Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models
The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and in...
Uložené v:
| Vydané v: | International journal of coal science & technology Ročník 10; číslo 1; s. 18 - 14 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Singapore
Springer Nature Singapore
01.12.2023
Springer Nature B.V SpringerOpen |
| Predmet: | |
| ISSN: | 2095-8293, 2198-7823 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues can affect the accuracy of slope stability prediction. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. Taking the Ganzhou City in China as the study area, the landslide inventory and their characteristics of geotechnical parameters, slope height and slope angle are analyzed. Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. Five control factors affecting slope stability, including slope height, slope angle, internal friction angle, cohesion and volumetric weight, are selected to form different slope and construct model input variables. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. Each slope stability coefficient and its corresponding control factors is a slope sample. As a result, a total of 2160 training samples and 450 testing samples are constructed. These sample sets are imported into LSTM for modelling and compared with the support vector machine (SVM), random forest (RF) and convolutional neural network (CNN). The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models. |
|---|---|
| AbstractList | The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues can affect the accuracy of slope stability prediction. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. Taking the Ganzhou City in China as the study area, the landslide inventory and their characteristics of geotechnical parameters, slope height and slope angle are analyzed. Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. Five control factors affecting slope stability, including slope height, slope angle, internal friction angle, cohesion and volumetric weight, are selected to form different slope and construct model input variables. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. Each slope stability coefficient and its corresponding control factors is a slope sample. As a result, a total of 2160 training samples and 450 testing samples are constructed. These sample sets are imported into LSTM for modelling and compared with the support vector machine (SVM), random forest (RF) and convolutional neural network (CNN). The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models. Abstract The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues can affect the accuracy of slope stability prediction. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. Taking the Ganzhou City in China as the study area, the landslide inventory and their characteristics of geotechnical parameters, slope height and slope angle are analyzed. Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. Five control factors affecting slope stability, including slope height, slope angle, internal friction angle, cohesion and volumetric weight, are selected to form different slope and construct model input variables. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. Each slope stability coefficient and its corresponding control factors is a slope sample. As a result, a total of 2160 training samples and 450 testing samples are constructed. These sample sets are imported into LSTM for modelling and compared with the support vector machine (SVM), random forest (RF) and convolutional neural network (CNN). The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models. |
| ArticleNumber | 18 |
| Author | Huang, Faming Catani, Filippo Lv, Zhitao Chang, Zhilu Chen, Shixuan Huang, Jinsong Xiong, Haowen |
| Author_xml | – sequence: 1 givenname: Faming surname: Huang fullname: Huang, Faming organization: School of Civil Engineering and Architecture, Nanchang University – sequence: 2 givenname: Haowen surname: Xiong fullname: Xiong, Haowen organization: School of Civil Engineering and Architecture, Nanchang University – sequence: 3 givenname: Shixuan surname: Chen fullname: Chen, Shixuan organization: School of Civil Engineering and Architecture, Nanchang University – sequence: 4 givenname: Zhitao surname: Lv fullname: Lv, Zhitao email: lvzhitao@ncu.edu.cn organization: School of Civil Engineering and Architecture, Nanchang University – sequence: 5 givenname: Jinsong surname: Huang fullname: Huang, Jinsong organization: Discipline of Civil, Surveying and Environmental Engineering, Priority Research Centre for Geotechnical Science and Engineering, University of Newcastle – sequence: 6 givenname: Zhilu surname: Chang fullname: Chang, Zhilu organization: School of Civil Engineering and Architecture, Nanchang University – sequence: 7 givenname: Filippo surname: Catani fullname: Catani, Filippo organization: Department of Geosciences, University of Padova |
| BookMark | eNp9UU1v1DAQjVCRKKV_gJMlroTajp3Y3FBFoVIlDsDZmtiTXS9JHGxvq_1R_Ee8G1QQh15mRqP33ny8l9XZHGasqteMvmOUdldJ0E7pmvKmplR2uhbPqnPOtKo7xZuzUlMta8V186K6TGlHKWVCc8HEefXr6xgWJClD70efD2SJ6LzNPsykh4SOlALIGOYNSdsQc50xTmTCKcQDmXEfYSwpP4T44z2xYVog-hTmRB583pbGfB_G_VHuhPsXnt6StF-Woknu0eYQyQR262dMBGZHYglhIkOImDKZgsMxvaqeDzAmvPyTL6rvNx-_XX-u7758ur3-cFdbwXWuOeq2k71zLdOAsm2RtQDOaWToYOC276W0UkrFse3btuu5slqAAolSOtFcVLerrguwM0v0E8SDCeDNqRHixkDM3o5oaAdccdaJpuGCqgEUG3rtFJOi1WpwRevNqrXE8HNfbjG7sI_lHclwpWkjixVNQfEVZWNIKeLwOJVRc3TZrC6b4rI5uWyOa6r_SNZnOD47R_Dj09RmpaYyZ95g_LvVE6zfTMLCzg |
