Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles-Reference-Cited by-同舟云学术

Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles

Published:2020-04 Issue: Volume:712 Page:136492
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Costache Romulus,Tien Bui Dieu^ORCID

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference58 articles.

1. Assessment of pluvial floods potential on the Rivers of the Republic of Moldova;Ana;Present Environ. Sustain. Dev.,2018

2. Ensemble flood forecasting considering dominant runoff processes–part 1: set-up and application to nested basins (Emme, Switzerland);Antonetti;Nat. Hazards Earth Syst. Sci.,2019

3. Novel hybrid evolutionary algorithms for spatial prediction of floods;Bui;Sci. Rep.,2018

4. A new intelligence approach based on GIS-based multivariate adaptive regression splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area;Bui;J. Hydrol.,2019

5. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping;Bui;Catena,2019

Cited by 110 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. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review;Remote Sensing;2024-09-11

2. Investigation of Flood Hazard Susceptibility Using Various Distance Measures in Technique for Order Preference by Similarity to Ideal Solution;Applied Sciences;2024-08-10

3. Advancing flood susceptibility modeling using stacking ensemble machine learning: A multi-model approach;Journal of Geographical Sciences;2024-08

4. A hydro-geomorphologic assessment of flood generation potentiality in ungauged sub-basins and their prioritization based on traditional, statistical, MCDM and Nash-GIUH models of a tropical plateau-fringe River;Journal of Hydrology;2024-08

5. Integrated approaches for flash flood susceptibility mapping: spatial modeling and comparative analysis of statistical and machine learning models. A case study of the Rheraya watershed, Morocco;Journal of Water and Climate Change;2024-07-17