| Title | Flood mapping using random forest and identifying the essential conditioning factors; a case study in Fredericton, New Brunswick, Canada |
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| Author | Esfandiari, M; Jabari, S; McGrath, H ; Coleman, D |
| Source | XXIV 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 |
| Image |  |
| Year | 2020 |
| Alt Series | Natural Resources Canada, Contribution Series 20200362 |
| Publisher | Copernicus GmbH |
| Meeting | XXIV ISPRS Congress, 2020 edition, Commission III; Nice; FR; August 28 - September 02, 2020 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf; html |
| Province | New Brunswick |
| NTS | 21G/15; 21J/02 |
| Area | Fredericton |
| Lat/Long WENS | -66.7917 -66.5500 46.0250 45.9000 |
| Subjects | geophysics; 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 |
| Illustrations | satellite images; tables; bar graphs |
| Program | Canada Centre for Remote Sensing Canada Centre for Remote Sensing Water Program |
| Released | 2020 08 03 |
| Abstract | Flood 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 ID | 327068 |
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