Open AccessComputer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning
Author(s) -
Sri Yulianto Joko Prasetyo,
Bistok Hasiholan Simanjuntak,
Kristoko Dwi Hartomo,
Wiwin Sulistyo
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i5.3100
Subject(s) - vegetation (pathology) , normalized difference vegetation index , vulnerability (computing) , kriging , vulnerability assessment , remote sensing , environmental science , mean squared error , physical geography , machine learning , geography , geology , computer science , statistics , mathematics , climate change , medicine , psychology , oceanography , computer security , pathology , psychological resilience , psychotherapist
This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.