An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study - Kazi Kutubuddin Sayyad Liyakat (International Journal of Climate Conditions, 2026)
As the Earth’s climate enters a state of unprecedented volatility, the traditional methods of ecological observation—characterized by delayed reporting and fragmented data—are no longer sufficient. This study investigates the paradigm shift toward AI-driven IoT (KSK Approach)-based decision-making frameworks as the primary frontier in climate science. By deploying a "planetary nervous system" of interconnected sensors—measuring everything from soil moisture in the Sahel to glacial melt rates in the Arctic—we generate a high-fidelity, real-time stream of environmental data. However, the raw data is a cacophony; it requires Artificial Intelligence to act as the conductor. This research explores how machine learning algorithms identify non-linear patterns within massive datasets, enabling prognostic intelligence rather than mere diagnostic observation. We examine the transition from "passive monitoring" to "active intervention," where AIIoT ecosystems facilitate autonomous decision-making in smart agricultural grids, urban heat-island mitigation, and carbon sequestration optimization. The integration of edge computing allows for localized, split-second responses to climate events, while cloud-based deep learning models provide long-term policy simulations. Ultimately, this paper discusses that the fusion of these technologies transforms climate change study from a record of loss into a roadmap for resilience, providing humanity with a digital scaffolding to support a sustainable biosphere. The KSK approach for AI-Driven IoT (AIIoT) in climate studies aims to improve environmental monitoring and decision-making accuracy. It utilizes drone-based IoT sensors KSK (Kutubuddin S Kazi) Approach to monitor temperature and CO2 levels, employing machine learning (ANN, K-NN, Decision Trees) to compare datasets for predictive, data-driven climate action.
Keywords: AI driven IoT; CO2; Climate change; Decision Making; KSK approach