| CitedBy_id | crossref_primary_10_1016_j_cscm_2024_e04112 crossref_primary_10_3390_su15118835 crossref_primary_10_1038_s41598_024_69271_0 crossref_primary_10_3390_electronics14010126 crossref_primary_10_1007_s11069_025_07665_7 crossref_primary_10_1038_s41598_024_68704_0 crossref_primary_10_1007_s12145_024_01600_3 crossref_primary_10_1038_s41598_025_02501_1 crossref_primary_10_1007_s40808_024_02221_x crossref_primary_10_1016_j_jobe_2024_110417 crossref_primary_10_1155_je_6652758 crossref_primary_10_1007_s11069_024_06839_z crossref_primary_10_1007_s12145_024_01348_w crossref_primary_10_1016_j_enggeo_2025_108219 crossref_primary_10_1186_s40677_024_00290_9 crossref_primary_10_3390_app15169158 crossref_primary_10_1038_s41598_024_72588_5 crossref_primary_10_1007_s40789_025_00790_5 crossref_primary_10_3390_w16152152 crossref_primary_10_1016_j_eswa_2023_122400 crossref_primary_10_1016_j_mtcomm_2024_109222 crossref_primary_10_1007_s00521_024_10471_0 crossref_primary_10_1007_s12145_024_01296_5 crossref_primary_10_1080_15376494_2024_2439557 crossref_primary_10_3390_su16156333 crossref_primary_10_1007_s10462_024_10836_w crossref_primary_10_1007_s40996_025_02027_6 crossref_primary_10_1016_j_infrared_2023_105084 crossref_primary_10_3390_machines12040212 crossref_primary_10_1007_s10064_025_04281_4 crossref_primary_10_1007_s12517_024_12146_5 crossref_primary_10_1038_s41598_024_71367_6 crossref_primary_10_3390_app13106224 crossref_primary_10_3390_w16010143 crossref_primary_10_3390_pr13082610 crossref_primary_10_1080_17499518_2024_2356543 crossref_primary_10_1007_s00603_025_04567_9 crossref_primary_10_1007_s40571_025_01015_x crossref_primary_10_1038_s41598_025_07494_5 crossref_primary_10_1007_s41939_024_00513_4 crossref_primary_10_1007_s11356_025_36406_3 crossref_primary_10_1177_17483026241309070 crossref_primary_10_1007_s40789_024_00678_w crossref_primary_10_1016_j_desal_2025_118880 crossref_primary_10_1007_s12665_025_12414_x crossref_primary_10_1007_s42452_025_06498_0 crossref_primary_10_1007_s40808_025_02454_4 crossref_primary_10_1016_j_envres_2023_117286 crossref_primary_10_1016_j_petsci_2025_09_020 crossref_primary_10_1007_s11069_024_06490_8 crossref_primary_10_1007_s42461_025_01344_8 crossref_primary_10_3390_app14156526 crossref_primary_10_1016_j_heliyon_2024_e36841 crossref_primary_10_1016_j_heliyon_2024_e35871 crossref_primary_10_1038_s41598_025_86989_7 crossref_primary_10_1016_j_aej_2025_06_023 crossref_primary_10_1007_s12145_024_01464_7 crossref_primary_10_1038_s43247_024_01602_5 crossref_primary_10_1007_s12145_024_01653_4 crossref_primary_10_1007_s00477_024_02745_9 crossref_primary_10_1007_s10064_024_03896_3 crossref_primary_10_1016_j_eswa_2024_125801 crossref_primary_10_1007_s40515_025_00621_9 crossref_primary_10_1007_s00521_025_11321_3 crossref_primary_10_1007_s13202_024_01761_3 crossref_primary_10_1038_s41598_024_64030_7 crossref_primary_10_1016_j_geoai_2025_100030 crossref_primary_10_1007_s42107_023_00704_3 crossref_primary_10_1016_j_enggeo_2025_108228 crossref_primary_10_1007_s11831_023_10024_z crossref_primary_10_1016_j_engeos_2024_100300 crossref_primary_10_2478_amns_2024_3421 crossref_primary_10_1007_s12145_024_01550_w crossref_primary_10_1186_s40677_024_00288_3 crossref_primary_10_1016_j_infrared_2023_104968 crossref_primary_10_3390_drones9090603 crossref_primary_10_1007_s10706_025_03091_5 crossref_primary_10_1007_s00477_024_02792_2 crossref_primary_10_1007_s12145_025_01899_6 crossref_primary_10_3390_s23167296 crossref_primary_10_1016_j_ress_2025_110813 |
| Cites_doi | 10.1109/access.2019.2912419 10.1108/rpj-03-2016-0041 10.1142/s0219530518500124 10.1023/a:1022627411411 10.1186/s12874-019-0863-0 10.1016/j.sandf.2018.10.008 10.1007/s00366-015-0400-7 10.1007/s11069-020-04141-2 10.1007/s10346-020-01473-9 10.1002/nag.2834 10.1016/j.enggeo.2019.105278 10.3390/s20236854 10.1007/s12583-021-1433-z 10.1016/j.jrmge.2022.07.009 10.1016/j.apm.2017.03.048 10.1038/s41598-019-56309-x 10.1016/j.enconman.2017.11.053 10.1007/s11263-019-01228-7 10.1109/TNSRE.2018.2876129 10.1186/s13638-017-0993-1 10.1007/s12583-020-1331-9 10.1016/j.energy.2019.116319 10.1007/s12583-021-1492-1 10.1007/s00500-018-3253-3 10.1080/19475705.2014.883440 10.1016/j.neucom.2018.08.067 10.1155/2020/8862243 10.3390/ijgi8090395 10.1007/s12583-020-1380-0 10.1155/2019/9476981 10.1007/s00366-019-00702-7 10.1007/s12583-020-1072-9 10.1007/s40948-020-00154-0 10.1007/s00521-019-04650-7 10.1007/s40789-022-00483-3 10.1007/s10064-020-01903-x 10.3390/app9214638 10.3390/ijgi9090539 10.1061/(asce)cp.1943-5487.0000739 10.1016/j.asoc.2017.07.011 10.3390/rs12244075 10.1016/j.gsf.2014.10.003 10.1016/j.neunet.2014.09.003 10.1007/s12517-021-06616-3 10.1007/s40789-022-00504-1 10.3390/rs14184436 10.1016/j.ssci.2019.05.046 10.1088/1748-9326/abe1f5 10.1109/access.2018.2843787 10.1016/j.geomorph.2022.108236 10.1177/0037549720943274 10.3389/feart.2021.731058 10.1007/s10346-019-01148-0 10.1007/s12665-014-3800-x 10.1680/jgele.18.00022 10.3390/su12083269 10.1080/19386362.2017.1305652 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2023 – notice: The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION ABUWG AEUYN AFKRA AZQEC BENPR BHPHI BKSAR CCPQU DWQXO HCIFZ PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS DOA |
| DOI | 10.1007/s40789-023-00579-4 |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Natural Science Collection ProQuest SciTech Premium Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection ProQuest One ProQuest Central Korea SciTech Premium Collection Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Sustainability ProQuest One Academic UKI Edition Natural Science Collection ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2198-7823 |
