Use of Mathematical and Statistical Approaches with AI for Predicting Climate Change Effects on Ecosystems: A Journey of Operational Research

Abstract

Climate change is one of the most challenging factors for ecosystems around the world, causing disruption in biodiversity and ecological balance due to rising temperatures, shifts in precipitation, and extreme weather conditions. The ability to predict such impacts is considered crucial for effective conservation and policy strategies. The current study proposes an integrated framework by mathematically modeling, using statistical techniques, and enhancing with AI for improved accuracy and scalability in predictions of climate impacts. The mathematical models would include Energy Balance and Lotka-Volterra equations, giving mechanistic insights into climate dynamics and species interactions, respectively. On the other hand, statistical approaches embrace regression analysis and modeling of time series, hence feeding these models with critical trends disclosed. Represented by techniques like machine learning or deep learning, AI analyzes high-dimensional complex datasets to capture non-linear relationships that result in improved predictive performance. Application to real-world climate and ecological data with the hybrid approach demonstrates its power in predicting the rate of warming, species extinction risks, and ecosystem responses to different climate scenarios. Results showed that performance improvement over the methods in a standalone manner was evident. The proposed framework justifies data-driven decision-making and presents actionable insights for policymakers, conservationists, and researchers in terms of sustainable climate adaptation strategies.