Machine Learning Techniques for Environmental Performance Forecasting and Reporting (gjstudies, 2026)
This paper introduces a novel, hybrid machine learning framework for forecasting andreporting environmental performance metrics, a domain traditionally dominated by deterministic models and manual reporting processes. We propose a methodology that synergistically combines bio-inspired optimization algorithms, specifically a modified Ant ColonyOptimization (ACO), with ensemble learning techniques to predict complex, non-linear environmental indicators such as watershed health, urban air quality indices, and industrialcarbon sequestration potential. Our approach diverges from conventional applications bytreating environmental systems as dynamic, adaptive networks, where data points (e.g.,sensor readings, satellite imagery derivatives) are conceptualized as nodes in a graph. TheACO metaheuristic is employed not for pathfinding, but for intelligent, iterative featureselection and weighting across temporal and spatial dimensions, optimizing the input spacefor a subsequent ensemble of regression models including Support Vector Regressors andRegression Trees. This two-stage process—bio-inspired feature space optimization followedby ensemble prediction—represents a significant methodological novelty. We validate ourframework using a multi-source dataset comprising 15 years of historical environmental datafrom North American and European monitoring networks. Results demonstrate a mean absolute percentage error (MAPE) improvement of 18.7