AI-Based Analysis of Microbial Communities for Climate Impact Prediction

Abstract

Climate change is one of the most pressing global challenges of the 21st century, influencing ecosystems, biodiversity, and human societies. Microbial communities play a central yet often underappreciated role in regulating Earth’s climate through their involvement in biogeochemical cycles, including carbon sequestration, nitrogen fixation, and greenhouse gas emissions. Due to their rapid response to environmental changes, microbial ecosystems serve as early indicators of climatic perturbations. However, the intrinsic complexity, diversity, and high dimensionality of microbial datasets pose significant challenges for conventional analytical approaches. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning techniques, have demonstrated exceptional potential in modelling non-linear, high-dimensional biological systems. This paper presents a comprehensive AI-based framework for analysing microbial community data to predict climate impacts. By integrating metagenomic sequencing data with environmental variables, the proposed approach leverages unsupervised learning for microbial pattern discovery, supervised deep learning models for climate-variable prediction, and explainable AI techniques to enhance interpretability. The study highlights how AI-driven microbial analysis can significantly improve prediction accuracy of climate-related parameters such as soil carbon flux, methane emissions, and ecosystem resilience under climate stress. Results indicate that AI models outperform traditional statistical techniques and provide meaningful ecological insights. This research establishes a robust interdisciplinary framework that bridges microbiology, climate science, and artificial intelligence, contributing to improved climate forecasting, environmental monitoring, and sustainable policy formulation.