Artificial intelligence models for regional environmental monitoring

Abstract

This article examines the development and implementation of intelligent environmental monitoring systems at the regional level, which is essential for ensuring the sustainable de-velopment of territories under intensive anthropogenic pressure. Modern regional ecosystems are subject to complex influences characterized by high non-linearity and stochasticity in pol-lutant dispersion, rendering traditional statistical methods insufficient. Consequently, the re-search focus shifts from mere data recording toward predictive analysis based on processing large heterogeneous datasets using deep learning techniques. 
The central element of the proposed approach is the deployment of a hybrid ConvLSTM architecture, which merges convolutional layers for spatial feature extraction with recurrent blocks for analyzing long-term temporal dependencies, enabling high-precision modeling of physical diffusion and air mass transport. A distinctive feature of this research is the repre-sentation of the regional monitoring network as a graph structure, facilitating the application of Graph Convolutional Networks (GCN) to account for topological connectivity and interac-tions between individual sensors.
To enhance results interpretability and streamline managerial decision-making, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is integrated, transforming complex numeri-cal forecasts into intelligible environmental risk categories. Experimental validation demon-strated superior performance, specifically achieving a 15% reduction in the Mean Absolute Error (MAE) for nitrogen dioxide concentration forecasting. Practical implementation en-ables a 40-50% reduction in decision-making time during emergencies. The scientific novelty lies in the synthesis of a multimodal architecture that combines spatial-temporal dynamics, topological graph analysis, and fuzzy risk assessment within a unified platform.