Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan
文献类型：期刊论文
| 作者 | Ali, Nafees6,7,8,9; Chen, Jian6,7,8,9; Fu, Xiaodong6,7,8,9; Ali, Rashid1; Hussain, Muhammad Afaq2; Daud, Hamza3; Hussain, Javid6,7,8,9; Altalbe, Ali4,5 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 16期号:6页码:27 |
| 关键词 | landslide susceptibility mapping machine learning baseline learning algorithms ensemble learning algorithms |
| DOI | 10.3390/rs16060988 |
| 英文摘要 | Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales. |
| 资助项目 | Prince Sattam bin Abdulaziz University ; [PSAU/2024/01/78918] ; [2021-PhD] ; [2023-PhD] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001192966700001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40943]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Chen, Jian |
| 作者单位 | 1.Zhejiang Normal Univ, Sch Math Sci, Jinhua 321004, Peoples R China 2.China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China 3.China Univ Geosci, Badong Natl Observat & Res Stn Geohazards, Wuhan 430074, Peoples R China 4.Prince Sattam Bin Abdulaziz Univ, Dept Comp Sci, Al Kharj 11942, Saudi Arabia 5.King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia 6.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 8.China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan 9.Hubei Key Lab Geoenvironm Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ali, Nafees,Chen, Jian,Fu, Xiaodong,et al. Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan[J]. REMOTE SENSING,2024,16(6):27. |
| APA | Ali, Nafees.,Chen, Jian.,Fu, Xiaodong.,Ali, Rashid.,Hussain, Muhammad Afaq.,...&Altalbe, Ali.(2024).Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan.REMOTE SENSING,16(6),27. |
| MLA | Ali, Nafees,et al."Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan".REMOTE SENSING 16.6(2024):27. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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