Landslide Susceptibility Assessment Considering Time-Varying of Dynamic Factors
Abstract
The selection of disaster-inducing factors and the construction of assessment models are two important stages in landslide susceptibility assessment. Existing research has neglected the influence of time-varying dynamic factors on landslide susceptibility. On the contrary, the best assessment model that fits the characteristics of disaster-pregnant environment in a certain area needs to be determined through a large number of comparative studies. In this paper, Boshan District, China was taken as the research area, and the assessment factor combination of static disaster-inducing factors and the change value of dynamic disaster-inducing factors in the current year was constructed. Landslide susceptibility modeling was carried out by random forest (RF), logistic regression (LR), support vector machine (SVM), stacking ensemble and convolutional neural network (CNN), and the assessment model with the highest accuracy was determined based on the area under the curve (AUC) method. Based on the geodetector, the interpretation degree of land use to landslide susceptibility and the relationship between the change information of dynamic disaster-inducing factors and the spatial distribution of landslide susceptibility were analyzed. The results show that CNN outperforms other models in the performance of modeling, with an AUC value of 0.920. The extremely high sensitive zones for landslides in the Boshan District were mainly distributed in the northwest, south, and east regions. The overall explanatory power of land use on landslide susceptibility shows a declining trend, suggesting that the spatial layout of land use in the Boshan District has been optimized. The rapidly growing population has destroyed the geological and hydrological environment and increased the probability of landslide susceptibility; Multiple acute changes of the normalized difference vegetation index (NDVI) in the opposite direction were more likely to trigger landslides, so it is not suitable to carry out logging, planting and other work repeatedly in a short time. This research can provide a theoretical foundation for land use planning and landslide prevention policies in the Boshan District.
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Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Grant No. 51808327) and Natural Science Foundation of Shandong Province (Grant No. ZR2019PEE016).
References
Bozzolan, E., E. A. Holcombe, F. Pianosi, I. Marchesini, M. Alvioli, and T. Wagener. 2023. “A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps.” Sci. Total Environ. 858 (Feb): 159412. https://doi.org/10.1016/j.scitotenv.2022.159412.
Bui, D. T., P. Tsangaratos, V.-T. Nguyen, N. Van Liem, and P. T. Trinh. 2020. “Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment.” Catena 188 (May): 104426. https://doi.org/10.1016/j.catena.2019.104426.
Caliskan, A., M. E. Yuksel, H. Badem, and A. Basturk. 2018. “Performance improvement of deep neural network classifiers by a simple training strategy.” Eng. Appl. Artif. Intell. 67 (Jan): 14–23. https://doi.org/10.1016/j.engappai.2017.09.002.
Cheng, J., X. Dai, Z. Wang, J. Li, G. Qu, W. Li, J. She, and Y. Wang. 2022. “Landslide susceptibility assessment model construction using typical machine learning for the three gorges reservoir area in China.” Remote Sens. 14 (9): 2257. https://doi.org/10.3390/rs14092257.
Dou, J., H. Yamagishi, H. R. Pourghasemi, A. P. Yunus, X. Song, Y. Xu, and Z. Zhu. 2015. “An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan.” Nat. Hazards 78 (Sep): 1749–1776. https://doi.org/10.1007/s11069-015-1799-2.
Du, G.-L., Y.-S. Zhang, J. Iqbal, Z.-H. Yang, and X. Yao. 2017. “Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China.” J. Mountain Sci. 14 (Feb): 249–268. https://doi.org/10.1007/s11629-016-4126-9.
Feng, H., Z. Miao, and Q. Hu. 2022. “Study on the uncertainty of machine learning model for earthquake-induced landslide susceptibility assessment.” Remote Sens. 14 (13): 2968. https://doi.org/10.3390/rs14132968.
Geological Professional Knowledge Service System. 2019. “Fault data of Shandong province.” Accessed November 19, 2023. http://geol.ckcest.cn/.
Ghorbanian, A., A. Mohammadzadeh, and S. Jamali. 2022. “Linear and non-linear vegetation trend analysis throughout Iran using two decades of MODIS NDVI imagery.” Remote Sens. 14 (15): 3683. https://doi.org/10.3390/rs14153683.
GS Cloud (Geospatial Data Cloud site). 2020 “Computer network information center.” Accessed May 13, 2023. http://www.gscloud.cn/.
