BLOCKCHAIN-ENABLED CNN FRAMEWORK FOR ENHANCED STORM AND FLOOD PREDICTION BASED ON ENHANCED GIRD CELLULAR AUTOMATA ALGORITHM TEMPORAL OUTPUTS

Abstract

Extreme weather events such as storms and floods are increasing in frequency and intensity due to climate variability, creating a strong need for accurate and trustworthy prediction systems. This paper proposes a Blockchain-Enabled Convolutional Neural Network (CNN) Framework that leverages EGC2A (Enhanced Grid-Cell Climate Analytics) temporal outputs to improve the reliability, security, and interpretability of hydrometeorological forecasting. The EGC2A model generates high-resolution temporal climate features—such as rainfall intensity, atmospheric pressure shifts, and multi-layer humidity variations—which are fed into a customized CNN architecture to predict storm onset, flood severity, and risk distribution. To ensure tamper-proof data integrity, transparency, and decentralized verification, all processed climate features, model inference logs, and alert events are securely stored on a private Ethereum-based blockchain. The integration of blockchain significantly reduces data manipulation risks, ensures traceability of prediction updates, and enhances the trustworthiness of climate-alert systems used by disaster management authorities. Experimental evaluation demonstrates that the proposed hybrid framework achieves higher prediction accuracy, reduced false-alert rates, and improved temporal sensitivity compared to traditional deep-learning-only models. This work establishes a scalable architecture for future climate prediction systems where AI-driven analytics and blockchain-based trust operate cohesively to support proactive disaster preparedness.