Machine Learning Techniques for Environmental Performance Forecasting and Reporting

Abstract

This paper introduces a novel, hybrid machine learning framework for forecasting and
reporting 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 Colony
Optimization (ACO), with ensemble learning techniques to predict complex, non-linear environmental indicators such as watershed health, urban air quality indices, and industrial
carbon sequestration potential. Our approach diverges from conventional applications by
treating environmental systems as dynamic, adaptive networks, where data points (e.g.,
sensor readings, satellite imagery derivatives) are conceptualized as nodes in a graph. The
ACO metaheuristic is employed not for pathfinding, but for intelligent, iterative feature
selection and weighting across temporal and spatial dimensions, optimizing the input space
for a subsequent ensemble of regression models including Support Vector Regressors and
Regression Trees. This two-stage process—bio-inspired feature space optimization followed
by ensemble prediction—represents a significant methodological novelty. We validate our
framework using a multi-source dataset comprising 15 years of historical environmental data
from North American and European monitoring networks. Results demonstrate a mean absolute percentage error (MAPE) improvement of 18.7