ARTIFICIAL INTELLIGENCE FOR CLIMATE CHANGE PREDICTION AND ENVIRONMENT MONITORING: ADVANCES, CHALLENGES, AND FUTURE DIRECTIONS

Abstract

Two of the biggest problems that society is facing today are climate change and degradation of natural ecosystems, both of which require monitoring and forecasting beyond the capacity of traditional observational and modelling methods. Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL) techniques, has proven to be a valuable tool to complement physics-based Climate Models and conventional Environmental Monitoring Networks, with better pattern recognition, higher resolution predictions and ability to integrate heterogeneous raw data from satellites, ground-based sensors and citizen contributed platforms. This paper reviews the concepts and methods of using AI in climate change forecasting and environmental surveillance, rather than reporting the results of any one study, but summarizing commonalities found in the literature. It reviews the key families of AI techniques applied in this area, such as supervised and unsupervised learning, deep neural architectures (convolutional and recurrent networks), hybrid physics-informed models and reinforcement learning, and discusses their applications to atmospheric forecasting, extreme weather prediction, air and water quality monitoring, biodiversity and ecosystem assessment, and disaster response. The paper also highlights a set of persistent problems that hinder the trustworthiness and fair use of AI in this area, such as data quality and availability concerns, lack of interpretability for complex black-box models, computational and environmental costs, and the difficulty of obtaining true interdisciplinary collaboration between AI researchers and domain scientists. Based on this synthesis, the paper suggests a framework of methodological integration designed to pave the way for future development of AI-based climate and environmental monitoring systems, organized into the categories of data governance, hybrid model design, explainability and policy translation. The paper ends by suggesting future research directions, in which the utility of AI in this space will be more a function of the trustworthiness, transparency, and inclusiveness of the AI systems being constructed rather than the accuracy of the predictions.