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RESEARCH ARTICLE

Ensemble learning landslide susceptibility assessment with optimized non-landslide samples selection

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Article: 2378176 | Received 25 Apr 2024, Accepted 04 Jul 2024, Published online: 17 Jul 2024

References

  • Achu AL, Aju CD, Di Napoli M, Prakash P, Gopinath G, Shaji E, Chandra V. 2023. Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis. Geosci Front. 14(6):101657. doi: 10.1016/j.gsf.2023.101657.
  • Achu AL, Thomas J, Aju CD, Remani PK, Gopinath G. 2023. Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data. Earth Sci Inform. 16(1):1025–1039. doi: 10.1007/s12145-022-00910-8.
  • Achu AL, Thomas J, Aju CD, Vijith H, Gopinath G. 2024. Redefining landslide susceptibility under extreme rainfall events using deep learning. Geomorphology. 448:109033. doi: 10.1016/j.geomorph.2023.109033.
  • Buscema M. 2002. A brief overview and introduction to artificial neural networks. Subst Use Misuse. 37(8–10):1093–1148. doi: 10.1081/JA-120004171.
  • Chang Z, Du Z, Zhang F, Huang F, Chen J, Li W, Guo Z. 2020. Landslide susceptibility prediction based on remote sensing images and GIS: comparisons of supervised and unsupervised machine learning models. Remote Sensing. 12(3):502. doi: 10.3390/rs12030502.
  • Chang Z, Huang J, Huang F, Bhuyan K, Meena SR, Catani F. 2023. Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models. Gondwana Res. 117:307–320. doi: 10.1016/j.gr.2023.02.007.
  • Chen T, Gao X, Liu G, Wang C, Zhao Z, Dou J, Niu R, Plaza AJ. 2024. BisDeNet: a new lightweight deep learning-based framework for efficient landslide detection. IEEE J Sel Top Appl Earth Observ Remote Sensing. 17:3648–3663. doi: 10.1109/JSTARS.2024.3351873.
  • Chen T, Wang Q, Zhao Z, Liu G, Dou J, Plaza A. 2024. LCFSTE: landslide conditioning factors and swin transformer ensemble for landslide susceptibility assessment. IEEE J Sel Top Appl Earth Observations Remote Sensing. 17:6444–6454. doi: 10.1109/JSTARS.2024.3373029.
  • Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J. 2017. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA. 151:147–160. doi: 10.1016/j.catena.2016.11.032.
  • Dai K, Chen C, Shi X, Wu M, Feng W, Xu Q, Liang R, Zhuo G, Li Z. 2023. Dynamic landslides susceptibility evaluation in Baihetan Dam area during extensive impoundment by integrating geological model and InSAR observations. Int J Appl Earth Obs Geoinf. 116:103157. doi: 10.1016/j.jag.2022.103157.
  • Dai K, Li Z, Xu Q, Tomas R, Li T, Jiang L, Zhang J, Yin T, Wang H. 2023. Identification and evaluation of the high mountain upper slope potential landslide based on multi-source remote sensing: the Aniangzhai landslide case study. Landslides. 20(7):1405–1417. doi: 10.1007/s10346-023-02044-4.
  • Dey R, Salem FM. 2017. Gate-variants of Gated Recurrent Unit (GRU) neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). Boston, MA: IEEE. p. 1597–1600. doi: 10.1109/MWSCAS.2017.8053243.
  • Dou H, He J, Huang S, Jian W, Guo C. 2023. Influences of non-landslide sample selection strategies on landslide susceptibility mapping by machine learning. Geomatics Nat Hazards Risk. 14(1):2285719. doi: 10.1080/19475705.2023.2285719.
  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Han Z, Pham BT. 2020. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides. 17(3):641–658. doi: 10.1007/s10346-019-01286-5.
  • Du J, Glade T, Woldai T, Chai B, Zeng B. 2020. Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Eng Geol. 270:105572. doi: 10.1016/j.enggeo.2020.105572.
  • Fang Z, Wang Y, Peng L, Hong H. 2021. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci. 35(2):321–347. doi: 10.1080/13658816.2020.1808897.
  • Friedman JH. 2001. Greedy function approximation: a gradient boosting machine. Ann. Statist. 29(5):1189–1232. doi: 10.1214/aos/1013203451.
  • Gao B,He Yi,Chen X,Chen H,Yang W,Zhang L. 2024. A Deep Neural Network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall. IEEE J Sel Top Appl Earth Observations Remote Sensing. 17:5946–5969. 10.1109/JSTARS.2024.3370218.
