2,438
Views
0
CrossRef citations to date
0
Altmetric
Research Article

An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features

ORCID Icon, , , , , ORCID Icon, ORCID Icon & show all
Article: 2295408 | Received 03 Aug 2023, Accepted 11 Dec 2023, Published online: 17 Dec 2023

References

  • Archer, K. J., and R. V. Kimes. 2008. “Empirical Characterization of Random Forest Variable Importance Measures.” Computational Statistics & Data Analysis 52 (4): 2249–2260. doi:10.1016/j.csda.2007.08.015.
  • Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti. 2002. “A new Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms.” IEEE Transactions on Geoscience and Remote Sensing 40 (11): 2375–2383. doi:10.1109/TGRS.2002.803792.
  • Budimir, M. E. A., P. M. Atkinson, and H. G. Lewis. 2015. “A Systematic Review of Landslide Probability Mapping Using Logistic Regression.” Landslides 12 (3): 419–436. doi:10.1007/s10346-014-0550-5.
  • Cantarino, I., M. A. Carrion, F. Goerlich, and V. M. Ibañez. 2019. “A ROC Analysis-Based Classification Method for Landslide Susceptibility Maps.” Landslides 16 (2): 265–282. doi:10.1007/s10346-018-1063-4.
  • Chauhan, S., M. Sharma, M. K. Arora, and N. K. Gupta. 2010. “Landslide Susceptibility Zonation Through Ratings Derived from Artificial Neural Network.” International Journal of Applied Earth Observation and Geoinformation 12 (5): 340–350. doi:10.1016/j.jag.2010.04.006.
  • Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. “SMOTE: Synthetic Minority Over-Sampling Technique.” Journal of Artificial Intelligence Research 16: 321–357.
  • Chen, Q. 2016. “Analyzing landslide susceptibility in the Upper Mingjiang Basin Fuzhou, China”. In Chinese.
  • Chen, H. S., Y. He, L. F. Zhang, S. Yao, W. Yang, Y. M. Fang, Y. X. Liu, and B. H. Gao. 2023. “A Landslide Extraction Method of Channel Attention Mechanism U-Net Network Based on Sentinel-2A Remote Sensing Images.” International Journal of Digital Earth 16 (1): 552–577. doi:10.1080/17538947.2023.2177359.
  • Chen, S., Z. L. Miao, and L. X. Wu. 2022. “A Method for Seismic Landslide Hazard Assessment Using Simplified Newmark Displacement Model Based on Modified Strength Parameters of Rock Mass.” Acta Seismologica Sinica 44 (03): 512–527. doi:10.11939/jass.20210008.
  • Chen, T., Z. Y. Zhong, R. Q. Niu, T. Liu, and S. Y. Chen. 2020. “Mapping Landslide Susceptibility Based on Deep Belief Network.” Geomatics and Information Science of Wuhan University 45 (11): 1809–1817. doi:10.3866/PKU.WHXB201112303.
  • Dai, K. R., J. Deng, Q. Xu, Z. H. Li, X. L. Shi, C. Hancock, N. L. Wen, L. L. Zhang, and G. C. Zhou. 2022. “Interpretation and Sensitivity Analysis of the InSAR Line of Sight Displacements in Landslide Measurements.” GIScience & Remote Sensing 59 (1): 1226–1242. doi:10.1080/15481603.2022.2100054.
  • Dai, K. R., Z. H. Li, R. Tomás, G. X. Liu, B. Yu, X. W. Wang, H. Q. Cheng, J. J. Chen, and J. Stockamp. 2016. “Monitoring Activity at the Daguangbao Mega-Landslide (China) Using Sentinel-1 TOPS Time Series Interferometry.” Remote Sensing of Environment 186: 501–513. doi:10.1016/j.rse.2016.09.009.
  • Dai, K. R., Z. H. Li, Q. Xu, R. Burgmann, D. G. Milledge, R. Tomas, X. M. Fan, et al. 2020. “Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework.” IEEE Geoscience and Remote Sensing Magazine 8 (1): 136–153. doi:10.1109/MGRS.2019.2954395.