| EndPage | 14 |
| ExternalDocumentID | oai_doaj_org_article_07a282174332408fa81fb9d8154698fd 10_1007_s40789_023_00579_4 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 41807285 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | -02 -0B -SB -S~ 0R~ 4.4 5VR 92M 9D9 9DB AAFWJ AAKKN AAXDM ABEEZ ACACY ACGFS ACULB ADINQ ADMLS AEUYN AFGXO AFKRA AFPKN AFUIB AHBYD AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP ASPBG AVWKF BAPOH BENPR BHPHI BKSAR C24 C6C CAJEB CCEZO CCPQU CDRFL CHBEP EBS EJD FA0 GROUPED_DOAJ HCIFZ IAO IPNFZ ISR ITC JUIAU OK1 PCBAR PIMPY Q-- R-B RIG RSV RT2 SOJ T8R U1F U1G U5B U5L ~LW AAYXX ABJIA AFFHD CITATION PHGZM PHGZT ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c429t-2e9675bdd619ae566e16aadd9e1edaf2cbb55c55582e6b667b28c94a8a5e55d43 |
| IEDL.DBID | C24 |
| ISICitedReferencesCount | 97 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000966734600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2095-8293 |
| IngestDate | Mon Nov 10 04:36:08 EST 2025 Wed Oct 08 14:21:26 EDT 2025 Sat Nov 29 02:30:50 EST 2025 Tue Nov 18 21:22:34 EST 2025 Fri Feb 21 02:44:29 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Long short-term memory Machine learning model Slope stability prediction Geo-Studio software |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c429t-2e9675bdd619ae566e16aadd9e1edaf2cbb55c55582e6b667b28c94a8a5e55d43 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://link.springer.com/10.1007/s40789-023-00579-4 |
| PQID | 2890354923 |
| PQPubID | 2044255 |
| PageCount | 14 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_07a282174332408fa81fb9d8154698fd proquest_journals_2890354923 crossref_primary_10_1007_s40789_023_00579_4 crossref_citationtrail_10_1007_s40789_023_00579_4 springer_journals_10_1007_s40789_023_00579_4 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-12-01 |
| PublicationDateYYYYMMDD | 2023-12-01 |
| PublicationDate_xml | – month: 12 year: 2023 text: 2023-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Singapore |
| PublicationPlace_xml | – name: Singapore – name: Heidelberg |
| PublicationTitle | International journal of coal science & technology |
| PublicationTitleAbbrev | Int J Coal Sci Technol |
| PublicationYear | 2023 |
| Publisher | Springer Nature Singapore Springer Nature B.V SpringerOpen |
| Publisher_xml | – name: Springer Nature Singapore – name: Springer Nature B.V – name: SpringerOpen |
| References | Wang, Xu, Gui, Yang, Pu (CR44) 2020; 12 Huang, Cao, Jiang, Zhou, Huang, Guo (CR14) 2020; 17 Tian, Zhang, Li, Lin, Yang (CR41) 2018; 318 Yang, Wu, Zheng (CR52) 2020; 80 Yin, Lin, Chen, Wang, Zhao (CR54) 2020; 96 Liu, Mi, Li (CR29) 2018; 156 Wu, Fang, Kang, Tao, Qiao (CR47) 2019; 9 Cortes, Vapnik (CR7) 1995; 20 Kumar, Basudhar (CR23) 2018; 8 Chen, Cui, Chen, Yuan, Kang, Zhu (CR6) 2021; 16 Kang, Xu, Li, Zhao (CR21) 2017; 60 Bui, Moayedi, Gör, Jaafari, Foong (CR4) 2019; 8 Kumar, Samui, Naithani (CR24) 2014; 7 Yang, Sun, Sun, Zheng (CR51) 2019; 261 Huang, Yin, He, Zhou, Zhang (CR13) 2016; 23 Yang, Zhang, Wang, Zhang (CR50) 2019; 2019 Amin, Sharif, Raza, Saba, Sial, Shad (CR3) 2019; 32 Ray, Kumar, Kumar, Rai, Khandelwal, Singh (CR35) 2020; 103 Gao, Raftari, Rashid, Mu’azu, Jusoh (CR10) 2019; 36 Xie, Zhou, Chai (CR49) 2019; 7 Zhang, Goh (CR55) 2016; 7 Lin, Zhou, Li (CR28) 2018; 6 Zhou (CR57) 2018; 16 He, Xu, Qi, Huang, Cheng, Xu, Yao, Lu, Dai (CR12) 2021; 32 Gordan, Armaghani, Hajihassani, Monjezi (CR11) 2016; 32 Schmidhuber (CR37) 2015; 61 Zhou, Li, Yang, Wang, Shi, Yao, Mitri (CR58) 2019; 118 Koopialipoor, Jahed Armaghani, Hedayat, Marto, Gordan (CR22) 2018; 23 Tan, Yu, Jiao, Lin, Lv, Cheng (CR40) 2021; 14 Huang, Xu, Zhang, Xue, Wang (CR16) 2021; 32 Wang, Wang, Zhu, Zhang (CR45) 2020; 6 Huang, Tao, Li, Lian, Catani, Huang, Li, Zhang (CR18) 2022; 14 Moayedi, Bui, Kalantar, Foong (CR31) 2019; 9 Jiang, Huang, Yao, Yang (CR19) 2017; 47 Liang, Sun, Sun, Gao (CR27) 2017; 2017 Akram, Li, Jin, Chen, Zhu, Zhao, Khaliq, Faheem, Ahmad (CR2) 2019; 189 Dai, Li, Wang, Lu, Xu, Jian (CR9) 2021; 32 Wang, Jiang, Liu, Shang, Zhang (CR43) 2018; 26 Wongvibulsin, Wu, Zeger (CR46) 2019; 20 Palenzuela Baena, Scifoni, Marsella, De Astis, Irigaray Fernández (CR32) 2019; 16 Tinoco, Gomes Correia, Cortez, Toll (CR42) 2018; 32 Chang, Catani, Huang, Liu, Meena, Huang, Zhou (CR5) 2022 Sun, Xu, Wen, Wang (CR39) 2020; 31 Yao, Moon, Bi (CR53) 2017; 23 Zhao, Deng, Li, Wang, Wei (CR56) 2020; 20 Abdalla, Attom, Hawileh (CR1) 2014; 73 Qian, Li, Chen, Lyamin, Jiang (CR34) 2019; 59 Jiang, Zhang, Wang, Wang, Han (CR20) 2022; 9 Qi, Tang (CR33) 2018; 42 Lu, Wei, Shang, Jing, Tang (CR30) 2020; 2020 Huang, Chen, Liu, Huang, Hong, Chen (CR17) 2022 Huang, Yang, Zhang, Li, Huang, Chen (CR15) 2020; 9 Rukhaiyar, Alam, Samadhiya (CR36) 2017 Wu, Chen, Lv, Xie, Chen, Gu (CR48) 2022; 9 Criss, Yao, Li, Tang (CR8) 2020; 31 Kwag, Hahm, Kim, Eem (CR25) 2020 Li, Shi, Huang, Hong, Song (CR26) 2021 Selvaraju, Cogswell, Das, Vedantam, Parikh, Batra (CR38) 2020; 128 H Moayedi (579_CR31) 2019; 9 S Kwag (579_CR25) 2020 J Zhou (579_CR58) 2019; 118 W Gao (579_CR10) 2019; 36 D-X Zhou (579_CR57) 2018; 16 C Qi (579_CR33) 2018; 42 SH Jiang (579_CR19) 2017; 47 F Huang (579_CR18) 2022; 14 R Lu (579_CR30) 2020; 2020 F Tan (579_CR40) 2021; 14 F Huang (579_CR17) 2022 MW Akram (579_CR2) 2019; 189 C Dai (579_CR9) 2021; 32 Y Tian (579_CR41) 2018; 318 DL Sun (579_CR39) 2020; 31 X Yao (579_CR53) 2017; 23 X Yin (579_CR54) 2020; 96 