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TitleFlood hazard risk mapping using a pseudo supervised random forest
AuthorEsfandiari, M; Abdi, G; Jabari, S; McGrath, HORCID logo; Coleman, D
SourceRemote Sensing vol. 12, issue 19, 3206, 2020 p. 1-23, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20200577
Mediapaper; on-line; digital
File formatpdf; html
Subjectshydrogeology; geophysics; Nature and Environment; Science and Technology; Health and Safety; floods; mapping techniques; models; modelling; water levels; hydrodynamics; remote sensing; satellite imagery; Methodology; Artificial intelligence
Illustrationslocation maps; satellite images; time series; tables; geoscientific sketch maps; flow diagrams; models; bar graphs; plots
ProgramCanada Centre for Remote Sensing Canada Centre for Remote Sensing Water Program
Released2020 10 01
AbstractDevastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, the height above nearest drainage (HAND) model can produce flood prediction maps with limited accuracy. The objective of this research is to produce an accurate and dynamic flood modeling technique to produce flood maps as a function of water level by combining the HAND model and machine learning. In this paper, the HAND model was utilized to generate a preliminary flood map; then, the predictions of the HAND model were used to produce pseudo training samples for a R.F. model. To improve the R.F. training stage, five of the most effective flood mapping conditioning factors are used, namely, Altitude, Slope, Aspect, Distance from River and Land use/cover map. In this approach, the R.F. model is trained to dynamically estimate the flood extent with the pseudo training points acquired from the HAND model. However, due to the limited accuracy of the HAND model, a random sample consensus (RANSAC) method was used to detect outliers. The accuracy of the proposed model for flood extent prediction, was tested on different flood events in the city of Fredericton, NB, Canada in 2014, 2016, 2018, 2019. Furthermore, to ensure that the proposed model can produce accurate flood maps in other areas as well, it was also tested on the 2019 flood in Gatineau, QC, Canada. Accuracy assessment metrics, such as overall accuracy, Cohen's kappa coefficient, Matthews correlation coefficient, true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR), were used to compare the predicted flood extent of the study areas, to the extent estimated by the HAND model and the extent imaged by Sentinel-2 and Landsat satellites. The results confirm that the proposed model can improve the flood extent prediction of the HAND model without using any ground truth training data.
Summary(Plain Language Summary, not published)
New approach to on the fly flood mapping using HAND model and pseudo supervised Random forest machine learning method.

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