| アイテムタイプ |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2024-06-24 |
| 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 |
| タイトル |
|
|
タイトル |
Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Mangkhaseum, Sackdavong
Bhattarai, Yogesh
Duwal, Sunil
花沢, 明俊
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human and natural ecosystems in the Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing and machine learning techniques is proposed to address this issue by enhancing flood susceptibility understanding and informed decision-making. This study utilizes remote sensing geo-datasets and machine learning algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, and Long Short-Term Memory) to generate comprehensive flood susceptibility maps. The results highlight Random Forest’s superior performance, achieving the highest train and test Area Under the Curve of Receiver Operating Characteristic (AUROC) (1.00 and 0.993), accuracy (0.957), F1-score (0.962), and kappa value (0.914), with the lowest mean squared error (0.207) and Root Mean Squared Error (0.043). Vulnerability is particularly pronounced in low-elevation and low-slope southern downstream areas (Central part of Lao PDR). The results reveal that 36%–53% of the basin’s total area is highly susceptible to flooding, emphasizing the dire need for coordinated floodplain management strategies. This research uses freely accessible remote sensing data, addresses data scarcity in flood studies, and provides valuable insights for disaster risk management and sustainable planning in Lao PDR. |
|
言語 |
en |
| 書誌情報 |
en : Geomatics, Natural Hazards and Risk
巻 15,
号 1,
p. 2357650,
発行日 2024-05-28
|
| 出版社 |
|
|
出版者 |
Taylor & Francis |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1080/19475705.2024.2357650 |
| ISSN |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
1947-5705 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
1947-5713 |
| 著作権関連情報 |
|
|
権利情報 |
Copyright (c) 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Flood susceptibility modeling |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
machine learning algorithm |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
remote sensing |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Nam Ngum River Basin |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Laos |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 査読の有無 |
|
|
値 |
yes |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/348_ja.html |
| 論文ID（連携） |
|
|
値 |
10441355 |
| 連携ID |
|
|
値 |
12365 |
10441355.pdf (3 MB)
