Dynamic flood risk prediction in Houston: a multi-model machine learning approach
| Autor: | Shuchi Mishra, Aproorv Bajpai, Agradeep Mohanta, Biplab Banerjee, Shrishti Rajput, Sudipta Kundu |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Geocarto International, Vol 39, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 10106049 1752-0762 1010-6049 |
| DOI: | 10.1080/10106049.2024.2432866 |
| Popis: | In assessing flood susceptibility in Houston, key geographical parameters such as drainage density, slope, distance from rivers and roads, LULC, and rainfall data were analyzed using machine learning models, including Decision Trees, Random Forest, Gradient Boosting, SVM, and ANN. Performance evaluation using ROC curves and AUC revealed ANN as the most effective model, achieving an AUC of 85.00%, outperforming Decision Trees (78.96%), Random Forest (80.29%), Gradient Boosting (82.16%), and SVM (81.84%). Statistical validation with the Kruskal-Wallis test confirmed significant differences among models (H = 11.35, p = 0.023), with ANN excelling in pairwise comparisons. This study highlights ANN's robustness in flood prediction, providing a crucial tool for urban planning and fostering resilient, sustainable development. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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