2.3% false alarms, outperforming existing approaches. The framework reduces the mean modified absolute error to 0.028 mg/L while maintaining faster convergence during training.