ZG Qian (579_CR34) 2019; 59 W Zhang (579_CR55) 2016; 7 H Zhao (579_CR56) 2020; 20 J Tinoco (579_CR42) 2018; 32 Y Chen (579_CR6) 2021; 16 YD Huang (579_CR16) 2021; 32 RE Criss (579_CR8) 2020; 31 F Kang (579_CR21) 2017; 60 Y Yang (579_CR51) 2019; 261 P Wang (579_CR45) 2020; 6 J Schmidhuber (579_CR37) 2015; 61 Z Chang (579_CR5) 2022 C Cortes (579_CR7) 1995; 20 Y Lin (579_CR28) 2018; 6 M Koopialipoor (579_CR22) 2018; 23 S Kumar (579_CR23) 2018; 8 F Huang (579_CR14) 2020; 17 JY Jiang (579_CR20) 2022; 9 S Wongvibulsin (579_CR46) 2019; 20 J Amin (579_CR3) 2019; 32 L Wang (579_CR44) 2020; 12 D Bui (579_CR4) 2019; 8 P Xie (579_CR49) 2019; 7 F Huang (579_CR13) 2016; 23 H Liu (579_CR29) 2018; 156 H Wu (579_CR47) 2019; 9 A Ray (579_CR35) 2020; 103 JA Palenzuela Baena (579_CR32) 2019; 16 S Rukhaiyar (579_CR36) 2017 D Yang (579_CR50) 2019; 2019 XL He (579_CR12) 2021; 32 HS Wu (579_CR48) 2022; 9 W Li (579_CR26) 2021 Y Yang (579_CR52) 2020; 80 M Kumar (579_CR24) 2014; 7 RR Selvaraju (579_CR38) 2020; 128 JA Abdalla (579_CR1) 2014; 73 B Gordan (579_CR11) 2016; 32 H Liang (579_CR27) 2017; 2017 P Wang (579_CR43) 2018; 26 F Huang (579_CR15) 2020; 9 |
| References_xml | – volume: 7 start-page: 54305 year: 2019 end-page: 54311 ident: CR49 article-title: The application of long short-term memory(LSTM) method on displacement prediction of multifactor-induced landslides publication-title: IEEE Access doi: 10.1109/access.2019.2912419 – volume: 23 start-page: 983 year: 2017 end-page: 997 ident: CR53 article-title: A hybrid machine learning approach for additive manufacturing design feature recommendation publication-title: Rapid Prototyp J doi: 10.1108/rpj-03-2016-0041 – volume: 16 start-page: 895 year: 2018 end-page: 919 ident: CR57 article-title: Deep distributed convolutional neural networks: Universality publication-title: Anal Appl doi: 10.1142/s0219530518500124 – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: CR7 article-title: support-vector networks publication-title: Mach Learn doi: 10.1023/a:1022627411411 – volume: 20 start-page: 1 year: 2019 ident: CR46 article-title: Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis publication-title: BMC Med Res Methodol doi: 10.1186/s12874-019-0863-0 – volume: 59 start-page: 556 year: 2019 end-page: 569 ident: CR34 article-title: An artificial neural network approach to inhomogeneous soil slope stability predictions based on limit analysis methods publication-title: Soils Found doi: 10.1016/j.sandf.2018.10.008 – volume: 32 start-page: 85 year: 2016 end-page: 97 ident: CR11 article-title: Prediction of seismic slope stability through combination of particle swarm optimization and neural network publication-title: Eng Comput doi: 10.1007/s00366-015-0400-7 – volume: 103 start-page: 3523 year: 2020 end-page: 3540 ident: CR35 article-title: Stability prediction of Himalayan residual soil slope using artificial neural network publication-title: Nat Hazards doi: 10.1007/s11069-020-04141-2 – volume: 17 start-page: 2919 year: 2020 end-page: 2930 ident: CR14 article-title: Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model publication-title: Landslides doi: 10.1007/s10346-020-01473-9 – volume: 42 start-page: 1823 year: 2018 end-page: 1839 ident: CR33 article-title: A hybrid ensemble method for improved prediction of slope stability publication-title: Int J Numer Anal Meth Geomech doi: 10.1002/nag.2834 – volume: 261 start-page: 105278 year: 2019 ident: CR51 article-title: Sequential excavation analysis of soil-rock-mixture slopes using an improved numerical manifold method with multiple layers of mathematical cover systems publication-title: Eng Geol doi: 10.1016/j.enggeo.2019.105278 – volume: 20 start-page: 6854 year: 2020 ident: CR56 article-title: Hierarchical spatial-spectral feature extraction with long short term memory (LSTM) for mineral identification using hyperspectral imagery publication-title: Sensors (basel) doi: 10.3390/s20236854 – volume: 32 start-page: 1069 year: 2021 end-page: 1078 ident: CR16 article-title: An updated database and spatial distribution of landslides triggered by the Milin, Tibet M(w)6.4 earthquake of 18 November 2017 publication-title: J Earth Sci doi: 10.1007/s12583-021-1433-z – year: 2022 ident: CR5 article-title: Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors publication-title: J Rock Mech Geotech Eng doi: 10.1016/j.jrmge.2022.07.009 – volume: 47 start-page: 710 year: 2017 end-page: 725 ident: CR19 article-title: Quantitative risk assessment of slope failure in 2-D spatially variable soils by limit equilibrium method publication-title: Appl Math Model doi: 10.1016/j.apm.2017.03.048 – volume: 9 start-page: 20387 year: 2019 ident: CR47 article-title: Predicting effective diffusivity of porous media from images by deep learning publication-title: Sci Rep doi: 10.1038/s41598-019-56309-x – volume: 156 start-page: 498 year: 2018 end-page: 514 ident: CR29 article-title: Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network publication-title: Energy Convers Manage doi: 10.1016/j.enconman.2017.11.053 – volume: 128 start-page: 336 year: 2020 end-page: 359 ident: CR38 article-title: Grad-CAM: visual explanations from deep networks via gradient-based localization publication-title: Int J Comput Vision doi: 10.1007/s11263-019-01228-7 – volume: 26 start-page: 2086 year: 2018 end-page: 2095 ident: CR43 article-title: LSTM-based EEG classification in motor imagery tasks publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2018.2876129 – volume: 2017 start-page: 211 year: 2017 ident: CR27 article-title: