Citation

Hartomo KD, Nataliani Y, Hasibuan ZA. PeerJ Comput. Sci. 2022; 8: e935.

Copyright

(Copyright © 2022, PeerJ)

DOI

10.7717/peerj-cs.935

PMID

35494821

PMCID

PMC9044244

Abstract

This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (i.e., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%.

Language: en

Keywords

Spatial; Risk areas; Risk values; Tsunami; Vegetation index