  • Dou, J., A. P. Yunus, D. T. Bui, A. Merghadi, M. Sahana, Z. F. Zhu, C. W. Chen, K. Khosravi, Y. Yang, and B. T. Pham. 2019. “Assessment of Advanced Random Forest and Decision Tree Algorithms for Modeling Rainfall-Induced Landslide Susceptibility in the Izu-Oshima Volcanic Island, Japan.” Science of The Total Environment 662: 332–346. doi:10.1016/j.scitotenv.2019.01.221.
  • Fan, X. M., A. P. Yunus, G. Scaringi, F. Catani, S. S. Subramanian, Q. Xu, and R. Q. Huang. 2021. “Rapidly Evolving Controls of Landslides After a Strong Earthquake and Implications for Hazard Assessments.” Geophysical Research Letters 48: e2020GL090509. doi:10.1029/2020GL090509.
  • Fang, Z. C., Y. Wang, L. Peng, and H. Y. Hong. 2020. “Integration of Convolutional Neural Network and Conventional Machine Learning Classifiers for Landslide Susceptibility Mapping.” Computers & Geosciences 139: 104470. doi:10.1016/j.cageo.2020.104470.
  • Gao, B. H., Y. He, X. Y. Chen, X. Y. Zheng, L. F. Zhang, Q. Zhang, and J. G. Lu. 2023a. “Landslide Risk Evaluation in Shenzhen Based on Stacking Ensemble Learning and InSAR.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16: 1–18. doi:10.1109/JSTARS.2023.3291490.
  • Gao, B. H., Y. He, L. F. Zhang, S. Yao, W. Yang, Y. Chen, X. He, Z. A. Zhao, and H. S. Chen. 2023b. “Dynamic Evaluation of Landslide Susceptibility by CNN Considering InSAR Deformation: A Case Study of Liujiaxia Reservoir.” Chinese Journal of Rock Mechanics and Engineering 42 (2): 450–465. doi:10.13722/j.cnki.jrme.2022.0266.
  • Hakim, W. L., F. Rezaie, A. S. Nur, M. Panahi, K. Khosravi, C. W. K. Lee, and S. Lee. 2022. “Convolutional Neural Network (CNN) with Metaheuristic Optimization Algorithms for Landslide Susceptibility Mapping in Icheon, South Korea.” Journal of Environmental Management 305: 114367. doi:10.1016/j.jenvman.2021.114367.
  • He, Y., Y. D. Chen, W. H. Wang, H. W. Yan, L. F. Zhang, and T. Liu. 2021b. “TS-InSAR Analysis for Monitoring Ground Deformation in Lanzhou New District, the Loess Plateau of China, from 2017 to 2019.” Advances in Space Research 67 (4): 1267–1283. doi:10.1016/j.asr.2020.11.004.
  • He, S., M. Hu, Z. H. Yang, X. Abudikeyimu, and K. Chen. 2022b. “Landslide Susceptibility Evaluation Based on Fuzzy Frequency Ratio and Entropy Index-an Example from Chongyi County.” Nonferrous Metals Science and Engineering 13 (04): 80–90. doi:10.13264/j.cnki.ysjskx.2022.04.010.
  • He, Y., W. H. Wang, H. W. Yan, L. F. Zhang, Y. D. Chen, and S. W. Yang. 2020. “Characteristics of Surface Deformation in Lanzhou with Sentinel-1A TOPS.” Geosciences 10 (3): 99. doi:10.3390/geosciences10030099.
  • He, Y., W. H. Wang, L. F. Zhang, Y. D. Chen, Y. Chen, B. S. Chen, X. He, and Z. A. Zhao. 2023a. “An Identification Method of Potential Landslide Zones Using InSAR Data and Landslide Susceptibility.” Geomatics, Natural Hazards and Risk 14 (1): 2185120. doi:10.1080/19475705.2023.2185120.
  • He, Y., H. W. Yan, W. Yang, S. Yao, L. F. Zhang, Y. Chen, and T. Liu. 2022a. “Time-Series Analysis and Prediction of Surface Deformation in the Jinchuan Mining Area, Gansu Province, by Using InSAR and CNN–PhLSTM Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 6732–6751. doi:10.1109/JSTARS.2022.3198728.
