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Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling by Kim, Dong Eon; Liong, Shie-Yui; Gourbesville, Philippe; Andres, Ludovic; Liu, Jiandong is a Environmental Science article available to read on EtoBox.

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Digital elevation models (DEMs) are crucial in flood modeling as DEM data reflects the actual topographic characteristics where water can flow in the model. However, a high-quality DEM is very difficult to acquire as it is very time consuming, costly, and, often restricted. DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the artificial neural network (ANN) to improve the quality of SRTM’s DEM. High-quality DEM is used as target data in the training of ANN. The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available. In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore; with the performance criteria using various matrices, e.g., visual clarity, scatter plots, root mean square error (RMSE) and flood maps. The DEM resulting from the improved SRTM (iSRTM) showed significantly better results than the original SRTM DEM, with about 38% RMSE reduction. Flood maps from iSRTM DEM show much more reasonable f

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Author

Kim, Dong Eon; Liong, Shie-Yui; Gourbesville, Philippe; Andres, Ludovic; Liu, Jiandong

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: Molecular Diversity Preservation International (MDPI), 2009- (ISSN 2073-4441)

Published

2020

Language

EN

Field

Environmental Science (Physical Sciences)