  • Gao B, He Y, Chen X, Zheng X, Zhang L, Zhang Q, Lu J. 2023. Landslide risk evaluation in Shenzhen based on stacking ensemble learning and InSAR. IEEE J Sel Top Appl Earth Observ Remote Sensing. 16:1–18. doi: 10.1109/JSTARS.2023.3291490.
  • He Y, Wang W, Zhang L, Chen Y, Chen Y, Chen B, He X, Zhao Z. 2023. An identification method of potential landslide zones using InSAR data and landslide susceptibility. Geomatics Nat Hazards Risk. 14(1):2185120. doi: 10.1080/19475705.2023.2185120.
  • He Y, Yao S, Yang W, Yan H, Zhang L, Wen Z, Zhang Y, Liu T. 2021. An extraction method for glacial lakes based on Landsat-8 imagery using an improved U-Net network. IEEE J Sel Top Appl Earth Observ Remote Sensing. 14:6544–6558. doi: 10.1109/JSTARS.2021.3085397.
  • He Y, Zhao Z, Yang W, Yan H, Wang W, Yao S, Zhang L, Liu T. 2021. A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping. Int J Appl Earth Obs Geoinf. 104:102508. doi: 10.1016/j.jag.2021.102508.
  • He Y, Zhao Z, Zhu Q, Liu T, Zhang Q, Yang W, Zhang L, Wang Q. 2024. An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features. Int J Digital Earth. 17(1):2295408. doi: 10.1080/17538947.2023.2295408.
  • Huang F, Cao Z, Guo J, Jiang S-H, Li S, Guo Z. 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA. 191:104580. doi: 10.1016/j.catena.2020.104580.
  • Huang F, Pan L, Yao C, Zhou C, Jiang Q, Chang Z. 2021. Landslide susceptibility prediction modelling based on semi-supervised machine learning. J Zhejiang Univ Eng Sci. 55:1705–1713. doi: 10.3785/j.issn.1008-973X.2021.09.012.
  • Huang Y, Zhao L. 2018. Review on landslide susceptibility mapping using support vector machines. CATENA. 165:520–529. doi: 10.1016/j.catena.2018.03.003.
  • Karaman MO, Çabuk SN, Pekkan E. 2022. Utilization of frequency ratio method for the production of landslide susceptibility maps: karaburun Peninsula case, Turkey. Environ Sci Pollut Res Int. 29(60):91285–91305. doi: 10.1007/s11356-022-21931-2.
  • LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521(7553):436–444. doi: 10.1038/nature14539.
  • Li C, Wang M, Liu K. 2018. A decadal evolution of landslides and debris flows after the Wenchuan earthquake. Geomorphology. 323:1–12. doi: 10.1016/j.geomorph.2018.09.010.
  • Lin S, Wang X, Nan C. 2024. Slope unit-based genetic landform mapping on Tibetan plateau – a terrain unit-based framework for large spatial scale landform classification. CATENA. 236:107757. doi: 10.1016/j.catena.2023.107757.
  • Liu J, Liang E, Xu S, Liu M, Wang Y, Zhang F, Luo A. 2022. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility. Acta Geod Cartogr Sin. 51(10):2034–2045. doi: 10.11947/1.AGCS,2022.20220326.
  • Lv L, Chen T, Dou J, Plaza A. 2022. A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping. Int J Appl Earth Obs Geoinf. 108:102713. doi: 10.1016/j.jag.2022.102713.
  • Ma Z, Mei G, Piccialli F. 2021. Machine learning for landslides prevention: a survey. Neural Comput Applic. 33(17):10881–10907. doi: 10.1007/s00521-020-05529-8.
  • Merghadi A, Abderrahmane B, Tien Bui D. 2018. Landslide susceptibility assessment at Mila Basin (Algeria): a comparative assessment of prediction capability of advanced machine learning methods. IJGI. 7(7):268. doi: 10.3390/ijgi7070268.
  • Pham QB, Achour Y, Ali SA, Parvin F, Vojtek M, Vojteková J, Al-Ansari N, Achu AL, Costache R, Khedher KM, et al. 2021. A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomatics Nat Hazards Risk. 12(1):1741–1777. doi: 10.1080/19475705.2021.1944330.
  • Sun D, Gu Q, Wen H, Xu J, Zhang Y, Shi S, Xue M, Zhou X. 2023. Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization. Gondwana Res. 123:89–106. doi: 10.1016/j.gr.2022.07.013.
  • Sun X, Chen J, Han X, Bao Y, Zhou X, Peng W. 2020. Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification. Bull Eng Geol Environ. 79(9):4657–4670. doi: 10.1007/s10064-020-01849-0.
