Flood Prediction using Clustering Analysis with Geospatial Dataset

Kennedy, Idhaya Bastine (2024) Flood Prediction using Clustering Analysis with Geospatial Dataset. Masters thesis, Dublin, National College of Ireland.

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Abstract

Floods are one of nature’s most deadly disasters, caused by deforestation, climate change, and increasing urbanization. Flood-prone locations must be accurately projected in order to reduce dangers and facilitate effective disaster management. This study employs geospatial dataset clustering analysis to identify flood-prone areas and determine risk levels. A novel weighted scoring approach for evaluating flood severity was developed using a dataset that included 21 important factors, such as topography drainage, urbanization, and monsoon intensity. K-Means clustering was used to classify regions as low, moderate, high, or critical risk. The geographic visualization of the clustered data revealed vital facts regarding local vulnerabilities. The findings revealed that clustering is excellent at spotting trends and prioritizing high-risk locations. This study demonstrates the synergy between machine learning and geospatial analysis, paving the door for scalable and flexible catastrophe management solutions. Future projects include integrating real-time data and investigating sophisticated clustering approaches to improve forecast accuracy.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 10:43
Last Modified:	03 Sep 2025 10:43
URI:	https://norma.ncirl.ie/id/eprint/8728

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