An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study

Year : 2026 | Volume : 03 | Issue : 01 | Page : 1 10
    By

    Kazi Kutubuddin Sayyad Liyakat,

  1. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

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, KSK approach, decision making, climate change, CO2

[This article belongs to International Journal of Climate Conditions ]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat. An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study. International Journal of Climate Conditions. 2026; 03(01):1-10.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat. An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study. International Journal of Climate Conditions. 2026; 03(01):1-10. Available from: https://journals.stmjournals.com/ijcc/article=2026/view=245277


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Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 21/04/2026
Accepted 30/04/2026
Published 01/05/2026
Publication Time 10 Days
5

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