Climate Change Data Portal
| DOI | 10.3390/ijgi13050161 |
| Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models | |
| 发表日期 | 2024 |
| EISSN | 2220-9964 |
| 起始页码 | 13 |
| 结束页码 | 5 |
| 卷号 | 13期号:5 |
| 英文摘要 | The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), groundwater depth, digital terrain model (DTM), normalized vegetation index (NDVI), geology, soil composition, distance to roads, and distance to rivers and streams. The dataset includes 2011 points referenced from the Persistent Scattering SAR Interferometry (PSI) method, of which 1011 points are subsidence points and the remaining are non-subsidence points. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), specificity, sensitivity, and overall accuracy (ACC). The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC and ACC above 0.88 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau Peninsula. In addition, 31 subsidence points measured by leveling surveys between 2005 and 2020, provided by the Department of Survey, Mapping and Geographic Information Vietnam, were used for validating the land subsidence susceptibility from the XGB method. The findings indicate a 70.9% accuracy rate in predicting subsidence susceptibility compared to the leveling measurement points. |
| 英文关键词 | AdaBoost; Gradient Boosting; XGBoost; Ca Mau; subsidence susceptibility |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| WOS类目 | Computer Science, Information Systems ; Geography, Physical ; Remote Sensing |
| WOS记录号 | WOS:001233032900001 |
| 来源期刊 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/300475 |
| 作者单位 | Hanoi University of Mining & Geology; Hanoi University of Mining & Geology; Polytechnic University of Milan; Vietnam Academy of Science & Technology (VAST); University of Science & Technology of Hanoi (USTH) |
| 推荐引用方式 GB/T 7714 | . Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models[J],2024,13(5). |
| APA | (2024).Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,13(5). |
| MLA | "Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 13.5(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
