Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.1016/j.envres.2019.108770 |
| Earth fissure hazard prediction using machine learning models | |
| Choubin, Bahram1; Mosavi, Amir2,3; Alamdarloo, Esmail Heydari4; Hosseini, Farzaneh Sajedi4; Shamshirband, Shahaboddin5,6; Dashtekian, Kazem7; Ghamisi, Pedram8 | |
| 通讯作者 | Shamshirband, Shahaboddin |
| 来源期刊 | ENVIRONMENTAL RESEARCH
 |
| ISSN | 0013-9351 |
| EISSN | 1096-0953 |
| 出版年 | 2019 |
| 卷号 | 179 |
| 英文摘要 | Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi-arid basins. The excessive withdrawal of groundwater, as well as the other underground natural resources, has been introduced as the significant causing of land subsidence and potentially, the earth fissuring. Fissuring is rapidly turning into the nations' major disasters which are responsible for significant economic, social, and environmental damages with devastating consequences. Modeling the earth fissure hazard is particularly important for identifying the vulnerable groundwater areas for the informed water management, and effectively enforce the groundwater recharge policies toward the sustainable conservation plans to preserve existing groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary involved to predict the earth fissures. This paper aims at proposing novel machine learning models for prediction of earth fissuring hazards. The Simulated annealing feature selection (SAFS) method was applied to identify key features, and the generalized linear model (GLM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and support vector machine (SVM) have been used for the first time to build the prediction models. Results indicated that all the models had good accuracy ( > 86%) and precision ( > 81%) in the prediction of the earth fissure hazard. The GLM model (as a linear model) had the lowest performance, while the RF model was the best model in the modeling process. Sensitivity analysis indicated that the hazardous class in the study area was mainly related to low elevations with characteristics of high groundwater withdrawal, drop in groundwater level, high well density, high road density, low precipitation, and Quaternary sediments distribution. |
| 英文关键词 | Hazard prediction Geohazard Earth fissure Machine learning |
| 类型 | Article |
| 语种 | 英语 |
| 国家 | Iran ; England ; Hungary ; Vietnam ; Germany |
| 收录类别 | SCI-E ; SSCI |
| WOS记录号 | WOS:000497259100008 |
| WOS关键词 | LAND SUBSIDENCE ; GROUND FISSURES ; REGRESSION TREES ; RISK-ASSESSMENT ; NORTH CHINA ; CLASSIFICATION ; AREA ; WATER ; SUSCEPTIBILITY ; MANAGEMENT |
| WOS类目 | Environmental Sciences ; Public, Environmental & Occupational Health |
| WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health |
| EI主题词 | 2019-12-01 |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/311424 |
| 作者单位 | 1.AREEO, Soil Conservat & Watershed Management Res Dept, West Azarbaijan Agr & Nat Resources Res & Educ Ct, Orumiyeh, Iran; 2.Oxford Brookes Univ, Sch Built Environm, Oxford OX3 0BP, England; 3.Obuda Univ, Kalman Kando Fac Elect Engn, Budapest, Hungary; 4.Univ Tehran, Fac Nat Resources, Dept Reclamat Arid & Mt Reg, Karaj, Iran; 5.Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam; 6.Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam; 7.AREEO, Yazd Agr & Nat Resources Res Ctr, Yazd, Iran; 8.Helmholtz Zentrum Dresden Rossendorf, Helmholtz Inst Freiberg Resource Technol, Explorat Devis, Freiberg, Germany |
| 推荐引用方式 GB/T 7714 | Choubin, Bahram,Mosavi, Amir,Alamdarloo, Esmail Heydari,et al. Earth fissure hazard prediction using machine learning models[J],2019,179. |
| APA | Choubin, Bahram.,Mosavi, Amir.,Alamdarloo, Esmail Heydari.,Hosseini, Farzaneh Sajedi.,Shamshirband, Shahaboddin.,...&Ghamisi, Pedram.(2019).Earth fissure hazard prediction using machine learning models.ENVIRONMENTAL RESEARCH,179. |
| MLA | Choubin, Bahram,et al."Earth fissure hazard prediction using machine learning models".ENVIRONMENTAL RESEARCH 179(2019). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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