  • He, Y., S. Yao, Y. Chen, H. W. Yan, and L. F. Zhang. 2023b. “Spatio-temporal Prediction of Time-Series InSAR Land Subsidence Based on ConvLSTM Neural Network.” Geomatics and Information Science of Wuhan University. doi:10.13203/j.whugis20220657.
  • He, Y., S. Yao, W. Yang, H. W. Yan, L. F. Zhang, Z. Q. Wen, Y. L. Zhang, and T. Liu. 2021c. “An Extraction Method for Glacial Lakes Based on Landsat-8 Imagery Using an Improved U-Net Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 6544–6558. doi:10.1109/JSTARS.2021.3085397.
  • He, Y., Z. A. Zhao, W. Yang, H. W. Yan, W. H. Wang, S. Yao, L. F. Zhang, and T. Liu. 2021a. “A Unified Network of Information Considering Superimposed Landslide Factors Sequence and Pixel Spatial Neighbourhood for Landslide Susceptibility Mapping.” International Journal of Applied Earth Observation and Geoinformation 104: 102508. doi:10.1016/j.jag.2021.102508.
  • Huang, F. M., Z. S. Cao, J. F. Guo, S. H. Jiang, S. Li, and Z. Z. Guo. 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. M., J. S. Huang, S. H. Jiang, and C. B. Zhou. 2017. “Landslide Displacement Prediction Based on Multivariate Chaotic Model and Extreme Learning Machine.” Engineering Geology 218: 173–186. doi:10.1016/j.enggeo.2017.01.016.
  • Ji, S. P., D. W. Yu, C. Y. Shen, W. L. Li, and Q. Xu. 2020. “Landslide Detection from an Open Satellite Imagery and Digital Elevation Model Dataset Using Attention Boosted Convolutional Neural Networks.” Landslides 17 (6): 1337–1352. doi:10.1007/s10346-020-01353-2.
  • Keefer, D. K., and M. C. Larsen. 2007. “Assessing Landslide Hazards.” Science 316 (5828): 1136–1138. doi:10.1126/science.1143308.
  • Kim, J., J. A. Coe, Z. Lu, N. N. Avdievitch, and C. P. Hults. 2022. “Spaceborne InSAR Mapping of Landslides and Subsidence in Rapidly Deglaciating Terrain, Glacier Bay National Park and Preserve and Vicinity, Alaska and British Columbia.” Remote Sensing of Environment 281: 113231. doi:10.1016/j.rse.2022.113231.
  • Luna-Alvarez, A., D. Mujica-Vargas, M. Matuz-Cruz, J. M. V. Kinani, and E. Ramos-Diaz. 2020. “Self-driving Through a Time-Distributed Convolutional Recurrent Neural Network.” International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 1–6. doi:10.1109/cce50788.2020.9299181.
  • Lv, L., T. Chen, J. Dou, and A. Plaza. 2022. “A Hybrid Ensemble-Based Deep-Learning Framework for Landslide Susceptibility Mapping.” International Journal of Applied Earth Observation and Geoinformation 108: 102713. doi:10.1016/j.jag.2022.102713.
  • Ma, Z. J., and G. Mei. 2021. “Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities.” Earth-Science Reviews 223: 103858. doi:10.1016/j.earscirev.2021.103858.
  • Ma, Z. J., G. Mei, and F. Piccialli. 2020. “Machine Learning for Landslides Prevention: A Survey.” Neural Computing and Applications 33 (17): 10881–10907. doi:10.36227/techrxiv.12546098.v1.
  • Mantovani, J. R., G. T. Bueno, E. Alcântara, E. Park, A. P. Cunha, L. Londe, K. Massi, and J. A. Marengo. 2023. “Novel Landslide Susceptibility Mapping Based on Multi-Criteria Decision-Making in Ouro Preto, Brazil.” Journal of Geovisualization and Spatial Analysis 7 (1): 7. doi:10.1007/s41651-023-00138-0.
