{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T14:48:11Z","timestamp":1778424491084,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41761081"],"award-info":[{"award-number":["41761081"]}]},{"name":"National Natural Science Foundation of China","award":["42161067"],"award-info":[{"award-number":["42161067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments.<\/jats:p>","DOI":"10.3390\/s22208041","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China"],"prefix":"10.3390","volume":"22","author":[{"given":"Bo","family":"Xiao","sequence":"first","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"},{"name":"Faculty of Road and Construction Engineering, Yunnan Communications Vocational and Technical College, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junsan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsheng","family":"Li","sequence":"additional","affiliation":[{"name":"International Cooperation Department, Kunming Metallurgy College, Kunming 650033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"},{"name":"Faculty of Road and Construction Engineering, Yunnan Communications Vocational and Technical College, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenfei","family":"Xi","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"},{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","first-page":"377","article-title":"Types of Potential Landslide and Corresponding Identification Technologies","volume":"47","author":"Xu","year":"2022","journal-title":"Geom. Inf. Sci. Wuhan Univ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.5194\/nhess-19-93-2019","article-title":"Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China","volume":"19","author":"Zhang","year":"2019","journal-title":"Nat. Hazards Earth Sys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1007\/s10346-018-1037-6","article-title":"Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016","volume":"15","author":"Lin","year":"2018","journal-title":"Landslides"},{"key":"ref_4","first-page":"108","article-title":"Combined SBAS-InSAR and PSO-BP algorithm for evaluating the risk of geological disasters in alpine valley regions","volume":"37","author":"Zhou","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.geomorph.2014.02.003","article-title":"An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic","volume":"214","author":"Zhu","year":"2014","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1061\/(ASCE)CP.1943-5487.0000034","article-title":"Knowledge-Based Landslide Susceptibility Zonation System","volume":"24","author":"Ghosh","year":"2009","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.cageo.2012.11.003","article-title":"Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal","volume":"52","author":"Kayastha","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s11069-012-0217-2","article-title":"Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran","volume":"63","author":"Pourghasemi","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s11069-015-2075-1","article-title":"Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)","volume":"81","author":"Myronidis","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s42452-020-2563-0","article-title":"Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia","volume":"2","author":"Wubalem","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"He, H., Hu, D., Sun, Q., Zhu, L., and Liu, Y. (2019). A Landslide Susceptibility Assessment Method Based on GIS Technology and an AHP-Weighted Information Content Method: A Case Study of Southern Anhui, China. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8060266"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yu, C., and Chen, J. (2020). Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model. Symmetry, 12.","DOI":"10.3390\/sym12111848"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1080\/19475705.2021.1896584","article-title":"GIS-based soil planar slide susceptibility mapping using logistic regression and neural networks: A typical red mudstone area in southwest China","volume":"12","author":"Zhang","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, Y., Deng, S., Chen, M., Fang, T., and Tan, H. (2019). Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8080332"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11069-020-04452-4","article-title":"Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method","volume":"106","author":"Xing","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_17","first-page":"11","article-title":"Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan","volume":"22","author":"Khan","year":"2019","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Panchal, S., and Shrivastava, A.K. (2021). A Comparative Study of Frequency Ratio, Shannon\u2019s Entropy and Analytic Hierarchy Process (AHP) Models for Landslide Susceptibility Assessment. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090603"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2012.03.003","article-title":"Application of an evidential belief function model in landslide susceptibility mapping","volume":"44","author":"Althuwaynee","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s10346-015-0576-3","article-title":"Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map","volume":"13","author":"Ilia","year":"2015","journal-title":"Landslides"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1080\/10106049.2019.1582716","article-title":"Landslide susceptibility assessment using different slope units based on the evidential belief function model","volume":"35","author":"Chen","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11069-012-0347-6","article-title":"Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling\u2013Narayanghat road section in Nepal Himalaya","volume":"65","author":"Devkota","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1080\/10106049.2017.1404143","article-title":"Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China","volume":"34","author":"Chen","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.catena.2018.03.003","article-title":"Review on landslide susceptibility mapping using support vector machines","volume":"165","author":"Huang","year":"2018","journal-title":"Catena"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"58963","DOI":"10.3389\/feart.2021.589630","article-title":"Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models","volume":"9","author":"Zhao","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1080\/10106049.2017.1323964","article-title":"Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea","volume":"33","author":"Kim","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s11069-017-3104-z","article-title":"Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada","volume":"90","author":"Behnia","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_28","first-page":"617","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_29","doi-asserted-by":"crossref","unstructured":"Park, S.J., Lee, C.W., Lee, S., and Lee, M.J. (2018). Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea. Remote Sens., 10.","DOI":"10.3390\/rs10101545"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1080\/10106049.2020.1737972","article-title":"Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers","volume":"37","author":"Pham","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10064-017-1034-3","article-title":"Landslide susceptibility mapping at Ovac\u0131k-Karab\u00fck (Turkey) using different artificial neural network models: Comparison of training algorithms","volume":"78","author":"Can","year":"2017","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_33","first-page":"5693","article-title":"Artificial neural network and sensitivity analysis in the landslide susceptibility mapping of Idukki district, India","volume":"37","author":"Saravanan","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ren, T., Gong, W., Gao, L., Zhao, F., and Cheng, Z. (2022). An Interpretation Approach of Ascending\u2013Descending SAR Data for Landslide Identification. Remote Sens., 14.","DOI":"10.3390\/rs14051299"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105244","DOI":"10.1016\/j.enggeo.2019.105244","article-title":"Slow-moving landslides interacting with the road network: Analysis of damage using ancillary data, in situ surveys and multi-source monitoring data","volume":"260","author":"Nappo","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, K., Deng, J., Ge, D., Liang, R., Li, W., and Xu, Q. (2021). Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR. Remote Sens., 13.","DOI":"10.3390\/rs13183662"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111370","DOI":"10.1016\/j.rse.2019.111370","article-title":"Improved correction of seasonal tropospheric delay in InSAR observations for landslide deformation monitoring","volume":"233","author":"Dong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","first-page":"102812","article-title":"A new algorithm for landslide dynamic monitoring with high temporal resolution by Kalman filter integration of multiplatform time-series InSAR processing","volume":"110","author":"Cai","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"56364","DOI":"10.1109\/ACCESS.2021.3072199","article-title":"Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm","volume":"9","author":"Jiang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Moretto, S., Bozzano, F., and Mazzanti, P. (2021). The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings. Remote Sens., 13.","DOI":"10.3390\/rs13183735"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, D., Zuo, X., and Zhao, Z. (2022). Constructing a Large-Scale Urban Land Subsidence Prediction Method Based on Neural Network Algorithm from the Perspective of Multiple Factors. Remote Sens., 14.","DOI":"10.3390\/rs14081803"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qiu, H., Zhu, Y., Liu, Y., Yang, D., Ma, S., Zhang, J., Wang, Y., Wang, L., and Tang, B. (2022). Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases. Remote Sens., 14.","DOI":"10.3390\/rs14041026"},{"key":"ref_43","first-page":"398","article-title":"Monitoring Land Subsidence and Fault Activity in Hefei City Based on MT-InSAR","volume":"41","author":"Yu","year":"2020","journal-title":"J. Geod. Geodyn."},{"key":"ref_44","first-page":"731","article-title":"The Research Progress in Measurement of Fault Activity by Times Series InSAR and Discussion of Related Issues","volume":"36","author":"Qu","year":"2014","journal-title":"Seismol. Geol."},{"key":"ref_45","unstructured":"Qiu, H.J. (2012). Study on the Regional Landslide Characteristic Analysis and Hazard Assessment: A Case Study of Ningqiang County. [Ph.D. Thesis, Northwest University]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1007\/s11442-015-1245-0","article-title":"Slope spectrum critical area and its spatial variation in the Loess Plateau of China","volume":"25","author":"Tang","year":"2015","journal-title":"J. Geogr. Sci."},{"key":"ref_47","first-page":"8854606","article-title":"A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units","volume":"2021","author":"Zhao","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101104","DOI":"10.1016\/j.gsf.2020.10.009","article-title":"Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms","volume":"12","author":"Balogun","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hussain, M.A., Chen, Z., Zheng, Y., Shoaib, M., Shah, S.U., Ali, N., and Afzal, Z. (2022). Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique. Sensors, 22.","DOI":"10.3390\/s22093119"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"055003","DOI":"10.1088\/1748-9326\/abf395","article-title":"Random forest-based understanding and predicting of the impacts of anthropogenic nutrient inputs on the water quality of a tropical lagoon","volume":"16","author":"Fang","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geomorph.2017.09.006","article-title":"Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion","volume":"298","author":"Rahmati","year":"2017","journal-title":"Geomorphology"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"104150","DOI":"10.1016\/j.catena.2019.104150","article-title":"Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh), India","volume":"182","author":"Pandey","year":"2019","journal-title":"Catena"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","article-title":"A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8041\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:46Z","timestamp":1760144326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":54,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22208041"],"URL":"https:\/\/doi.org\/10.3390\/s22208041","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}