Dynamic flood risk prediction in Houston: a multi-model machine learning approach
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 evalua...
I tiakina i:
| Ngā kaituhi matua: | , , , , , |
|---|---|
| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
Taylor & Francis Group
2024-01-01
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| Rangatū: | Geocarto International |
| Ngā marau: | |
| Urunga tuihono: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2432866 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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