Topological data analysis via unsupervised machine learning for recognizing atmospheric river patterns on flood detection
Felix Obi Ohanuba, Mohd Tahir Ismail, Majid Khan Majahar Ali · Scientific African · 2021
Topological data analysis (TDA) has recently been a very reliable research area in Statistics for extracting shape from data. Flooding annually destroys properties, buildings, farmland, loss of life, etc. in various regions of the world. In this study, a new K-means clustering method that combined TDA and machine learning (ML) functions. The method is aimed at solving flood problems by identifying the feature patterns of floods associated with seven selected states in Nigeria; predicting flood menace; measuring the extent of spread of the resultant clusters (degree of flood risk) and choosing the best test that measures the validity of the analysis used. The study method is designed to provide vital information about shape characteristic of spatial data. Other advantages include its flexibility with other methods. It is threshold-free (i.e., no need to fix any threshold criteria for detection method; it has properties which does that). After our model's training process, we obtained the best group at k = 2, where we have the highest Silhouette coefficient that gave an efficiency outcome of approximately 80%. The method was able to detect the flooding and no flooding areas in the data, and the discovery of variability of clusters. The findings can provide information to dweller in flooding zones to vacate and avert flood disaster, and for the risk managers to take action. We recommend the method in flood mitigation and control. Further research is needed to explore the combination aspect.