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Project","award":["2021YFB2600600"],"award-info":[{"award-number":["2021YFB2600600"]}]},{"name":"National Natural Science Foundation Joint Fund Project","award":["U2034203"],"award-info":[{"award-number":["U2034203"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land resource management. In this study, an analysis was conducted on the landslide caused by Typhoon Megi in 2016. A representative mountainous area along the eastern coast of China\u2014characterized by urban development, deforestation, and severe road expansion\u2014was used to analyze the spatial distribution of landslides. For this purpose, high-precision Planet optical remote sensing images were used to obtain the landslide inventory related to the Typhoon Megi event. The main innovative features are as follows: (i) the newly developed patch generating land-use simulation (PLUS) model simulated and analyzed the driving factors of land-use land-cover (LULC) from 2010 to 2060; (ii) the innovative stacking strategy combined three strong ensemble models\u2014Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)\u2014to calculate the distribution of landslide susceptibility; and (iii) distance from road and LULC maps were used as short-term and long-term dynamic factors to examine the impact of human engineering activities on landslide susceptibility. The results show that the maximum expansion area of built-up land from 2010 to 2020 was 13.433 km2, mainly expanding forest land and cropland land, with areas of 8.28 km2 and 5.99 km2, respectively. The predicted LULC map for 2060 shows a growth of 45.88 km2 in the built-up land, mainly distributed around government residences in areas with relatively flat terrain and frequent socio-economic activities. The factor contribution shows that distance from road has a higher impact than LULC. The Stacking RF-XGB-LGBM model obtained the optimal AUC value of 0.915 in the landslide susceptibility analysis in 2016. Furthermore, future road network and urban expansion have intensified the probability of landslides occurring in urban areas in 2015. To our knowledge, this is the first application of the PLUS and Stacking RF-XGB-LGBM models in landslide susceptibility analysis in international literature. The research results can serve as a foundation for developing land management guidelines to reduce the risk of landslide failures.<\/jats:p>","DOI":"10.3390\/rs15164111","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:46:22Z","timestamp":1692665182000},"page":"4111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1241-3238","authenticated-orcid":false,"given":"Taorui","family":"Zeng","sequence":"first","affiliation":[{"name":"College of River and Ocean Engineering, Chongqing Jiaotong Univeristy, Chongqing 400047, China"},{"name":"Faculty of Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"ENGAGE\u2014Geomorphic Systems and Risk Research, Department of Geography and Regional Research, University of Vienna, 1010 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zizheng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9092-3700","authenticated-orcid":false,"given":"Linfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of River and Ocean Engineering, Chongqing Jiaotong Univeristy, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bijing","family":"Jin","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fayou","family":"Wu","sequence":"additional","affiliation":[{"name":"College of River and Ocean Engineering, Chongqing Jiaotong Univeristy, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rujun","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, S., Shao, X., and Xu, C. (2022). Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China. Remote Sens., 14.","DOI":"10.3390\/rs14174257"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1033085","DOI":"10.3389\/feart.2022.1033085","article-title":"Danqing Song. Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques-a case study from middle Yellow River catchment","volume":"10","author":"Guo","year":"2023","journal-title":"Front. Earth Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, Z., Tian, B., He, J., Xu, C., Zeng, T., and Zhu, Y. (2023). Hazard assessment for regional typhoon-triggered landslides by using physically-based model\u2014A case study from southeastern China. Georisk Assess. Manag. Risk, 1\u201315.","DOI":"10.1080\/17499518.2023.2188465"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jin, J., Huang, Q., Yuan, K., and Xu, C. (2022). A Data-Driven Model for Spatial Shallow Landslide Probability of Occurrence Due to a Typhoon in Ningguo City, Anhui Province, China. Remote Sens., 13.","DOI":"10.3390\/f13050732"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105667","DOI":"10.1016\/j.enggeo.2020.105667","article-title":"Quantitative risk assessment of slow-moving landslides from the viewpoint of decision-making: A case study of the Three Gorges Reservoir in China","volume":"273","author":"Guo","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhuang, Y., Xing, A., Sun, Q., Jiang, Y., Zhang, Y., and Wang, C. (2023). Failure and disaster-causing mechanism of a typhoon-induced large landslide in Yongjia, Zhejiang, China. Landslides.","DOI":"10.1007\/s10346-023-02099-3"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.accre.2022.07.002","article-title":"Identification of synoptic patterns for extreme rainfall events associated with landfalling typhoons in China during 1960\u20132020","volume":"13","author":"Zhao","year":"2022","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1005199","DOI":"10.3389\/feart.2022.1005199","article-title":"Developmental characteristics of rainfall-induced landslides from 1999 to 2016 in Wenzhou City of China","volume":"10","author":"Qin","year":"2022","journal-title":"Front. Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s11069-013-0755-2","article-title":"Composite risk assessment of typhoon-induced disaster for China\u2019s coastal area","volume":"69","author":"Yin","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1007\/s12583-021-1433-z","article-title":"An updated database and spatial distribution of landslides triggered by the milin, tibet Mw6.4 Earthquake of 18 November 2017","volume":"32","author":"Huang","year":"2021","journal-title":"J. Earth Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/s10064-023-03242-z","article-title":"Quantitative risk assessment of the Shilongmen reservoir landslide in the Three Gorges area of China","volume":"82","author":"Zeng","year":"2023","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103820","DOI":"10.1016\/j.ijdrr.2023.103820","article-title":"Deep learning powered long-term warning systems for reservoir landslides","volume":"94","author":"Zeng","year":"2023","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1007\/s00477-021-02145-3","article-title":"Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model","volume":"36","author":"Zeng","year":"2022","journal-title":"Stoch. Environ. Res. Risk A"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11108","DOI":"10.1038\/s41598-022-14037-9","article-title":"Groundwater level prediction based on a combined intelligence method for the Sifangbei landslide in the Three Gorges Reservoir Area","volume":"12","author":"Zeng","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117649","DOI":"10.1016\/j.jclepro.2019.117649","article-title":"The effect of urbanization on environmental pollution in rapidly developing urban agglomerations","volume":"237","author":"Liang","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1007\/s10661-012-2855-y","article-title":"Land use change and landslide characteristics analysis for community-based disaster mitigation","volume":"185","author":"Chen","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Glade, T., Anderson, M., and Crozier, M.J. (2005). Landslide Hazard and Risk: Issues, Concepts and Approach, John Wiley & Sons, Ltd.","DOI":"10.1002\/9780470012659"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s10346-022-01968-7","article-title":"Mapping the landslide susceptibility considering future land-use land-cover scenario","volume":"20","author":"Tyagi","year":"2023","journal-title":"Landslides"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.scitotenv.2017.05.231","article-title":"Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future","volume":"601","author":"Pisano","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1007\/s10346-023-02050-6","article-title":"Prolonged influence of urbanization on landslide susceptibility","volume":"20","author":"Rohan","year":"2023","journal-title":"Landslides"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106363","DOI":"10.1016\/j.catena.2022.106363","article-title":"Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India","volume":"216","author":"Hao","year":"2022","journal-title":"Catena"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"161430","DOI":"10.1016\/j.scitotenv.2023.161430","article-title":"Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China","volume":"866","author":"Xiong","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.5194\/nhess-19-2207-2019","article-title":"The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuan\u2019en County (Hubei, China)","volume":"19","author":"Chen","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.apgeog.2014.05.020","article-title":"Analysis of land cover changes in the past and the future as contribution to landslide risk scenarios","volume":"53","author":"Promper","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101542","DOI":"10.1016\/j.gsf.2023.101542","article-title":"Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China","volume":"14","author":"Guo","year":"2023","journal-title":"Geosci. Front."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"133557","DOI":"10.1016\/j.scitotenv.2019.07.363","article-title":"Relation between land cover and landslide susceptibility in Val d\u2019Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction","volume":"693","author":"Shu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, J., Xiong, N., Liang, B., Wang, Z., and Cressey, E. (2022). Spatial and Temporal Variation, Simulation and Prediction of Land Use in Ecological Conservation Area of Western Beijing. Remote Sens., 14.","DOI":"10.3390\/rs14061452"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101569","DOI":"10.1016\/j.compenvurbsys.2020.101569","article-title":"Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China","volume":"85","author":"Liang","year":"2021","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, J., Yang, Y., Feng, Z., Huang, R., Zhou, G., You, H., and Han, X. (2023). Ecological Risk Assessment and Prediction Based on Scale Optimization\u2014A Case Study of Nanning, a Landscape Garden City in China. Remote Sens., 15.","DOI":"10.3390\/rs15051304"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"134004","DOI":"10.1016\/j.jclepro.2022.134004","article-title":"Land use optimization in Ningbo City with a coupled GA and PLUS model","volume":"375","author":"Li","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107097","DOI":"10.1016\/j.catena.2023.107097","article-title":"Coordination of economic development and ecological conservation during spatiotemporal evolution of land use\/cover in eco-fragile areas","volume":"226","author":"Zhang","year":"2023","journal-title":"Catena"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cageo.2017.11.019","article-title":"Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China","volume":"112","author":"Zhou","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108236","DOI":"10.1016\/j.geomorph.2022.108236","article-title":"Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold","volume":"408","author":"Huang","year":"2022","journal-title":"Geomorphology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.gsf.2023.101645","article-title":"Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy","volume":"14","author":"Zeng","year":"2023","journal-title":"Geosci. Front."