Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments
文献类型：期刊论文
| 作者 | Tang, Xianzhe1,2; Wu, Zhanyu2; Liu, Wei3,4; Tian, Juwei1; Liu, Luo1 |
| 刊名 | JOURNAL OF ENVIRONMENTAL MANAGEMENT
 |
| 出版日期 | 2023-10-15 |
| 卷号 | 344页码:9 |
| ISSN号 | 0301-4797 |
| 关键词 | Urban waterlogging Imbalanced class Machine learning SMOTE OSSA |
| DOI | 10.1016/j.jenvman.2023.118682 |
| 通讯作者 | Liu, Wei(liuwei6010@igsnrr.ac.cn) ; Liu, Luo(liuluo@scau.edu.cn) |
| 英文摘要 | Machine learning (ML)-based urban waterlogging susceptibility studies suffer from class imbalance, as fewer positive samples are generally available than potential negative samples. Few studies have considered optimizing the results by improving the quality of training samples. To address this issue, we explored effective approaches to reliably increase the numbers of positive samples for such studies. The Synthetic Minority Over-Sampling Technique (SMOTE) and Optimized Seed Spread Algorithm (OSSA), representative of oversampling (synthesizing new samples based on the feature space) and physical (simulating potential inundated area based on the mechanisms of water flow) approaches, respectively, were employed to increase the number of positive samples. Waterlogging in Shenzhen was selected as a case study using eight selected spatial variables. An elaborate experiment was conducted to compare the quality of added samples based on the classifiers' performance and accuracy of waterlogging susceptibility maps (WSMs). The results indicated that (1) the performance of classifiers generated with SMOTE was worse than the original samples, while the use of OSSA improved the trained classifiers, and (2) the accuracy of WSMs was not improved with SMOTE but increased markedly with OSSA. These results may be driven by the diversity of information and features of the added samples. This study indicates the use of SMOTE fails to synthesize reliable samples when applied to waterlogging analysis in Shenzhen, whereas an effective solution for generating reliable positive samples is to use OSSA that simulates the potential submerged regions based on the mechanisms of disaster occurrence and spread. |
| WOS关键词 | SMOTE ; SIMULATION ; EXPANSION ; MODEL ; SWMM ; PSO |
| 资助项目 | National Key Research and Development Program of China[2020YFD1100203] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:001144123700001 |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202158]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Wei; Liu, Luo |
| 作者单位 | 1.South China Agr Univ, Guangdong Prov Key Lab Land Use & Consolidat, Guangzhou 510642, Peoples R China 2.South China Agr Univ, Joint Inst Environm & Educ, Coll Nat Resources & Environm, Guangzhou 510642, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tang, Xianzhe,Wu, Zhanyu,Liu, Wei,et al. Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2023,344:9. |
| APA | Tang, Xianzhe,Wu, Zhanyu,Liu, Wei,Tian, Juwei,&Liu, Luo.(2023).Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments.JOURNAL OF ENVIRONMENTAL MANAGEMENT,344,9. |
| MLA | Tang, Xianzhe,et al."Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments".JOURNAL OF ENVIRONMENTAL MANAGEMENT 344(2023):9. |
入库方式： OAI收割
来源：地理科学与资源研究所
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