Comparative Evaluation of AI Models for Flood Prediction: CNN and RNN Integration with Bi-LSTM
DOI:
https://doi.org/10.32628/IJSRST251383Keywords:
Flood Prediction, Artificial Intelligence, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Deep Learning, Disaster ManagementAbstract
Flooding is devastating natural disaster that poses significant threats to human life, infrastructure, and economies, particularly in regions susceptible to heavy rainfall and monsoonal patterns. Accurate and timely flood prediction is crucial for effective disaster management and mitigation. This paper presents a comparative evaluation of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, two prominent deep learning architectures, for flood prediction. The research evaluates modeling results for different forecasting horizons for Darbhanga, Bihar. The study draws on existing literature and proposes methodology to evaluate these models using ten years of spatial-temporal historical rainfall and flood data, aiming to provide valuable insights for governance, practitioners and researchers in field of flood disaster management.
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