Text feature extraction based on deep learning: a review publication-title: EURASIP J Wirel Commun Netw doi: 10.1186/s13638-017-0993-1 – volume: 31 start-page: 1051 year: 2020 end-page: 1057 ident: CR8 article-title: A predictive, two-parameter model for the movement of reservoir landslides publication-title: J Earth Sci doi: 10.1007/s12583-020-1331-9 – volume: 189 start-page: 116319 year: 2019 ident: CR2 article-title: CNN based automatic detection of photovoltaic cell defects in electroluminescence images publication-title: Energy doi: 10.1016/j.energy.2019.116319 – volume: 32 start-page: 1056 year: 2021 end-page: 1068 ident: CR12 article-title: Landslides triggered by the 2020 Qiaojia M(w)5.1 earthquake, Yunnan, China: distribution, influence factors and tectonic significance publication-title: J Earth Sci doi: 10.1007/s12583-021-1492-1 – volume: 23 start-page: 5913 year: 2018 end-page: 5929 ident: CR22 article-title: Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions publication-title: Soft Comput doi: 10.1007/s00500-018-3253-3 – volume: 7 start-page: 186 year: 2014 end-page: 193 ident: CR24 article-title: Determination of stability of epimetamorphic rock slope using minimax probability machine publication-title: Geomat Nat Haz Risk doi: 10.1080/19475705.2014.883440 – volume: 318 start-page: 297 year: 2018 end-page: 305 ident: CR41 article-title: LSTM-based traffic flow prediction with missing data publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.08.067 – volume: 2020 start-page: 1 year: 2020 end-page: 13 ident: CR30 article-title: Stability analysis of jointed rock slope by strength reduction technique considering ubiquitous joint model publication-title: Adv Civil Eng doi: 10.1155/2020/8862243 – volume: 8 start-page: 395 year: 2019 ident: CR4 article-title: Predicting slope stability failure through machine learning paradigms publication-title: ISPRS Int J Geo-Inf doi: 10.3390/ijgi8090395 – volume: 32 start-page: 1092 year: 2021 end-page: 1103 ident: CR9 article-title: Active landslide detection based on sentinel-1 data and InSAR technology in Zhouqu county, Gansu Province, Northwest China publication-title: J Earth Sci doi: 10.1007/s12583-020-1380-0 – volume: 2019 start-page: 1 year: 2019 end-page: 11 ident: CR50 article-title: A time-aware CNN-based personalized recommender system publication-title: Complexity doi: 10.1155/2019/9476981 – volume: 36 start-page: 325 year: 2019 end-page: 344 ident: CR10 article-title: A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes publication-title: Eng Comput doi: 10.1007/s00366-019-00702-7 – volume: 31 start-page: 1068 year: 2020 end-page: 1086 ident: CR39 article-title: An optimized random forest model and its generalization ability in landslide susceptibility mapping: application in two areas of Three Gorges Reservoir, China publication-title: J Earth Sci doi: 10.1007/s12583-020-1072-9 – volume: 6 start-page: 33 year: 2020 ident: CR45 article-title: Classification and extent determination of rock slope using deep learning publication-title: Geomech Geophys Geo-Energy Geo-Resour doi: 10.1007/s40948-020-00154-0 – volume: 32 start-page: 15965 year: 2019 end-page: 15973 ident: CR3 article-title: Brain tumor detection: a long short-term memory (LSTM)-based learning model publication-title: Neural Comput Appl doi: 10.1007/s00521-019-04650-7 – volume: 9 start-page: 12 issue: 1 year: 2022 ident: CR20 article-title: Web pillar stability in open-pit highwall mining publication-title: Int J Coal Sci Technol doi: 10.1007/s40789-022-00483-3 – volume: 80 start-page: 345 year: 2020 end-page: 352 ident: CR52 article-title: Stability analysis of slopes using the vector sum numerical manifold method publication-title: Bull Eng Geol Env doi: 10.1007/s10064-020-01903-x – volume: 9 start-page: 4638 year: 2019 ident: CR31 article-title: Machine-learning-based classification approaches toward recognizing slope stability failure publication-title: Appl Sci doi: 10.3390/app9214638 – volume: 9 start-page: 539 year: 2020 ident: CR15 article-title: Regional terrain complexity assessment based on principal component analysis and geographic information system: a case of Jiangxi Province, China publication-title: ISPRS Int J Geo-Inf doi: 10.3390/ijgi9090539 – volume: 32 start-page: 04017088 year: 2018 ident: CR42 article-title: Stability condition identification of rock and soil cutting slopes based on soft computing publication-title: J Comput Civ Eng doi: 10.1061/(asce)cp.1943-5487.0000739 – volume: 60 start-page: 387 year: 2017 end-page: 396 ident: CR21 article-title: Slope stability evaluation using Gaussian processes with various covariance functions publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.07.011 – volume: 23 start-page: 617 year: 2016 end-page: 626 ident: CR13 article-title: Influencing factor analysis and displacement prediction in reservoir landslides: a case study of Three Gorges Reservoir (China) publication-title: Tehnički Vjesnik – volume: 12 start-page: 4075 year: 2020 ident: CR44 article-title: Learning rotation domain deep mutual information using convolutional LSTM for unsupervised PolSAR image classification publication-title: Rem Sens doi: 10.3390/rs12244075 – volume: 7 start-page: 45 year: 2016 end-page: 52 ident: CR55 article-title: Multivariate adaptive regression splines and neural network models for prediction of pile drivability publication-title: Geosci Front doi: 10.1016/j.gsf.2014.10.003 – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: CR37 article-title: Deep learning