Flood risk mapping for the lower Narmada basin in India: a machine learning and IoT-based framework
Author:
Funder
Microsoft Research
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-022-05347-2.pdf
Reference52 articles.
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2. Balica SF, Popescu I, Beevers L, Wright NG (2013) Parametric and physically based modelling techniques for flood risk and vulnerability assessment: a comparison. Environ Model Softw 41:84–92. https://doi.org/10.1016/j.envsoft.2012.11.002
3. Breiman L (2001) Random forests. Mach Learn. https://doi.org/10.1023/A:1010933404324
4. Carvalho J, Santos JPV, Torres RT et al (2018) Tree-based methods: concepts, uses and limitations under the framework of resource selection models. J Environ Inform. https://doi.org/10.3808/jei.201600352
5. Chen YR, Yeh CH, Yu B (2011) Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Nat Hazards 59:1261–1276. https://doi.org/10.1007/s11069-011-9831-7
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