Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study-Reference-Cited by-同舟云学术

Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study

Published:2018-05 Issue:9 Volume:11 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Al-Abadi Alaa M.

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

http://link.springer.com/article/10.1007/s12517-018-3584-5/fulltext.html

Reference55 articles.

1. Akar Ö (2017) The Rotation Forest algorithm and object-based classification method for land use mapping through UAV images. Geocarto Int. https://doi.org/10.1080/10106049.2016.1277273

2. Bathrellos GD, Karymbalis E, Skilodimou HD, Gaki-Papanastassiou K, Baltas FA (2016) Urban flood hazard assessment in the basin of Athens Metropolitan city, Greece. Environ Earth Sci 75:319. https://doi.org/10.1007/s12665-015-5157-1

3. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

4. Breiman L, Cutler A (2004) Random forests: classification/clustering. Retrieved May 2004, from http://www.stat.berkeley.edu/users/breiman/RandomForests

5. Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazard Earth Sys 5(6):853–862

Cited by 93 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment;Environmental Science and Pollution Research;2024-07-20

2. Utilizing a fusion of remote sensing data and machine learning models to forecast flood risks to agriculture in Hanoi City, Vietnam;Letters in Spatial and Resource Sciences;2024-06-24

3. Drought susceptibility mapping in Iraq using GRACE/GRACE-FO, GLDAS, and machine learning algorithms;Physics and Chemistry of the Earth, Parts A/B/C;2024-06

4. Harnessing multisatellite remote sensing data and machine learning for flood risk assessment in Nam Ngum River Basin, Lao PDR;SPIE Future Sensing Technologies 2024;2024-05-28

5. Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR;Geomatics, Natural Hazards and Risk;2024-05-28