in neural networks: an overview publication-title: Neural Netw doi: 10.1016/j.neunet.2014.09.003 – volume: 14 start-page: 220 year: 2021 ident: CR40 article-title: Rapid assessment of landslide risk level based on deep learning publication-title: Arab J Geosci doi: 10.1007/s12517-021-06616-3 – volume: 9 start-page: 38 issue: 1 year: 2022 ident: CR48 article-title: Stability analysis of rib pillars in highwall mining under dynamic and static loads in open-pit coal mine publication-title: Int J Coal Sci Technol doi: 10.1007/s40789-022-00504-1 – volume: 14 start-page: 4436 year: 2022 ident: CR18 article-title: Landslide susceptibility prediction considering neighborhood characteristics of landslide spatial datasets and hydrological slope units using remote sensing and GIS technologies publication-title: Rem Sens doi: 10.3390/rs14184436 – volume: 118 start-page: 505 year: 2019 end-page: 518 ident: CR58 article-title: Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories publication-title: Saf Sci doi: 10.1016/j.ssci.2019.05.046 – volume: 16 start-page: 044006 year: 2021 ident: CR6 article-title: An LSTM-based neural network method of particulate pollution forecast in China publication-title: Environ Res Lett doi: 10.1088/1748-9326/abe1f5 – volume: 6 start-page: 31169 year: 2018 end-page: 31179 ident: CR28 article-title: Prediction of slope stability using four supervised learning methods publication-title: IEEE Access doi: 10.1109/access.2018.2843787 – year: 2022 ident: CR17 article-title: Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold publication-title: Geomorphology doi: 10.1016/j.geomorph.2022.108236 – volume: 96 start-page: 841 year: 2020 end-page: 848 ident: CR54 article-title: Precise evaluation method for the stability analysis of multi-scale slopes publication-title: Simulation doi: 10.1177/0037549720943274 – year: 2021 ident: CR26 article-title: Uncertainties of collapse susceptibility prediction based on remote sensing and GIS: effects of different machine learning models publication-title: Front Earth Sci doi: 10.3389/feart.2021.731058 – volume: 16 start-page: 969 year: 2019 end-page: 982 ident: CR32 article-title: Landslide susceptibility mapping on the islands of Vulcano and Lipari (Aeolian Archipelago, Italy), using a multi-classification approach on conditioning factors and a modified GIS matrix method for areas lacking in a landslide inventory publication-title: Landslides doi: 10.1007/s10346-019-01148-0 – volume: 73 start-page: 5463 year: 2014 end-page: 5477 ident: CR1 article-title: Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network publication-title: Environ Earth Sci doi: 10.1007/s12665-014-3800-x – volume: 8 start-page: 149 year: 2018 end-page: 154 ident: CR23 article-title: A neural network model for slope stability computations publication-title: Géotechnique Letters doi: 10.1680/jgele.18.00022 – year: 2020 ident: CR25 article-title: Development of a probabilistic seismic performance assessment model of slope using machine learning methods publication-title: Sustainability doi: 10.3390/su12083269 – year: 2017 ident: CR36 article-title: A PSO-ANN hybrid model for predicting factor of safety of slope publication-title: Int J Geotech Eng doi: 10.1080/19386362.2017.1305652 – volume: 16 start-page: 969 year: 2019 ident: 579_CR32 publication-title: Landslides doi: 10.1007/s10346-019-01148-0 – volume: 32 start-page: 85 year: 2016 ident: 579_CR11 publication-title: Eng Comput doi: 10.1007/s00366-015-0400-7 – volume: 20 start-page: 273 year: 1995 ident: 579_CR7 publication-title: Mach Learn doi: 10.1023/a:1022627411411 – volume: 36 start-page: 325 year: 2019 ident: 579_CR10 publication-title: Eng Comput doi: 10.1007/s00366-019-00702-7 – volume: 8 start-page: 149 year: 2018 ident: 579_CR23 publication-title: Géotechnique Letters doi: 10.1680/jgele.18.00022 – volume: 8 start-page: 395 year: 2019 ident: 579_CR4 publication-title: ISPRS Int J Geo-Inf doi: 10.3390/ijgi8090395 – volume: 47 start-page: 710 year: 2017 ident: 579_CR19 publication-title: Appl Math Model doi: 10.1016/j.apm.2017.03.048 – volume: 80 start-page: 345 year: 2020 ident: 579_CR52 publication-title: Bull Eng Geol Env doi: 10.1007/s10064-020-01903-x – year: 2017 ident: 579_CR36 publication-title: Int J Geotech Eng doi: 10.1080/19386362.2017.1305652 – volume: 2017 start-page: 211 year: 2017 ident: 579_CR27 publication-title: EURASIP J Wirel Commun Netw doi: 10.1186/s13638-017-0993-1 – volume: 96 start-page: 841 year: 2020 ident: 579_CR54 publication-title: Simulation doi: 10.1177/0037549720943274 – year: 2021 ident: 579_CR26 publication-title: Front Earth Sci doi: 10.3389/feart.2021.731058 – volume: 60 start-page: 387 year: 2017 ident: 579_CR21 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.07.011 – volume: 7 start-page: 54305 year: 2019 ident: 579_CR49 publication-title: IEEE Access doi: 10.1109/access.2019.2912419 – volume: 318 start-page: 297 year: 2018 ident: 579_CR41 publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.08.067 – volume: 6 start-page: 31169 year: 2018 ident: 579_CR28 publication-title: IEEE Access doi: 10.1109/access.2018.2843787 – volume: 23 start-page: 617 year: 2016 ident: 579_CR13 publication-title: Tehnički Vjesnik – volume: 23 start-page: 5913 year: 2018 ident: 579_CR22 publication-title: Soft Comput doi: 10.1007/s00500-018-3253-3 – volume: 73 start-page: 5463 year: 2014 ident: 579_CR1 publication-title: Environ Earth Sci doi: 10.1007/s12665-014-3800-x – volume: 32 start-page: 1056 year: 2021 ident: 579_CR12 publication-title: J