Topological Data Analysis and Wavelet- Unsupervised Machine Learning Approaches to Identifying the Flooding and Non-Flooding Zones

Every year, millions of individuals are affected by flooding in Bangladesh resulting in loss of habitat, properties, and lives. Because the country experiences monsoon rains, flat lands, cyclones as well as glacier melt, it is highly prone to flooding. This work aimed to lessen the impact of flooding disasters and develop effective strategies for early warning systems. The intertwined weather patterns known as the atmospheric river (AR) of Bangladesh make use of topological data analysis (TDA) in connection with wavelet decomposition and unsupervised machine learning (k-means clustering) methods to pave the way for enhanced flood detection. We have pinpointed the most desired cluster group, which stands at k = 4 in our argument. In terms of the silhouette coefficient, this group had the highest values in Rangpur at 0.9016, Sylhet at 0.9028, Barisal at 0.9094, and Khulna at 0.9014. Our work was highly efficient and ranked at approximately 96% using SVMs. Later, we identified flooded and non-flooded regions with the same technique. The potential flood-prone periods were identified for all four regions based on optimal clustering results, as indicated by silhouette coefficient values. At any time when there is a possibility of flood occurrence, it can alert residents in the affected regions to move out to safer grounds; this could also stimulate appropriate actions from the risk managers.