Open Access
| Issue |
E3S Web Conf.
Volume 259, 2021
2021 12th International Conference on Environmental Science and Development (ICESD 2021)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 7 | |
| Section | Environmental Monitoring and Ecosystem Protection | |
| DOI | https://doi.org/10.1051/e3sconf/202125901004 | |
| Published online | 12 May 2021 | |
- Du, J., Fang, J., Xu, W., & Shi, P. Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province China. Stochastic Environmental Research and Risk Assessment, 27(2), 377–387 (2013) [Google Scholar]
- Ke, Q., Tian, X., Bricker, J., Tian, Z., Guan, G., Cai, H., Huang, X., Yang, H., Liu, J. Urban pluvial flooding prediction by machine learning approaches-a case study of Shenzhen city, China. Advances in Water Resources, 145: 103–719 (2020) [Google Scholar]
- Elkhrachy, & Ismail. Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran city, Kingdom of Saudi Arabia (KSA). Egyptian Journal of Remote Sensing & Space Sciences, 18(2), 261–278 (2015) [Google Scholar]
- Cai, T., Li, X., Ding, X., Wang, J., & Zhan, J. Flood risk assessment based on hydrodynamic model and fuzzy comprehensive evaluation with GIS technique. International Journal of Disaster Risk Reduction, 2019.101077 (2019) [Google Scholar]
- Chau, K. W., Wu, C. L., & Li, Y. S. Comparison of several flood forecasting models in Yangtze river. Journal of Hydrologic Engineering, 10(6), 485–491 (2005) [Google Scholar]
- Hong, H., Pradhan, B., Bui, T., Xu, C., Youssef, M., & Chen, W. Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Geomatics, Natural Hazards and Risk, 8(2), 544–569 (2016) [Google Scholar]
- Tehrany, S., Pradhan, B., & Jebur, N. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512(2014), 332–343 (2014) [Google Scholar]
- Wenjie, C. Urban Flood Hydrological and Hydrodynamic Model Construction and Flood Management Key Issues Exploration. Unpublished doctoral dissertation, South China University of Technology, Guangzhou, China (2019) [Google Scholar]
- Lamovec, P., Veljanovski, T., Mikos, M., & Ostir, K. Detecting flooded areas with machine learning techniques: Case study of the Selska Sora river flash flood in September 2007. Journal of Applied Remote Sensing, 7(1), 1–13 (2013) [Google Scholar]
- Tehrany, S., Pradhan, B., & Jebur, N. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504(2013), 69–79 (2013) [Google Scholar]
- Mukherjee, F., Singh, D. Detecting flood prone areas in Harris County: a GIS based analysis. GeoJournal. 85(3): 647–663 (2019) [Google Scholar]
- Xitao, H. Study on urban waterlogging vulnerability evaluation and disaster impact model. Unpublished doctoral dissertation, Xi’an University of Technology, Xi’an, China. (2020) [Google Scholar]
- Abedin, Hossain, S., J. Stephen, & Haroon. GIS framework for spatiotemporal mapping of urban flooding. Geosciences. 9(2) (2019) [Google Scholar]
- Sen, Z., & Abdüsselam Altunkaynak. A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrological Processes, 20(9), 3991–3991 (2006) [Google Scholar]
- Deepak Singh, Bish Chandranath, Chatterjee, & Shivani, et al. Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study. Natural Hazards, 84(2), 749–776 (2016) [Google Scholar]
- Tyrna, B., Assmann, A., Fritsch, K., & Johann, G. Large-scale high-resolution pluvial flood hazard mapping using the raster-based hydrodynamic two-dimensional model floodareaHPC. Journal of Flood Risk Management, 11(S2) S1024–S1037 (2018) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.