Earth Sci doi: 10.1007/s12583-021-1492-1 – volume: 7 start-page: 186 year: 2014 ident: 579_CR24 publication-title: Geomat Nat Haz Risk doi: 10.1080/19475705.2014.883440 – volume: 261 start-page: 105278 year: 2019 ident: 579_CR51 publication-title: Eng Geol doi: 10.1016/j.enggeo.2019.105278 – volume: 16 start-page: 044006 year: 2021 ident: 579_CR6 publication-title: Environ Res Lett doi: 10.1088/1748-9326/abe1f5 – volume: 189 start-page: 116319 year: 2019 ident: 579_CR2 publication-title: Energy doi: 10.1016/j.energy.2019.116319 – volume: 32 start-page: 1069 year: 2021 ident: 579_CR16 publication-title: J Earth Sci doi: 10.1007/s12583-021-1433-z – volume: 9 start-page: 38 issue: 1 year: 2022 ident: 579_CR48 publication-title: Int J Coal Sci Technol doi: 10.1007/s40789-022-00504-1 – volume: 2019 start-page: 1 year: 2019 ident: 579_CR50 publication-title: Complexity doi: 10.1155/2019/9476981 – volume: 32 start-page: 04017088 year: 2018 ident: 579_CR42 publication-title: J Comput Civ Eng doi: 10.1061/(asce)cp.1943-5487.0000739 – year: 2022 ident: 579_CR5 publication-title: J Rock Mech Geotech Eng doi: 10.1016/j.jrmge.2022.07.009 – volume: 23 start-page: 983 year: 2017 ident: 579_CR53 publication-title: Rapid Prototyp J doi: 10.1108/rpj-03-2016-0041 – volume: 26 start-page: 2086 year: 2018 ident: 579_CR43 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2018.2876129 – volume: 6 start-page: 33 year: 2020 ident: 579_CR45 publication-title: Geomech Geophys Geo-Energy Geo-Resour doi: 10.1007/s40948-020-00154-0 – volume: 14 start-page: 4436 year: 2022 ident: 579_CR18 publication-title: Rem Sens doi: 10.3390/rs14184436 – volume: 31 start-page: 1068 year: 2020 ident: 579_CR39 publication-title: J Earth Sci doi: 10.1007/s12583-020-1072-9 – volume: 128 start-page: 336 year: 2020 ident: 579_CR38 publication-title: Int J Comput Vision doi: 10.1007/s11263-019-01228-7 – year: 2022 ident: 579_CR17 publication-title: Geomorphology doi: 10.1016/j.geomorph.2022.108236 – volume: 9 start-page: 20387 year: 2019 ident: 579_CR47 publication-title: Sci Rep doi: 10.1038/s41598-019-56309-x – volume: 31 start-page: 1051 year: 2020 ident: 579_CR8 publication-title: J Earth Sci doi: 10.1007/s12583-020-1331-9 – year: 2020 ident: 579_CR25 publication-title: Sustainability doi: 10.3390/su12083269 – volume: 9 start-page: 539 year: 2020 ident: 579_CR15 publication-title: ISPRS Int J Geo-Inf doi: 10.3390/ijgi9090539 – volume: 12 start-page: 4075 year: 2020 ident: 579_CR44 publication-title: Rem Sens doi: 10.3390/rs12244075 – volume: 61 start-page: 85 year: 2015 ident: 579_CR37 publication-title: Neural Netw doi: 10.1016/j.neunet.2014.09.003 – volume: 7 start-page: 45 year: 2016 ident: 579_CR55 publication-title: Geosci Front doi: 10.1016/j.gsf.2014.10.003 – volume: 20 start-page: 6854 year: 2020 ident: 579_CR56 publication-title: Sensors (basel) doi: 10.3390/s20236854 – volume: 32 start-page: 1092 year: 2021 ident: 579_CR9 publication-title: J Earth Sci doi: 10.1007/s12583-020-1380-0 – volume: 118 start-page: 505 year: 2019 ident: 579_CR58 publication-title: Saf Sci doi: 10.1016/j.ssci.2019.05.046 – volume: 32 start-page: 15965 year: 2019 ident: 579_CR3 publication-title: Neural Comput Appl doi: 10.1007/s00521-019-04650-7 – volume: 59 start-page: 556 year: 2019 ident: 579_CR34 publication-title: Soils Found doi: 10.1016/j.sandf.2018.10.008 – volume: 20 start-page: 1 year: 2019 ident: 579_CR46 publication-title: BMC Med Res Methodol doi: 10.1186/s12874-019-0863-0 – volume: 14 start-page: 220 year: 2021 ident: 579_CR40 publication-title: Arab J Geosci doi: 10.1007/s12517-021-06616-3 – volume: 156 start-page: 498 year: 2018 ident: 579_CR29 publication-title: Energy Convers Manage doi: 10.1016/j.enconman.2017.11.053 – volume: 16 start-page: 895 year: 2018 ident: 579_CR57 publication-title: Anal Appl doi: 10.1142/s0219530518500124 – volume: 42 start-page: 1823 year: 2018 ident: 579_CR33 publication-title: Int J Numer Anal Meth Geomech doi: 10.1002/nag.2834 – volume: 9 start-page: 4638 year: 2019 ident: 579_CR31 publication-title: Appl Sci doi: 10.3390/app9214638 – volume: 17 start-page: 2919 year: 2020 ident: 579_CR14 publication-title: Landslides doi: 10.1007/s10346-020-01473-9 – volume: 2020 start-page: 1 year: 2020 ident: 579_CR30 publication-title: Adv Civil Eng doi: 10.1155/2020/8862243 – volume: 103 start-page: 3523 year: 2020 ident: 579_CR35 publication-title: Nat Hazards doi: 10.1007/s11069-020-04141-2 – volume: 9 start-page: 12 issue: 1 year: 2022 ident: 579_CR20 publication-title: Int J Coal Sci Technol doi: 10.1007/s40789-022-00483-3 |
| SSID | ssj0001492414 |
| Score | 2.5572112 |
| Snippet | The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the... Abstract The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used... |
| SourceID | doaj proquest crossref springer |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 18 |