  • Trinh T, Luu BT, Nguyen DH, Le THT, Pham SV, VuongThi N. 2023. A study of non-landslide samples and weights for mapping landslide susceptibility using regression and clustering methods. Earth Sci Inform. 16(4):4009–4034. doi: 10.1007/s12145-023-01144-y.
  • Wang Y, Fang Z, Hong H. 2019. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ. 666:975–993. doi: 10.1016/j.scitotenv.2019.02.263.
  • Wang Y, Feng L, Li S, Ren F, Du Q. 2020. A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China. CATENA. 188:104425. doi: 10.1016/j.catena.2019.104425.
  • Wei A, Yu K, Dai F, Gu F, Zhang W, Liu Y. 2022. Application of tree-based ensemble models to landslide susceptibility mapping: a comparative study. Sustainability. 14(10):6330. doi: 10.3390/su14106330.
  • Wolpert DH. 1992. Stacked generalization. Neural Netw. 5(2):241–259. doi: 10.1016/S0893-6080(05)80023-1.
  • Xiao X, Zou Y, Huang J, Luo X, Yang L, Li M, Yang P, Ji X, Li Y. 2024. An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest. Geomatics Nat Hazards Risk. 15(1):2347421. doi: 10.1080/19475705.2024.2347421.
  • Yan G, Liang S, Zhao H. 2017. An approach to improving slope unit division using GIS technique. Sci Geogr Sin. 37:1764–1770. doi: 10.13249/j.cnki.sgs.2017.11.019.
  • Yu L, Wang Y, Pradhan B. 2024. Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China. Geosci Front. 15(4):101802. doi: 10.1016/j.gsf.2024.101802.
  • Zeng T, Glade T, Xie Y, Yin K, Peduto D. 2023. Deep learning powered long-term warning systems for reservoir landslides. Int J Disaster Risk Reduct. 94:103820. doi: 10.1016/j.ijdrr.2023.103820.
  • Zeng T, Guo Z, Wang L, Jin B, Wu F, Guo R. 2023. Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity. Remote Sensing. 15(16):4111. doi: 10.3390/rs15164111.
  • Zeng T, Jin B, Glade T, Xie Y, Li Y, Zhu Y, Yin K. 2024. Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: A critical inquiry. CATENA. 236:107732. doi: 10.1016/j.catena.2023.107732.
  • Zeng T, Wu L, Hayakawa YS, Yin K, Gui L, Jin B, Guo Z, Peduto D. 2024. Advanced integration of ensemble learning and MT-InSAR for enhanced slow-moving landslide susceptibility zoning. Eng Geol. 331:107436. doi: 10.1016/j.enggeo.2024.107436.
  • Zeng T, Wu L, Peduto D, Glade T, Hayakawa YS, Yin K. 2023. Ensemble learning framework for landslide susceptibility mapping: different basic classifier and ensemble strategy. Geosci Front. 14(6):101645. doi: 10.1016/j.gsf.2023.101645.
  • Zhang Q, He Y, Zhang L, Lu J, Gao B, Yang W, Chen H, Zhang Y. 2024. A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network. Gondwana Res. 132:323–342. doi: 10.1016/j.gr.2024.04.013.
  • Zhang S, Ma Z, Li Y, Hu K, Zhang Q, Li L. 2021. A grid-based physical model to analyze the stability of slope unit. Geomorphology. 391:107887. doi: 10.1016/j.geomorph.2021.107887.
  • Zhao Z,He Yi,Yao S,Yang W,Wang W,Zhang L,Sun Q. 2022. A comparative study of different neural network models for landslide susceptibility mapping. Adv. Space Res. 70(2):383–401. 10.1016/j.asr.2022.04.055.
  • Zhao Z, Chen T, Dou J, Liu G, Plaza A. 2024. Landslide susceptibility mapping considering landslide local-global features based on CNN and transformer. IEEE J Sel Top Appl Earth Observ Remote Sensing. 17:7475–7489. doi: 10.1109/JSTARS.2024.3379350.
  • Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR. 2018. Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci. 112:23–37. doi: 10.1016/j.cageo.2017.11.019.
  • Zhu Q, Chen L, Hu H, Pirasteh S, Li H, Xie X. 2020. Unsupervised feature learning to improve transferability of landslide susceptibility representations. IEEE J Sel Top Appl Earth Observ Remote Sensing. 13:3917–3930. doi: 10.1109/JSTARS.2020.3006192.
  • Zhu Q, Zeng H, Ding Y, Xie X, Liu F, Zhang L, Li H, Hu H, Zhang J, Chen L. 2019. A review of major potential landslide hazards analysis. Acta Geod Cartogr Sin. 48(12):1551–1561. doi: 10.11947/j.AGCS.2019.20190452.