Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami
AbstractIn Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2022.2147455 |
| _version_ | 1797389231679602688 |
|---|---|
| author | Riantini Virtriana Agung Budi Harto Fiza Wira Atmaja Irwan Meilano Kamal Nur Fauzan Tania Septi Anggraini Kalingga Titon Nur Ihsan Fatwa Cahya Mustika Wulan Suminar |
| author_facet | Riantini Virtriana Agung Budi Harto Fiza Wira Atmaja Irwan Meilano Kamal Nur Fauzan Tania Septi Anggraini Kalingga Titon Nur Ihsan Fatwa Cahya Mustika Wulan Suminar |
| author_sort | Riantini Virtriana |
| collection | DOAJ |
| description | AbstractIn Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas. |
| first_indexed | 2024-03-08T22:52:52Z |
| format | Article |
| id | doaj.art-c17e42ed001443dc9407f695d5469811 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-03-08T22:52:52Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-c17e42ed001443dc9407f695d54698112023-12-16T08:49:46ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132023-12-01141285110.1080/19475705.2022.2147455Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunamiRiantini Virtriana0Agung Budi Harto1Fiza Wira Atmaja2Irwan Meilano3Kamal Nur Fauzan4Tania Septi Anggraini5Kalingga Titon Nur Ihsan6Fatwa Cahya Mustika7Wulan Suminar8Remote Sensing and GIS Research Group, Faculty of Earth Science and Technology, Institut Teknologi Bandung, Bandung, IndonesiaRemote Sensing and GIS Research Group, Faculty of Earth Science and Technology, Institut Teknologi Bandung, Bandung, IndonesiaDoctoral Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, IndonesiaDepartment of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, IndonesiaMaster Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, IndonesiaDoctoral Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, IndonesiaDoctoral Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, IndonesiaMaster Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, IndonesiaDoctoral Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, IndonesiaAbstractIn Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas.https://www.tandfonline.com/doi/10.1080/19475705.2022.2147455Building damage detectionmachine learningrandom forestremote sensingtsunami |
| spellingShingle | Riantini Virtriana Agung Budi Harto Fiza Wira Atmaja Irwan Meilano Kamal Nur Fauzan Tania Septi Anggraini Kalingga Titon Nur Ihsan Fatwa Cahya Mustika Wulan Suminar Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami Geomatics, Natural Hazards & Risk Building damage detection machine learning random forest remote sensing tsunami |
| title | Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami |
| title_full | Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami |
| title_fullStr | Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami |
| title_full_unstemmed | Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami |
| title_short | Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami |
| title_sort | machine learning remote sensing using the random forest classifier to detect the building damage caused by the anak krakatau volcano tsunami |
| topic | Building damage detection machine learning random forest remote sensing tsunami |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2022.2147455 |
| work_keys_str_mv | AT riantinivirtriana machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT agungbudiharto machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT fizawiraatmaja machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT irwanmeilano machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT kamalnurfauzan machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT taniaseptianggraini machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT kalinggatitonnurihsan machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT fatwacahyamustika machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami AT wulansuminar machinelearningremotesensingusingtherandomforestclassifiertodetectthebuildingdamagecausedbytheanakkrakatauvolcanotsunami |