| SubjectTerms | Deep learning Disaster studies Energy Fossil Fuels (incl. Carbon Capture) Geo-Studio software Geotechnical Engineering & Applied Earth Sciences Landslides Long short-term memory Machine learning Machine learning model Mineral Resources Neural networks Slope stability Slope stability prediction Support vector machines |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9tAEF1ViEM5oEKpGgrVHLiVFf72LjdARJwQUlspt9V-GYoSx4oDEj-q_7Ezayc1lQoXbpa9ljee8b43mdk3jB3pNCFRI8MpZcYzJwwXLpdce59adCnkAC40myivr8VkIm8Grb6oJqyTB-5e3ElUaowKiDeTdJyotIgrI51A6C-kqBytvlEpB8HUfcf7EZoopZwgh-ACQa3fMRP2zVHySnKEKx42Y_LsGSoF8f5njPOfJGnAnvEHtt2TRjjrJrvD3vl6l20NpAQ_st_fp_PGA3K9UO36BM2CUjD02oGQygEeaJjO61to75Bzc1qTYUaFtk9Aqpb4gLqrCT8Fu-5O2AL9UwtUnN47aRg3HN4eQ_vQEI-Hx5ADgFmo0PQt6NoBgqGbzwDJMf5ICJ132j32c3z54-KK960YuEXAWvLES4wsjHMYb2mPFNDHhcalUfrYO10l1pg8t6QdlvjCFEVpEmFlpoXOfZ67LP3ENup57T8zwJASzRaZTFuXuVgaZESRMFFWmsraMhuxeGUKZXudcmqXMVVrheVgPoXmU8F8Cu_5tr6n6VQ6Xhx9ThZejySF7XAC_U71fqde87sRO1j5h-o_-1ZR1jYNmncjdrzymb-X_z-l_beY0hf2PiGfDsU2B2xjuXjwh2zTPi5_tYuv4QP5A1NWEvc priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELag5QAH3oilBc2BG7XYZJ3E5lJR1IrTquIh9Wb5lYK0m4T1tlJ_FP-RGa93t0WiF25RYuehmcx84xl_w9hbMymJ1MhySplx4aXl0leKmxAmDlUKMYBPzSaa6VSenanTvOAWc1nl2iYmQ-17R2vk7ykhNkl0YofDL05doyi7mlto3GW7xFSGer57dDw9_bJdZcEJIhF8l4gluETnlnfOpP1zlMRSHN0WT5syubjhnRKJ_w3k-VeyNPmgk0f_-_aP2cOMPuHjSl2esDuhe8oeXOMkfMZ-f531QwAEjals9gqGBeVySH5ALs8DHhiY9d05xB8I3jkZd5hTxe4VED0mPqBbFZd_ALdpcxiBlnyBqtyztqdx14fHA4gXAwUEcJmSCTBPpZ4hguk8oFf1_RwQZeN3Q2rhE5-z7yfH3z595rmnA3fo-Za8DApDFOs9Bm4mIJYMRW3QxqpQBG_a0llbVY5IyMpQ27pubCmdEkaaKlSVF5MXbKfru_CSAcamsvVjK4zzwhfKIrQaSzsWjW2da8SIFWtZapcJz6nvxkxvqJqT_DXKXyf5a5zzbjNnWNF93Dr6iFRkM5KoutOJfnGu85-vx43BsJYCP-I-lK2RRWuVl4hda4UfMGL7a5XR2X5EvdWXETtYK9328r9f6dXtd9tj90tS91SPs892louL8Jrdc5fLn3HxJv89fwAAdSOz priority: 102 providerName: ProQuest |
| Title | Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models |
| URI | https://link.springer.com/article/10.1007/s40789-023-00579-4 https://www.proquest.com/docview/2890354923 https://doaj.org/article/07a282174332408fa81fb9d8154698fd |
| Volume | 10 |
| WOSCitedRecordID | wos000966734600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2198-7823 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001492414 issn: 2095-8293 databaseCode: DOA dateStart: 20140101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2198-7823 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001492414 issn: 2095-8293 databaseCode: PCBAR dateStart: 20140301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2198-7823 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001492414 issn: 2095-8293 databaseCode: BENPR dateStart: 20140301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2198-7823 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001492414 issn: 2095-8293 databaseCode: PIMPY dateStart: 20140301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLink Open Access Journals customDbUrl: eissn: 2198-7823 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001492414 issn: 2095-8293 databaseCode: C24 dateStart: 20140301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9NAEF5BywEOvBGBEs2BG10pdvzY5UarVnAginhI5bTal1tQYkfZtFJ_FP-Rmc3atAiQ4BY542TtGe9845n5hrGXepoTqZHhlDLjhROGC1dKrr2fWjQpxAAuDpuoZzNxciLnqSks9NXufUoy7tRDsxtlnCRHH8NjByUvbrLdMhOSCvkOU4_Dty3mR7dE6eQc8QMX6NBSt8zvf-aaR4rE_dfQ5i8J0uh3ju_934rvs7sJZ8KbrWE8YDd8-5DducI--Ih9_7joVh4QHsYC2UtYrSlrQ5oCcm4O8IOGRdeeQjhDmM5pG4cl1eZeAhFh4h-02zLy12CHgYYB6OUuUD17susod1U87EM4XxH0h4uYNoBlLOr0AXTrAP2n65aAeBrvDcRhPeEx-3x89OnwLU_TG7hFH7fhuZcYjBjnMETTHlGjzyqNu6n0mXe6ya0xZWmJbiz3lamq2uTCykILXfqydMX0Cdtpu9Y_ZYBRqGjcxBTausJl0iCImggzKWrTWFsXI5b1GlQ2UZvThI2FGkiZoyoUqkJFVSg859VwzmpL7PFX6QMyjEGSSLnjgW59qtIzria1xgCWQjxiORSNFlljpBOIUiuJFzBie71ZqbRTBEWJ3mmkyRux_d6Mfn795yU9-zfx5-x2TpYYK3H22M5mfe5fsFv2YvM1rMds9-BoNv8wjk_SOL6YwGPzd-_nX34A35IcIw |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VFAk48EYECswBTnRF7PixRkKIV9WobRSJIpXTdl9uKyW2idOi_Cj4jcxs7KRForceuEXJ2nE23-x8szP7DWMvVT8kUSPNKWXGIys0FzbOuHKubxBSyAGsbzaRDofi4CAbrbHf7VkYKqts10S_UNvS0B75G0qI9b2c2PvqB6euUZRdbVtoLGCx4-Y_MWSr3w0-4__7Kgy3vux_2uZNVwFucO2d8dBlSJK1tRg6KIdsxgWJQivPXOCsykOjdRwbksEKXaKTJNWhMFmkhIpdHNuoj_e9xtYjAnuHrY8Ge6Pvq10dfMDIC4qHyF24QGfanNTx5_UoaZZxdJPcHwLl0QVv6JsGXGC6fyVnvc_buvO_zdZddrth1_BhYQ732Jor7rNb5zQXH7BfX8dl5QBJsS8LnkM1pVwV4RPIpVvAFwrGZXEE9TEGJ5ycF0yoInkOJP-JX1Asiuffglm2cayBtrSBqvgba_bjzg-vN6E-rSjggTOfLIGJL2V1NajCArIGW04AowicZ_AtiuqH7NuVzNcj1inKwj1mgLG3yG1PR8rYyAaZRurYE7oXpTo3Jo26LGixI00j6E59RcZyKUXt8SYRb9LjTeI1r5fXVAs5k0tHfyRILkeSFLl_o5weyWZlk71UYdhOgS1pO4pciSDXmRXIzZMMf0CXbbQQlc36WMsVPrtsswX56uN_P9KTy-_2gt3Y3t_blbuD4c5TdjMkU_O1RxusM5ueumfsujmbndTT543lAju8avj_AcLNgwc |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEF6VFiE4lPISoYXOgRtdNXb82O2tFKKiVlElQOpttS-3oMS24rRSfxT_kZm1Y1IESBU3yx4nm8x45xvPzDeMvdWjmEiNDKeUGU-cMFy4VHLt_ciiSSEGcGHYRD6ZiPNzebbSxR-q3ZcpybangViaysV-7Yr9vvGNsk-So7_hoZuSJ_fYBp0jGz_q-h2-t_gfXRSllmPEElygc-s6Z_78Mbe8UyDxv4U8f0uWBh80fvz_q99imx3-hMPWYJ6wNV8-ZY9WWAmfsR-fp1XtAWFjKJy9gXpO2RzSIJDTc4AHGqZVeQHNJcJ3Tts7zKhm9waIIBO_oGzLyw_A9oMOG6CXvkB17p29B7lV8WYPmquaQgK4DukEmIViT9-ALh2gX3XVDBBn4_8EYYhP85x9HX_8cnTMu6kO3KLvW_DYSwxSjHMYummPaNJHmcZdVvrIO13E1pg0tURDFvvMZFluYmFlooVOfZq6ZPSCrZdV6V8ywOhUFG5oEm1d4iJpEFwNhRkmuSmszZMBi5baVLajPKfJG1PVkzUHVShUhQqqUHjPu_6euiX8-Kf0ezKSXpLIusOJan6humdfDXONgS2FfsR-KAotosJIJxC9ZhJ_wIDtLE1MdTtIoygBPAr0eQO2tzSpX5f_vqRXdxPfZQ_OPozV6afJyTZ7GJNRhmKdHba-mF_51-y-vV58a-ZvwoP1E-vlI6s |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Slope+stability+prediction+based+on+a+long+short-term+memory+neural+network%3A+comparisons+with+convolutional+neural+networks%2C+support+vector+machines+and+random+forest+models&rft.jtitle=International+journal+of+coal+science+%26+technology&rft.au=Huang%2C+Faming&rft.au=Xiong%2C+Haowen&rft.au=Chen%2C+Shixuan&rft.au=Lv%2C+Zhitao&rft.date=2023-12-01&rft.issn=2095-8293&rft.eissn=2198-7823&rft.volume=10&rft.issue=1&rft_id=info:doi/10.1007%2Fs40789-023-00579-4&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s40789_023_00579_4 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2095-8293&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2095-8293&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2095-8293&client=summon |