Next Article in Journal
Intra-Annual Sentinel-2 Time-Series Supporting Grassland Habitat Discrimination
Previous Article in Journal
SAR Image Classification Using Fully Connected Conditional Random Fields Combined with Deep Learning and Superpixel Boundary Constraint
Open AccessArticle

A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling

1
School of Earth and Environment, University of Canterbury, Christchurch 8041, New Zealand
2
Geospatial Research Institute, University of Canterbury, Christchurch 8041, New Zealand
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 275; https://doi.org/10.3390/rs13020275
Received: 1 December 2020 / Revised: 10 January 2021 / Accepted: 10 January 2021 / Published: 14 January 2021
(This article belongs to the Section AI Remote Sensing)
Given the high financial and institutional cost of collecting and processing accurate topography data, many large-scale flood hazard assessments continue to rely instead on freely-available global Digital Elevation Models, despite the significant vertical biases known to affect them. To predict (and thereby reduce) these biases, we apply a fully-convolutional neural network (FCN), a form of artificial neural network originally developed for image segmentation which is capable of learning from multi-variate spatial patterns at different scales. We assess its potential by training such a model on a wide variety of remote-sensed input data (primarily multi-spectral imagery), using high-resolution, LiDAR-derived Digital Terrain Models published by the New Zealand government as the reference topography data. In parallel, two more widely used machine learning models are also trained, in order to provide benchmarks against which the novel FCN may be assessed. We find that the FCN outperforms the other models (reducing root mean square error in the testing dataset by 71%), likely due to its ability to learn from spatial patterns at multiple scales, rather than only a pixel-by-pixel basis. Significantly for flood hazard modelling applications, corrections were found to be especially effective along rivers and their floodplains. However, our results also suggest that models are likely to be biased towards the land cover and relief conditions most prevalent in their training data, with further work required to assess the importance of limiting training data inputs to those most representative of the intended application area(s). View Full-Text
Keywords: DEM; DTM; topography; flood modelling; machine learning; artificial neural networks; convolutional neural networks; random forests DEM; DTM; topography; flood modelling; machine learning; artificial neural networks; convolutional neural networks; random forests
Show Figures

Graphical abstract

MDPI and ACS Style

Meadows, M.; Wilson, M. A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling. Remote Sens. 2021, 13, 275. https://doi.org/10.3390/rs13020275

AMA Style

Meadows M, Wilson M. A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling. Remote Sensing. 2021; 13(2):275. https://doi.org/10.3390/rs13020275

Chicago/Turabian Style

Meadows, Michael; Wilson, Matthew. 2021. "A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling" Remote Sens. 13, no. 2: 275. https://doi.org/10.3390/rs13020275

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop