A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries - Research on remote sensing classification and spatial prediction

Aug 5, 2026

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This study builds a computer model to detect and predict built-up land within tsunami hazard zones using Sentinel-2A imagery and built-up indices. The model combines multiple indices—NBI, UI, NDBI, modified built-up index (MBI), and index-based built-up index (IBI)—with machine learning classifiers Random Forest and extreme gradient boosting (XGBoost). Spatial distribution is predicted using ordinary kriging, and classification performance is evaluated using overall accuracy and Cohen’s Kappa. Results show highly accurate mapping, with XGBoost reaching above 91%, supporting identification of settlements, industrial areas, open land and water, and agriculture/tourism land.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Tsunami vulnerability assessment using remote sensing

Rationale and study aims

Methodology

Built-up indices from Sentinel-2A imagery

Machine learning classification (RF, XGBoost)

Spatial prediction (Ordinary Kriging)

Accuracy evaluation (Overall accuracy, Cohen’s Kappa)

Results and Discussion

Built-up land identification and accuracy outcomes

Bulletin of Electrical Engineering and Informatics Vol. 13, No. 2, April 2024, pp. 1138~1146 ISSN: 2302-9285, DOI: 10. 11591/eei.v13i2 .5910 􀂈 1138 | A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel
imageries
Sri Yulianto Joko Prasetyo1, Bistok Hasiholan Simanjuntak2, Yeremia Alfa Susatyo1, Wiwin Sulistyo1
1Department of Informatic Engineering, Faculty of Information Technology, Satya Wacana Christian University, Central Java, Indonesia 2Bussines and Agriculture Faculty, Satya Wacana Christian University, Central Java, Indonesia | | | | --- | --- | --- | | Article Info | ABSTRACT | | | Article history:
Received Feb 1, 2023 Revised Jun 11, 2023 Accepted Sep 14, 2023
Keywords:
Build-up indices Computer model Machine learning Remote sensing Tsunami
Corresponding Author: | | This study aims to build a computer model to detect built-up land in the identified tsunami hazard zone based on Sentinel 2A imagery using the normalized built up area index (NBI), urban index (UI), normalize difference build-up index (NDBI), a modified built-up index (MBI), index-based builtup index (IBI) algorithms, optimized with machine learning Random Forest (RF) and extreme gradient boosting (XGboost) algorithms and the spatial patterns are predicted using the ordinary kriging (OK) method. Testing of the accuracy of the classification and optimization results was performed using the Kohen Kappa and overall accuracy functions. The results of the study show that a built-up land consisting of open land and water, settlements, industry areas, and agriculture and tourism areas can be identified using the parameters of built-up indices. The accuracy testings that were performed using overall accuracy and Kohen Kappa methods show that classification and prediction are highly accurate using XGboost machine learning, namely >91% . This study produces a novelty of finding, namely a computer model to detect and predict the spatial distribution of built-up land in 4 scales, i.e., very low, low, high, and very high based on NBI, UI, NDBI, MBI, IBI data extracted from Sentinel 2A imagery.
This is an open access article under the CC BY-SA license.
| | Sri Yulianto Joko Prasetyo
Department of Informatic Engineering, Faculty of Information Technology Satya Wacana Christian University
Central Java, Indonesia
Email: [sri.yulianto@uksw.edu](sri.yulianto@uksw.edu) | | | 1. INTRODUCTION Currently, the methodology for conducting tsunami vulnerability detect and assessments is very advanced and developed rapidly starting from modeling methods are linear, non linear, numerical, photogrammetry image analysis and remote sensing [1]–[5] . Remote sensing image analysis methods include medium resolution images such as Landsat 8 OLI and Sentinel 2A, or high-resolution images such as SPOT 5 and Quickbird [6]–[8] . A quick calculation of damage to buildings caused by a tsunami can be done because of the existence of various machine learning functions and built-up indices data extracted from remote sensing imageries [9] . Machine learning methods have long been applied to mitigate tsunamis, including predicting inundation, maximum wave height and arrival time of tsunami waves on land, even though the uncertainty of the prediction results is very high [10] . In Indonesia, tsunami is the threat of disaster in the future due to indicators of ancient tsunami silt deposits on the south coast of Java and the Euro-Asian and Indo-Australian plates which have the potential to cause large earthquakes and trigger tsunami waves from the Sunda Strait to Bali [11]–[18] . In terms of seismotectonic zones in Indonesia, the coastal areas of Central Java and Yogyakarta are included in the zone B with an intensity of tsunami events of more than 2.5 times in a period of 30-50 years. In this zone, tsunamis are generated by two types of earthquakes, namely the subduction of the Indian Oce

FAQs

What data and indices are used to detect built-up land in tsunami hazard zones?

The model uses Sentinel-2A imagery and built-up indices including NBI, UI, NDBI, MBI, and IBI extracted from the imagery.

Which machine learning algorithms are applied and how is performance evaluated?

Random Forest and XGBoost are used for classification, while overall accuracy and Cohen’s Kappa measure the accuracy of classification and prediction.

How is the spatial distribution of built-up land predicted?

The spatial patterns are predicted using Ordinary Kriging (OK) based on the classified and extracted index data.

Aug 5, 2026

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