  • Meghanadh, D., V. K. Maurya, A. Tiwari, and R. Dwivedi. 2022. “A Multi-Criteria Landslide Susceptibility Mapping Using Deep Multi-Layer Perceptron Network: A Case Study of Srinagar-Rudraprayag Region (India).” Advances in Space Research, doi:10.1016/j.asr.2021.10.021.
  • Niu, P. F. 2021. Landslide Susceptibility Evaluation in Zhouqu County Based on Comprehensive Index Model. Hebei GEO University. doi:10.27752/d.cnki.gsjzj.2021.000012.
  • Novellino, A., M. Cesarano, P. Cappelletti, D. D. Martire, M. D. Napoli, M. Ramondini, A. SowtereD, and D. Calcaterra. 2021. “Slow-moving Landslide Risk Assessment Combining Machine Learning and InSAR Techniques.” Catena 203: 105317. doi:10.1016/j.catena.2021.105317.
  • Pham, B. T., D. T. Bui, H. R. Pourghasemi, P. Indra, and M. B. Dholakia. 2017a. “Landslide Susceptibility Assesssment in the Uttarakhand Area (India) Using GIS: A Comparison Study of Prediction Capability of Naïve Bayes, Multilayer Perceptron Neural Networks, and Functional Trees Methods.” Theoretical and Applied Climatology 128 (1-2): 255–273. doi:10.1007/s00704-015-1702-9.
  • Pham, B. T., D. T. Bui, I. Prakash, and M. B. Dholakia. 2017b. “Hybrid integration of Multilayer Perceptron Neural Networks and machine Learning Ensembles for Landslide Susceptibility Assessment at Himalayan Area (India) Using GIS.” Catena (Giessen) 149: 52–63. doi:10.1016/j.catena.2016.09.007.
  • Pourghasemi, H. R., and N. Kerle. 2016. “Random Forests and evidential Belief Function-based landslide Susceptibility Assessment in Western Mazandaran Province, Iran.” Environmental Earth Sciences 75 (3): 185. doi:10.1007/s12665-015-4950-1.
  • Pu, H. Y. 2022. “Landslide Susceptibility Evaluation Based on Improved BDN in Zhouqu County with the Support of InSAR Technology.” Lanzhou Jiaotong University, doi:10.27205/d.cnki.gltec.2022.001295.
  • Reichenbach, P., M. Rossi, B. D. Malamud, M. Mihir, and F. Guzzetti. 2018. “A Review of Statistically Based Landslide Susceptibility Models.” Earth-Science Reviews 180: 60–91. doi:10.1016/j.earscirev.2018.03.001.
  • Sun, C., Z. F. Wu, Z. Q. Lv, N. Yao, and J. B. Wei. 2013. “Quantifying Different Types of Urban Growth and the Change Dynamic in Guangzhou Using Multi-Temporal Remote Sensing Data.” International Journal of Applied Earth Observation and Geoinformation 21 (1): 409–417. doi:10.1016/j.jag.2011.12.012.
  • Thomas, A. V., S. Saha, J. H. Danumah, S. Raveendran, M. K. Prasad, R. S. Ajin, and S. L. Kuriakose. 2021. “Landslide Susceptibility Zonation of Idukki District Using GIS in the Aftermath of 2018 Kerala Floods and Landslides: A Comparison of AHP and Frequency Ratio Methods.” Journal of Geovisualization and Spatial Analysis 5 (2): 21. doi:10.1007/s41651-021-00090-x.
  • Wang, Y., Z. C. Fang, and H. Y. Hong. 2019. “Comparison of Convolutional Neural Networks for Landslide Susceptibility Mapping in Yanshan County, China.” Science of the Total Environment 666: 975–993. doi:10.1016/j.scitotenv.2019.02.263.
  • Wang, Y., Z. C. Fang, R. Q. Niu, and L. Peng. 2021. “Landslide Susceptibility Analysis Based on Deep Learning.” Journal of Geo-Information Science 23 (12): 2244–2260. doi:10.12082/dqxxkx.2021.210057.
