Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning

Abstract

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.

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

“…The HE method enhances the image by distributing the image brightness levels equally across the brightness scale [1], [11], [15], [21], [44], [53], [54], [72], [94], [119], [136], [140], [145], [150], [159], [168], [169], [172], [175]. Furthermore, the intensity of the contrast enhancement method is measured through the root mean square (RMS), where the higher the RMS value, the better the contrast image [22], [35], [48], [178]- [181]. As depicted in Table 1, the image enhancement methods for SPOT-5 images have been compared and ran to produce the best approach.…”

“…The goal of NIR-HE using the NIR channel is to improve the image contrast and hence to make it suitable for classification of vegetation in feature extraction [2], [28], [33], [38], [40], [48], [52]- [55]. The false color composite image is enhanced using HE algorithm.…”

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data

“…VI is a new LULC analysis method based on infrared and visible spectral irradiance measurement with leaf area indices, ground cover, chlorophyll content, and plant biomass [9]. LULC temporal dynamics, identified using the Vegetation Index (VI) indicator, are the normalized vegetation difference index (NDVI), the modified terrestrial adaptation vegetation index (MSAVI), and the normalized water difference index ( NDWI), Modified Normalized Hydration Index (MNDWI) and Normalized Difference Composition Index (NDBI) [10].…”