<![CDATA[Flooding is one of the most frequent natural disasters and has substantial impacts on social, economic, and environmental conditions. Therefore, early detection plays a critical role in minimizing potential damage and supporting effective disaster response. This study proposes a Flood Detection System Using an Artificial Neural Network (ANN) with Blockchain-Based Data Integrity, which integrates predictive analytics and secure data management in a unified framework. The ANN model processes multisource environmental data such as satellite imagery, rainfall intensity, water level fluctuations, and soil moisture obtained from Google Earth Engine (GEE). Training is conducted using a sigmoid activation function and backpropagation algorithm to identify spatial and temporal patterns associated with flood-prone areas. The resulting classification outputs are stored in a blockchain ledger to ensure immutability, transparency, and protection against unauthorized data modification. Experimental evaluations demonstrate that the proposed hybrid approach achieves an accuracy of 95.82%, supported by precision, recall, and F1-score values that indicate consistent model performance across varying environmental conditions. The integration of blockchain provides verifiable and tamper-proof documentation of ANN predictions and related metadata. Overall, this research contributes a reliable, secure, and technically robust method for early flood detection, offering valuable support for data-driven decision-making in disaster mitigation and environmental risk management.]]>
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