  • Wang, Y., Z. C. Fang, M. Wang, L. Peng, and H. Y. Hong. 2020a. “Comparative Study of Landslide Susceptibility Mapping with Different Recurrent Neural Networks.” Computers & Geosciences 138: 104445. doi:10.1016/j.cageo.2020.104445.
  • Wang, W. H., Y. He, L. F. Zhang, Y. D. Chen, L. S. Qiu, and H. Y. Pu. 2020b. “Analysis of Surface Deformation and Driving Forces in Lanzhou.” Open Geosciences 12 (1): 1127–1145. doi:10.1515/geo-2020-0128.
  • Wang, Z. H., Z. W. Hu, W. J. Zhao, Q. Z. Guo, and S. M. Wan. 2015. “Research on Regional Landslide Susceptibility Assessment Based on Multiple Layer Perceptron-Taking the Hilly Area in Sichuan as Example.” Journal of Disaster Prevention and Mitigation Engineering 35 (5): 691–698. doi:10.13409/j.cnki.jdpme.2015.05.021.
  • Wang, X. M., X. L. Zhang, J. Bi, X. D. Zhang, S. Q. Deng, Z. W. Liu, L. Z. Wang, and H. X. Guo. 2022. “Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning.” International Journal of Environmental Research and Public Health 19: 14241. doi:10.3390/ijerph192114241.
  • Wei, R. L., C. M. Ye, T. B. Sui, Y. G. Ge, Y. Li, and J. Li. 2022. “Combining Spatial Response Features and Machine Learning Classifiers for Landslide Susceptibility Mapping.” International Journal of Applied Earth Observation and Geoinformation 107: 102681. doi:10.1016/j.jag.2022.102681.
  • Yao, J. M., X. Yao, Z. Zhao, and X. H. Liu. 2023. “Performance Comparison of Landslide Susceptibility Mapping Under Multiple Machine-Learning Based Models Considering InSAR Deformation: A Case Study of the Upper Jinsha River.” Geomatics, Natural Hazards and Risk 14 (1): 2212833. doi:10.1080/19475705.2023.2212833.
  • Yi, Y. N., Z. J. Zhang, W. C. Zhang, H. H. Jia, and J. Q. Zhang. 2020. “Landslide Susceptibility Mapping Using Multiscale Sampling Strategy and Convolutional Neural Network: A Case Study in Jiuzhaigou Region.” CATENA 195: 104851. doi:10.1016/j.catena.2020.104851.
  • Yuan, R., and J. Chen. 2022. “A Hybrid Deep Learning Method for Landslide Susceptibility Analysis with the Application of InSAR Data.” Natural Hazards 114: 1393–1426. doi:10.1007/s11069-022-05430-8.
  • Zhang, J. Q., M. Guo, and B. Xiao. 2021. “Image Description Based on GoogLeNet and Double-Layer GRU.” Journal of Shaanxi Normal University(Natural Science Edition) 49 (1): 68–73. doi:10.15983/j.cnki.jsnu.2021.01.009.
  • Zhang, T. Y., L. Han, J. C. Han, X. Li, H. Zhang, and H. Wang. 2019. “Assessment of Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with Kernel Logistic Regression Model.” Entropy 21 (2), doi:10.3390/e21020218.
  • Zhang, Y., X. M. Meng, C. Jordan, A. Novellino, T. Dijkstra, and G. Guan. 2018. “Investigating Slow-Moving Landslides in the Zhouqu Region of China Using Insar Time Series.” Landslides 15 (7): 1299–1315. doi:10.1007/s10346-018-0954-8.
  • Zhao, Z. A., Y. He, S. Yao, W. Yang, W. H. Wang, L. F. Zhang, and Q. Sun. 2022. “A Comparative Study of Different Neural Network Models for Landslide Susceptibility Mapping.” Advances in Space Research 70 (02): 383–401. doi:10.1016/j.asr.2022.04.055.
  • Zhu, Q., L. Chen, H. Hu, S. Pirasteh, H. F. Li, and X. Xie. 2020. “Unsupervised Feature Learning to Improve Transferability of Landslide Susceptibility Representations.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 3917–3930. doi:10.1109/JSTARS.2020.3006192.