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DOI: https://doi.org/10.4491/eer.2022.411
Application of topological data analysis to flood disaster management in Nigeria
Felix Obi Ohanuba1,2, Mohd Tahir Ismail1, and Majid Khan Majahar Ali1
1School of Mathematical Sciences, Universiti Sains Malaysia,11800 Penang, Malaysia
2Department of Statistics, University of Nigeria, 410001 Nsukka, Enugu State, Nigeria
Corresponding Author: Felix Obi Ohanuba ,Tel: +60104634429, Fax: +2348067104123 , Email: felix.ohanuba@student.usm.my; felix.ohanuba@unn.edu.ng
Received: August 1, 2022;  Accepted: November 16, 2022.
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The importance of this research to the literature lies in the ability to develop a hybrid method of Topological Data Analysis and Unsupervised Machine Learning (TDA-uML) for flood detection. The working method in TDA-uML entails collection and loading of datasets, feature representation, data preprocessing (i.e., training, testing, computation, and validation), and data classification. Three properties make TDA distinct from traditional methods: coordinate-invariance, deformative-invariance, and compressed-representative. Formerly utilized hydrologic, hydraulic, and statistical models for flood control were frequently erroneous in their forecasts, lacked the use of hybrid models, and were not validated. The main research objective is to develop a hybrid method for predicting floods. The motivation is to fill research gaps by using TDA-uML methods for persistent homology (PH) and synthetic k-means clustering. The results will be used to compare and categorize the features that are produced. Seven states were selected based on Nigeria’s flood history and affected population. The 7 states are located within the 8 hydrological areas of Nigeria. The efficiency of the resultant validity was 91%. The findings contributed to the development of a model for flood prediction and management; topological features were extracted from the data to predict and categorize the risk
Keywords: Disaster management | Hazards | Machine-Learning | Persistent-homology | Topological data
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Application of Biological Activated Carbon Process for Water Quality Improvement of Stagnant Stream Channels  2014 December;19(4)
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