Methods and Algorithms for Decision-Making in Agro-Industrial Environmental Management

Abstract

This paper presents a novel methodology for comprehensive assessment and decision-making in managing the ecological state of agro-industrial territories. The study introduces an intelligent situational modeling approach that integrates fuzzy logic, GIS-based analysis, and IoT-driven environmental monitoring to evaluate both current and forecasted conditions. The proposed cyber-physical system utilizes real-time sensor networks and UAV-based hyperspectral imaging to collect, process, and analyze environmental parameters, including soil pollution index (SPI), air quality index (AQI), and vegetation health index (VHI). Field experiments conducted in the Belgorod Region, Russia, on 50-hectare test sites demonstrated a 27% improvement in forecasting accuracy compared to conventional methods. Key findings reveal that implementing optimized land-use scenarios resulted in: 19% reduction in pollutant accumulation in soil, 27% increase in agricultural productivity, 25% decrease in public health risks. The proposed framework facilitates adaptive management by providing science-based recommendations for establishing protective forest strips, reducing pollutant exposure, and optimizing land-use planning. The findings confirm the necessity of integrating intelligent environmental monitoring into territorial management systems to enhance sustainable agro-industrial development and mitigate ecological risks.