Preprints
https://doi.org/10.5194/nhess-2020-41
https://doi.org/10.5194/nhess-2020-41

  13 Mar 2020

13 Mar 2020

Review status: this preprint was under review for the journal NHESS but the revision was not accepted.

Comparison of machine learning classification algorithms for land cover change in a coastal area affected by the 2010 Earthquake and Tsunami in Chile

Matias I. Volke and Rodrigo Abarca-Del-Rio Matias I. Volke and Rodrigo Abarca-Del-Rio
  • Department of Geophysics, Faculty of Physical and Mathematical Sciences, University of Concepcion, Concepcion, Chile

Abstract. Earthquakes and tsunamis are the natural events that generate subsequent geomorphological land cover changes. The damage is usually of such importance and of such a diverse nature that it is imperative to have tools that allow quick and precise monitoring. Thus, know which classification methods have the best potential to obtain greater precision will improve natural disaster management. We analyze Tubul locality (37.21º S; 73.43º O) in Biobío region, Chile, in which greatest geomorphological changes were documented after the earthquake and tsunami occurred 27/February/2010. These changes can be analyzed using different machine learning methods. We investigate the Support Vector Machine (SVM) and Random Forest (RF), versus the Maximum Likelihood (ML) classification method of Landsat TM and ASTER satellite images. The comparison of the performance of the classifiers and certifying accuracy improvement shows that machine learning algorithms are superior to traditional classification methods in terms of overall accuracy and robustness. The general classification accuracy was approximately 97 %. We also visualize the land cover transformations, showing that 26 % of the region was altered. The results of performance testing of machine learning methodologies was consistent with other studies and presents a valid application in the visualization of land cover changes in areas of natural disasters.

Matias I. Volke and Rodrigo Abarca-Del-Rio

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Matias I. Volke and Rodrigo Abarca-Del-Rio

Matias I. Volke and Rodrigo Abarca-Del-Rio

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Short summary
The damage caused by earthquakes and tsunamis is usually of such importance that it is essential to have tools that allow rapid and accurate monitoring of the effects of such events. The classification of satellite images is shown as a important tool for such objectives. Therefore, knowing which classification methods have the best potential for greater precision will improve natural disaster management. The results shows that machine learning algorithms are superior in terms of accuracy.
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