Published Paper Details:

Floods pose significant risks to human lives and infrastructure worldwide, necessitating accurate prediction and timely response strategies. This study proposes an Artificial Intelligence (AI) and Machine Learning (ML) approach to predict flood intensity and evaluation human headcount in flood-affected areas. Leveraging historical flood data, atmospheric parameters, terrain features, and satellite imagery, our model employs advanced AI algorithms, including deep learning neural networks and ensemble methods. Firstly, flood intensity prediction is addressed using a amalgamation of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to evaluate spatiotemporal patterns and predict flood levels with high exactness. Secondly, human headcount estimation utilizes a hybrid model incorporating support vector machines (SVMs) and random forest algorithms trained on socio-demographic data, population densities, and real-time information from social media and disaster retort agencies. The proposed AI/ML framework offers several advantages, including real-time monitoring, early caution systems, and efficient resource allocation during flood events. Validation against historical flood incidents establishes the model's robustness and effectiveness in predicting flood intensity and estimating human headcount. Integration of this technology into prevailing disaster management systems can enhance preparation, response, and recovery efforts, ultimately modifying the adverse impacts of floods on communities and organization.