A Statistical Approach to Mapping Flood Susceptibility in the Lower Connecticut River Valley Region-Reference-Cited by-同舟云学术

A Statistical Approach to Mapping Flood Susceptibility in the Lower Connecticut River Valley Region

Published:2018-10 Issue:10 Volume:54 Page:7603-7618
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Giovannettone Jason¹^ORCID,Copenhaver Tom²,Burns Margot³,Choquette Scott⁴

Affiliation:

1. Dewberry Fairfax VA USA

2. Dewberry Denver CO USA

3. RiverCOG Essex CT USA

4. Dewberry New Haven CT USA

Funder

U.S. Department of Housing and Urban Development

Connecticut Department of Energy and Environmental Protection

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR023018

Reference59 articles.

1. An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 2: Structure of a physically-based, distributed modelling system

2. The hydromorphology of an urbanizing watershed using multivariate elasticity

3. Flood Risk Delineation in the United States: How Much Loss Are We Capturing?

4. Center for Land Use Education and Research Land Cover College of Agriculture and Natural Resources University of Connecticut. (2010).Land cover 30‐m data set. Location. Retrieved fromhttps://clear.uconn.edu/projects/landscapeLIS/landcover.htm

5. Crawford N. H. &Linsley R. K.(1966).Digital simulation in hydrology: Stanford Watershed Model IV. (Tech. Rep. 39 p. 210). Stanford CA: Department of Civil Engineering Stanford University.

Cited by 35 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. Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability;Journal of Hydrology;2024-10

2. Flood assessment using machine learning and its implications for coastal spatial planning in Phu Yen Province, Vietnam;Journal of Water and Climate Change;2024-07-22

3. Examination of the efficacy of machine learning approaches in the generation of flood susceptibility maps;Environmental Earth Sciences;2024-07

4. Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia;Remote Sensing;2024-06-14

5. Flood hazard forecasting and management systems: A review of state-of-the-art modelling, management strategies and policy-practice gap;International Journal of Disaster Risk Reduction;2024-06