A Review on AI Driven IoT Based Decision Making in Rainfall Perdition in Climate Change Study

Year : 2026 | Volume : 03 | Issue : 02 | Page : 1 6
By

Heena T Shaikh,

Kazi Kutubuddin Sayyad Liyakat,

  1. Assistant Profesor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India
  2. Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India

Abstract

We are moving from a reactive relationship with the weather to a symbiotic one. This AI-IoT framework is more than just a technological upgrade; it is a vital adaptation strategy in an age of climate volatility. As climate change induces unprecedented volatility in global weather patterns, traditional meteorological forecasting models often struggle to maintain hyper-local accuracy. This paper explores the convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) to create an autonomous, real-time rainfall prediction framework. By deploying a distributed network of low-cost IoT sensors measuring barometric pressure, humidity, dew point, and ambient temperature this system facilitates the continuous streaming of high-fidelity environmental telemetry. We employ Long Short-Term Memory (LSTM) neural network architecture to process these spatio-temporal data streams, effectively capturing non-linear relationships that elude conventional numerical weather prediction (NWP) models. Our findings demonstrate that an AI-driven IoT approach significantly enhances the precision of short-term rainfall forecasting at the micro-geographic level, providing a scalable solution for urban flood management, precision agriculture, and disaster mitigation. The KSK approach is a hybrid machine learning method for forecasting rainfall. It integrates knowledge-based statistical techniques with advanced learning algorithms to improve predictive reliability under diverse climatic conditions. By combining sensor-driven data acquisition, feature optimization, and intelligent forecasting models, the proposed framework enables faster decision-making and supports sustainable climate-resilient planning. The approach also reduces uncertainty in rainfall estimation, offering valuable insights for water resource management, agricultural productivity enhancement, and early warning systems in vulnerable regions.

Keywords: AI driven IoT, KSK approach, rainfall perdition, climate change, global weather patterns.

[This article belongs to International Journal of Climate Conditions ]

How to cite this article: Heena T Shaikh, Kazi Kutubuddin Sayyad Liyakat. A Review on AI Driven IoT Based Decision Making in Rainfall Perdition in Climate Change Study. International Journal of Climate Conditions. 2026; 03(02):1-6.
How to cite this URL: Heena T Shaikh, Kazi Kutubuddin Sayyad Liyakat. A Review on AI Driven IoT Based Decision Making in Rainfall Perdition in Climate Change Study. International Journal of Climate Conditions. 2026; 03(02):1-6. Available from: https://journals.stmjournals.com/ijcc/article=2026/view=260326

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Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 27/06/2026
Accepted 14/07/2026
Published 15/07/2026
Publication Time 18 Days

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