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Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang

Landslides are one of the major geological phenomena that is widespread across the globe and have caused destructive outcomes to human life and the overall economic system. Tedious work is required to conventionally collect all evidence of multiple sizes of landslide occurrences in such a huge, deve...

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Main Authors: Nur Aqilah, Yusri, Syarifuddin, Misbari, Izza Wajihah, Ismail, Anak Gisen, Jacqueline Isabella
Format: Conference or Workshop Item
Language:English
Published: IOP Publishing 2024
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/41691/1/Satellite-based%20landslide%20distribution%20mapping%20with%20the%20adoption%20of%20deep.pdf
http://umpir.ump.edu.my/id/eprint/41691/
https://doi.org/10.1088/1755-1315/1296/1/012014
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spelling my.ump.umpir.416912024-07-31T03:23:41Z http://umpir.ump.edu.my/id/eprint/41691/ Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang Nur Aqilah, Yusri Syarifuddin, Misbari Izza Wajihah, Ismail Anak Gisen, Jacqueline Isabella T Technology (General) TA Engineering (General). Civil engineering (General) Landslides are one of the major geological phenomena that is widespread across the globe and have caused destructive outcomes to human life and the overall economic system. Tedious work is required to conventionally collect all evidence of multiple sizes of landslide occurrences in such a huge, developing city, including the Kuantan River Basin (KRB). In fact, landslides are difficult to identify in remote areas, such as in thick and mountainous areas, if no aerial devices or sensor technology is provided at the incident area. Ironically, the landslide distribution map is a useful tool that helps in staging the landslide mitigation plan for landslide-prone areas. Thus, the objectives of this study are (i) to identify landslide events using deep learning and vegetation index approaches on optical satellite data; and (ii) to develop landslide distribution mapping in KRB using the best approach. Remotely sensed optical images of Landsat 8 OLI and Worldview-2 were used to map the landslide distribution and study the spectral pattern of the landslide area. Normalized Difference Vegetation Index (NDVI) were generated for two consecutive years, which is from the year 2022 to 2023. Spectral bands in red and infrared are used to generate the NDVI for visual interpretation of landslide occurrences. The deep learning based on Convolutional Neural Network (CNN) model were used for the pixel classification process. The main output of this study would be a landslide distribution map for the KRB area with high accuracy. The result has also been verified using drone monitoring at the incident sites, which was able to improve landslide detection in tropical environments. Landslide distribution maps accuracy was measured by using the ROC-AUC method, the map accuracy is 88.9%. This map should help the government and private sector plan for the city's urban development and provide proper planning for geohazard mitigation. An accurate landslide distribution map could be a source of reference for the National Disaster Management Authority (NADMA) for a quick rescue during emergency. IOP Publishing 2024 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/41691/1/Satellite-based%20landslide%20distribution%20mapping%20with%20the%20adoption%20of%20deep.pdf Nur Aqilah, Yusri and Syarifuddin, Misbari and Izza Wajihah, Ismail and Anak Gisen, Jacqueline Isabella (2024) Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang. In: IOP Conf. Series : Earth and Environmental Science. World Sustainable Construction Conference 2023 , 13 - 14 October 2023 , Kuala Lumpur, Malaysia. pp. 1-20., 1296 (012014). ISSN 1755-1315 (Published) https://doi.org/10.1088/1755-1315/1296/1/012014
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
Nur Aqilah, Yusri
Syarifuddin, Misbari
Izza Wajihah, Ismail
Anak Gisen, Jacqueline Isabella
Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang
description Landslides are one of the major geological phenomena that is widespread across the globe and have caused destructive outcomes to human life and the overall economic system. Tedious work is required to conventionally collect all evidence of multiple sizes of landslide occurrences in such a huge, developing city, including the Kuantan River Basin (KRB). In fact, landslides are difficult to identify in remote areas, such as in thick and mountainous areas, if no aerial devices or sensor technology is provided at the incident area. Ironically, the landslide distribution map is a useful tool that helps in staging the landslide mitigation plan for landslide-prone areas. Thus, the objectives of this study are (i) to identify landslide events using deep learning and vegetation index approaches on optical satellite data; and (ii) to develop landslide distribution mapping in KRB using the best approach. Remotely sensed optical images of Landsat 8 OLI and Worldview-2 were used to map the landslide distribution and study the spectral pattern of the landslide area. Normalized Difference Vegetation Index (NDVI) were generated for two consecutive years, which is from the year 2022 to 2023. Spectral bands in red and infrared are used to generate the NDVI for visual interpretation of landslide occurrences. The deep learning based on Convolutional Neural Network (CNN) model were used for the pixel classification process. The main output of this study would be a landslide distribution map for the KRB area with high accuracy. The result has also been verified using drone monitoring at the incident sites, which was able to improve landslide detection in tropical environments. Landslide distribution maps accuracy was measured by using the ROC-AUC method, the map accuracy is 88.9%. This map should help the government and private sector plan for the city's urban development and provide proper planning for geohazard mitigation. An accurate landslide distribution map could be a source of reference for the National Disaster Management Authority (NADMA) for a quick rescue during emergency.
format Conference or Workshop Item
author Nur Aqilah, Yusri
Syarifuddin, Misbari
Izza Wajihah, Ismail
Anak Gisen, Jacqueline Isabella
author_facet Nur Aqilah, Yusri
Syarifuddin, Misbari
Izza Wajihah, Ismail
Anak Gisen, Jacqueline Isabella
author_sort Nur Aqilah, Yusri
title Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang
title_short Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang
title_full Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang
title_fullStr Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang
title_full_unstemmed Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang
title_sort satellite-based landslide distribution mapping with the adoption of deep learning approach in the kuantan river basin, pahang
publisher IOP Publishing
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41691/1/Satellite-based%20landslide%20distribution%20mapping%20with%20the%20adoption%20of%20deep.pdf
http://umpir.ump.edu.my/id/eprint/41691/
https://doi.org/10.1088/1755-1315/1296/1/012014
_version_ 1822924554576592896
score 13.232432

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