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105354","DOI":"10.1016\/j.envsoft.2022.105354","article-title":"FSLAM: A QGIS plugin for fast regional susceptibility assessment of rainfall-induced landslides","volume":"150","author":"Guo","year":"2022","journal-title":"Environ. Model. Softw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1007\/s12583-020-1380-0","article-title":"Active Landslide Detection Based on Sentinel-1 Data and InSAR Technology in Zhouqu County, Gansu Province, Northwest China","volume":"32","author":"Dai","year":"2021","journal-title":"J. Earth Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101249","DOI":"10.1016\/j.gsf.2021.101249","article-title":"Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management","volume":"12","author":"Guo","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.catena.2020.104580","article-title":"Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping","volume":"191","author":"Huang","year":"2020","journal-title":"Catena"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Huang, F., Xiong, H., Yao, C., Catani, F., Zhou, C., and Huang, J. (J. Rock Mech. Geotech. Eng., 2023). Uncertainties of landslide susceptibility prediction considering different landslide types, J. Rock Mech. Geotech. Eng., in press.","DOI":"10.1016\/j.jrmge.2023.03.001"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jin, B., Yin, K., Li, Q., Gui, L., Yang, T., Zhao, B., Guo, B., Zeng, T., and Ma, Z. (2022). Susceptibility Analysis of Land Subsidence along the Transmission Line in the Salt Lake Area Based on Remote Sensing Interpretation. Remote Sens., 14.","DOI":"10.3390\/rs14133229"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1007\/s11053-021-09822-8","article-title":"Estimating Air Over-pressure Resulting from Blasting in Quarries Based on a Novel Ensemble Model (GLMNETs\u2013MLPNN)","volume":"30","author":"Nguyen","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101317","DOI":"10.1016\/j.gsf.2021.101317","article-title":"Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions","volume":"13","author":"Huang","year":"2022","journal-title":"Geosci. Front."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Huang, F., Tao, S., Li, D., Lian, Z., Catani, F., Huang, J., Li, K., and Zhang, C. (2022). Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies. Remote Sens., 14.","DOI":"10.3390\/rs14184436"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2015.04.007","article-title":"Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling","volume":"81","author":"Goetz","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1007\/s10064-022-02672-5","article-title":"The uncertainty of landslide susceptibility prediction modeling: Suitability of linear conditioning factors","volume":"81","author":"Huang","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, S., Yin, K., Zhou, C., Gui, L., Liang, X., Lin, W., and Zhao, B. (2021). Susceptibility Assessment for Landslide Initiated along Power Transmission Lines. Remote Sens., 13.","DOI":"10.3390\/rs13245068"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, Q., Jiang, Z., Wang, M., and Liu, K. (2021). Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods. Remote Sens., 13.","DOI":"10.3390\/rs13081572"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kadavi, P., Lee, C., and Lee, S. (2018). Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens., 10.","DOI":"10.3390\/rs10081252"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10346-019-01286-5","article-title":"Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan","volume":"17","author":"Dou","year":"2020","journal-title":"Landslides"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"712240","DOI":"10.3389\/feart.2021.712240","article-title":"Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping","volume":"9","author":"Wang","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.gsf.2020.04.014","article-title":"Modelling of shallow landslides with machine learning algorithms","volume":"12","author":"Liu","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1007\/s00477-021-02032-x","article-title":"Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China","volume":"36","author":"Li","year":"2022","journal-title":"Stoch. Environ. Res. Risk A"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Karimi-Sangchini, E., Pal, S.C., Saha, A., Chowdhuri, I., Lee, S., and Tien Bui, D. (2020). Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility. Remote Sens., 12.","DOI":"10.3390\/rs12203389"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, P., Xu, C., Ma, S., Shao, X., Tian, Y., and Wen, B. (2020). Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan. Remote Sens., 12.","DOI":"10.3390\/rs12233992"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1139\/cgj-2019-0751","article-title":"A cautionary note for rock avalanche field investigation; recent sequential and overlapping landslides in British Columbia","volume":"58","author":"Geertsema","year":"2021","journal-title":"Can. Geotech. