Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extend of the flood inventory

Shafapour Tehrany, M and Jones, S 2017, 'Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extend of the flood inventory', in Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Kuala Lumpur, Malaysia, 4-5 October 2017, pp. 209-214.


Document type: Conference Paper
Collection: Conference Papers

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Title Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extend of the flood inventory
Author(s) Shafapour Tehrany, M
Jones, S
Year 2017
Conference name Geomatic and Geospatial Technology 2017
Conference location Kuala Lumpur, Malaysia
Conference dates 4-5 October 2017
Proceedings title Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publisher International Society for Photogrammetry and Remote Sensing
Place of publication Germany
Start page 209
End page 214
Total pages 6
Abstract This paper explores the influence of the extent and density of the inventory data on the final outcomes. This study aimed to examine the impact of different formats and extents of the flood inventory data on the final susceptibility map. An extreme 2011 Brisbane flood event was used as the case study. LR model was applied using polygon and point formats of the inventory data. Random points of 1000, 700, 500, 300, 100 and 50 were selected and susceptibility mapping was undertaken using each group of random points. To perform the modelling Logistic Regression (LR) method was selected as it is a very well-known algorithm in natural hazard modelling due to its easily understandable, rapid processing time and accurate measurement approach. The resultant maps were assessed visually and statistically using Area under Curve (AUC) method. The prediction rates measured for susceptibility maps produced by polygon, 1000, 700, 500, 300, 100 and 50 random points were 63 %, 76 %, 88 %, 80 %, 74 %, 71 % and 65 % respectively. Evidently, using the polygon format of the inventory data didn't lead to the reasonable outcomes. In the case of random points, raising the number of points consequently increased the prediction rates, except for 1000 points. Hence, the minimum and maximum thresholds for the extent of the inventory must be set prior to the analysis. It is concluded that the extent and format of the inventory data are also two of the influential components in the precision of the modelling.
Subjects Natural Hazards
Geospatial Information Systems
Photogrammetry and Remote Sensing
Copyright notice © The Author(s) 2017. Creative Commons Attribution 4.0 License
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