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TitleFlood mapping using random forest and identifying the essential conditioning factors; a case study in Fredericton, New Brunswick, Canada
 
AuthorEsfandiari, M; Jabari, S; McGrath, HORCID logo; Coleman, D
SourceXXIV ISPRS Congress, Commission III; ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. 5, issue 3, 162280, 2020 p. 609-615, https://doi.org/10.5194/isprs-Annals-V-3-2020-609-2020 Open Access logo Open Access
Image
Year2020
Alt SeriesNatural Resources Canada, Contribution Series 20200362
PublisherCopernicus GmbH
MeetingXXIV ISPRS Congress, 2020 edition, Commission III; Nice; FR; August 28 - September 02, 2020
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf; html
ProvinceNew Brunswick
NTS21G/15; 21J/02
AreaFredericton
Lat/Long WENS -66.7917 -66.5500 46.0250 45.9000
Subjectsgeophysics; hydrogeology; Science and Technology; Nature and Environment; floods; remote sensing; satellite imagery; mapping techniques; urban geology; surface waters; rivers; hydrologic environment; statistical methods; modelling; topography; machine learning; Artificial intelligence; Geographic information systems; Emergency preparedness; elevations
Illustrationssatellite images; tables; bar graphs
ProgramCanada Centre for Remote Sensing Canada Centre for Remote Sensing Water Program
Released2020 08 03
AbstractFlood is one of the most damaging natural hazards in urban areas in many places around the world as well as the city of Fredericton, New Brunswick, Canada. Recently, Fredericton has been flooded in two consecutive years in 2018 and 2019. Due to the complicated behavior of water when a river overflows its bank, estimating the flood extent is challenging. The issue gets even more challenging when several different factors are affecting the water flow, like the land texture or the surface flatness, with varying degrees of intensity. Recently, machine learning algorithms and statistical methods are being used in many research studies for generating flood susceptibility maps using topographical, hydrological, and geological conditioning factors. One of the major issues that researchers have been facing is the complexity and the number of features required to input in a machine-learning algorithm to produce acceptable results. In this research, we used Random Forest to model the 2018 flood in Fredericton and analyzed the effect of several combinations of 12 different flood conditioning factors. The factors were tested against a Sentinel-2 optical satellite image available around the flood peak day. The highest accuracy was obtained using only 5 factors namely, altitude, slope, aspect, distance from the river, and land-use / cover with 97.57% overall accuracy and 95.14% kappa coefficient. we used Random Forest to model the 2018 flood in Fredericton and analyzed the effect of several combinations of 12 different flood conditioning factors. The factors were tested against a Sentinel-2 optical satellite image available around the flood peak day. The highest accuracy was obtained using only 5 factors namely, altitude, slope, aspect, distance from the river, and land-use / cover with 97.57% overall accuracy and 95.14% kappa coefficient. we used Random Forest to model the 2018 flood in Fredericton and analyzed the effect of several combinations of 12 different flood conditioning factors. The factors were tested against a Sentinel-2 optical satellite image available around the flood peak day. The highest accuracy was obtained using only 5 factors namely, altitude, slope, aspect, distance from the river, and land-use / cover with 97.57% overall accuracy and 95.14% kappa coefficient.
Summary(Plain Language Summary, not published)
Evaluation of the essential data layers for flood susceptibility mapping, using Random Forest
GEOSCAN ID327068

 
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