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107989","DOI":"10.1016\/j.geomorph.2021.107989","article-title":"Rapid vegetation recovery at landslide scars detected by multitemporal high-resolution satellite imagery at Aso volcano, Japan","volume":"398","author":"Saito","year":"2022","journal-title":"Geomorphology"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10346-013-0436-y","article-title":"The Varnes classification of landslide types, an update","volume":"11","author":"Hungr","year":"2014","journal-title":"Landslides"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2017.04.013","article-title":"Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine","volume":"223","author":"Huang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3531","DOI":"10.1007\/s10346-021-01693-7","article-title":"Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: A national-scale assessment for Austria","volume":"18","author":"Lima","year":"2021","journal-title":"Landslides"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.gsf.2020.05.006","article-title":"Big data and machine learning in geoscience and geoengineering: Introduction","volume":"12","author":"Zhang","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1007\/s11629-021-7254-9","article-title":"Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility","volume":"19","author":"Lima","year":"2022","journal-title":"J. Mt. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s10346-022-02020-4","article-title":"Land use and land cover as a conditioning factor in landslide susceptibility: A literature review","volume":"20","author":"Korup","year":"2023","journal-title":"Landslides"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.geomorph.2010.12.013","article-title":"Catastrophic landslide induced by Typhoon Morakot, Shiaolin, Taiwan","volume":"127","author":"Tsou","year":"2011","journal-title":"Geomorphology"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4701","DOI":"10.1007\/s10064-020-01859-y","article-title":"Formation and chemo-mechanical characteristics of weak clay interlayers between alternative mudstone and sandstone sequence of gently inclined landslides in Nanjiang, SW China","volume":"79","author":"Liu","year":"2020","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.1080\/13658816.2018.1502441","article-title":"Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model","volume":"32","author":"Liang","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"155238","DOI":"10.1016\/j.scitotenv.2022.155238","article-title":"Land use\/land cover prediction and analysis of the middle reaches of the Yangtze River under different scenarios","volume":"833","author":"Zhang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916), New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_70","first-page":"1","article-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked Generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1007\/s10346-021-01669-7","article-title":"Machine learning-based thermokarst landslide susceptibility modeling across the permafrost region on the Qinghai-Tibet Plateau","volume":"18","author":"Yin","year":"2021","journal-title":"Landslides"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"104805","DOI":"10.1016\/j.catena.2020.104805","article-title":"Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping","volume":"195","author":"Pham","year":"2020","journal-title":"Catena"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/j.landusepol.2011.11.010","article-title":"Land-use changes and policy dimension driving forces in China: Present, trend and future","volume":"29","author":"Wang","year":"2012","journal-title":"Land Use Policy"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.gloenvcha.2018.04.001","article-title":"Land use projections in China under global socioeconomic and emission scenarios: Utilizing a scenario-based land-use change assessment framework","volume":"50","author":"Dong","year":"2018","journal-title":"Glob. Environ. Chang."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"117543","DOI":"10.1016\/j.jenvman.2023.117543","article-title":"Simulation of future land use\/cover change (LUCC) in typical watersheds of arid regions under multiple scenarios","volume":"335","author":"Wang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/13658816.2015.1066791","article-title":"International Journal of Geographical Information Science","volume":"30","author":"Green","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1016\/j.jrmge.2021.12.011","article-title":"Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China","volume":"14","author":"Zhang","year":"2022","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.catena.2005.11.006","article-title":"Landsliding related to land-cover change: A diachronic analysis of hillslope instability distribution in the Sierra Norte, Puebla, Mexico","volume":"65","author":"Parrot","year":"2006","journal-title":"Catena"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1007\/s00267-014-0357-0","article-title":"The influence of land use change on landslide susceptibility zonation: The Briga catchment test site (Messina, Italy)","volume":"54","author":"Reichenbach","year":"2014","journal-title":"Environ. Manag."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s10346-020-01348-z","article-title":"Experimental study on the effects of tree planting on slope stability","volume":"17","author":"Lan","year":"2020","journal-title":"Landslides"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1525\/bio.2011.61.11.6","article-title":"The influence of plant root systems on subsurface flow: Implications for slope stability","volume":"61","author":"Ghestem","year":"2011","journal-title":"Bioscience"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10346-021-01775-6","article-title":"Impacts of future climate and land cover changes on landslide susceptibility: Regional scale modelling in the Val d\u2019Aran region (Pyrenees, Spain)","volume":"19","author":"Guo","year":"2022","journal-title":"Landslides"}],"updated-by":[{"DOI":"10.3390\/rs15235549","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T14:22:09Z","timestamp":1754230929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,21]]},"references-count":83,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15164111"],"URL":"https:\/\/doi.org\/10.3390\/rs15164111","relation":{"correction":[{"id-type":"doi","id":"10.3390\/rs15235549","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,21]]}}}