• Sangeeta Uranakar
• Adithi B
• Daniya Khanum
• Haleema Sultana
• Iram Shaikh

Flood is a veritably generally being disaster affecting a lot of people across the globe. Hence, flood tide threat assessment becomes a serious concern, which can reduce the damage or affects caused by the cataracts. This evaluation can assist with anticipating flood conditions, producing early warning systems, managing catastrophic events, offering prompt assistance, and carrying out rescue operations in inundated areas. In this project, we explore the use of deep learning models for predicting the severity level of a flooding event captured in images by the habitats in a flooded region. The proposed model takes the images of a flooding event as input and determines its severity level. The proposed model is then evaluated on the dataset and compared with a baseline convolutional neural network (CNN) based model. Simulation results reveal that the proposed model outperforms the baseline model in terms of accuracy for classification of images of flooding events into severity levels.

CNN, Training Dataset, Pre-processing, Image processing.

"Projection of the amount of inundation in accordance with river flood level projections", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.297-301, May-2024, Available :http://www.jetir.org/papers/JETIRGG06047.pdf

"Projection of the amount of inundation in accordance with river flood level projections", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp297-301, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06047.pdf