| アイテムタイプ |
共通アイテムタイプ(1) |
| 公開日 |
2025-02-17 |
| タイトル |
|
|
タイトル |
Harnessing multisatellite remote sensing data and machine learning for flood risk assessment in Nam Ngum River Basin, Lao PDR |
|
言語 |
en |
| その他のタイトル |
|
|
その他のタイトル |
Harnessing Multi-satellite Remote Sensing Data and Machine Learning for Flood Risk Assessment in Nam Ngum River Basin, Lao PDR |
|
言語 |
en |
| 著者 |
Mangkhaseum, Sackdavong
Duwal, Sunil
Bhattarai, Yogesh
花沢, 明俊
|
| 著作権関連情報 |
|
|
権利情報 |
Copyright (c) 2024 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The study focuses on flood susceptibility in the Nam Ngum River Basin, Lao PDR, an area prone to annual flooding due to monsoons and rainstorms. Flooding in this region significantly threatens human life, causes economic losses, and damages communities and agriculture. The study employs advanced remote sensing and machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), to address these issues and create detailed flood susceptibility maps. The machine learning models used historical flood data, Sentinel-1 SAR imagery from 2018 to 2020, and open-source flood data for training and validation. Eleven flood factors were considered. With 776 samples, 70% were trained, and 30% tested the model. Flood susceptibility map accuracy is assessed using statistical techniques such as multicollinearity, Kappa index, and Area Under the curve of Receiver Operating Characteristics (AUROC). The generated flood susceptibility map is used to analyze the possible effect on the different land use/land cover classes and populations. RF outperforms SVM and ANN, achieving higher accuracy based on Receiver Operating Characteristics. The resulting flood susceptibility map reveals that 25-44% of the basin area is highly susceptible, predominantly in low-elevation and low-slope regions. Likewise, 85 to 90% of the people are highly vulnerable to flooding within 260 to 280 km2 of built-up area. The study proposes a new approach to using machine learning and readily available remote sensing data for flood susceptibility mapping. The findings of this study provide essential insights for policymakers, aiding in disaster risk reduction and facilitating sustainable development planning in Lao PDR. |
|
言語 |
en |
| 備考 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
SPIE Future Sensing Technologies 2024, April 22 - 24, 2024, Yokohama, Japan |
|
言語 |
en |
| 書誌情報 |
en : Proceedings of SPIE
巻 13083,
発行日 2024-05-28
|
| 出版社 |
|
|
出版者 |
Society of Photo-Optical Instrumentation Engineers |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1117/12.3022819 |
| ISSN |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
0277-786X |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
1996-756X |
| 会議記述 |
|
|
|
会議名 |
SPIE Future Sensing Technologies 2024 |
|
|
言語 |
en |
|
|
開始年 |
2024 |
|
|
開始月 |
04 |
|
|
開始日 |
22 |
|
|
終了年 |
2024 |
|
|
終了月 |
04 |
|
|
終了日 |
24 |
|
開催国 |
JPN |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/348_ja.html |
| 論文ID（連携） |
|
|
値 |
10444313 |
| 連携ID |
|
|
値 |
12606 |
10444313.pdf (11.7 MB)
