International Journal of Climate Conditions Review Article
A Review on AI Driven IoT Based Decision Making in Rainfall Perdition in Climate Change Study
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
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