Günther, A., P. Reichenbach, J.-P. Malet, M. Van Den Eeckhaut, J. Hervás, C. Dashwood, and F. Guzzetti. 2013. “Tier-based approaches for landslide susceptibility assessment in Europe.” Landslides 10 (Oct): 529–546. https://doi.org/10.1007/s10346-012-0349-1.
Guo, Z., J. V. Ferrer, M. Hürlimann, V. Medina, C. Puig-Polo, K. Yin, and D. Huang. 2023. “Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China.” Geosci. Front. 14 (4): 101542. https://doi.org/10.1016/j.gsf.2023.101542.
Hua, Y., X. Wang, Y. Li, P. Xu, and W. Xia. 2021. “Dynamic development of landslide susceptibility based on slope unit and deep neural networks.” Landslides 18 (Jan): 281–302. https://doi.org/10.1007/s10346-020-01444-0.
Ko, F. W. Y., and F. L. C. Lo. 2018. “From landslide susceptibility to landslide frequency: A territory-wide study in Hong Kong.” Eng. Geol. 242 (Aug): 12–22. https://doi.org/10.1016/j.enggeo.2018.05.001.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. “Imagenet classification with deep convolutional neural networks.” Commun. ACM 60 (6): 84–90. https://doi.org/10.1145/3065386.
Larochelle, H., Y. Bengio, J. Louradour, and P. Lamblin. 2009. “Exploring strategies for training deep neural networks.” J. Mach. Learn. Res. 10 (1): 1–40.
Li, Z., C. Yin, Z. Tan, X. Liu, S. Li, and X. Zhang. 2023. “Rainfall-seismic coupling effect induced landslide hazard assessment.” Nat. Hazards 118 (3): 2123–2152. https://doi.org/10.1007/s11069-023-06084-w.
Liu, C., C. Wang, Y. Li, and Y. Wang. 2022. “Spatiotemporal differentiation and geographic detection mechanism of ecological security in Chongqing, China.” Global Ecol. Conserv. 35 (Jun): e02072. https://doi.org/10.1016/j.gecco.2022.e02072.
Liu, S., L. Wang, W. Zhang, W. Sun, J. Fu, T. Xiao, and Z. Dai. 2023. “A physics-informed data-driven model for landslide susceptibility assessment in the three gorges reservoir area.” Geosci. Front. 26 (Apr): 101621. https://doi.org/10.1016/j.gsf.2023.101621.
Liu, W., Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi. 2017. “A survey of deep neural network architectures and their applications.” Neurocomputing 234 (Apr): 11–26. https://doi.org/10.1016/j.neucom.2016.12.038.
Manchado, A. M.-T., J. A. Ballesteros-Cánovas, S. Allen, and M. Stoffel. 2022. “Deforestation controls landslide susceptibility in Far-Western Nepal.” Catena 219 (Dec): 106627. https://doi.org/10.1016/j.catena.2022.106627.
Milich, L., and E. Weiss. 2000. “GAC NDVI interannual coefficient of variation (CoV) images: Ground truth sampling of the Sahel along north-south transects.” Int. J. Remote Sens. 21 (2): 235–260. https://doi.org/10.1080/014311600210812.
Mousavinezhad, M., A. Feizi, and M. Aalipour. 2023. “Performance evaluation of machine learning algorithms in change detection and change prediction of a watershed’s land use and land cover.” Int. J. Environ. Res. 17 (2): 29. https://doi.org/10.1007/s41742-023-00518-w.
Mushava, J., and M. Murray. 2022. “A novel XGBoost extension for credit scoring class-imbalanced data combining a generalized extreme value link and a modified focal loss function.” Expert Syst. Appl. 202 (Sep): 117233. https://doi.org/10.1016/j.eswa.2022.117233.
OpenStreetMap. 2024. “Free and open geographic database.” Accessed January 12, 2024. tps://www.openstreetmap.org/#map=5/34.574/113.247.
Pham, B. T., B. Pradhan, D. T. Bui, I. Prakash, and M. Dholakia. 2016. “A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India).” Environ. Modell. Software 84 (Jun): 240–250. https://doi.org/10.1016/j.envsoft.2016.07.005.
Pham, B. T., I. Prakash, S. K. Singh, A. Shirzadi, H. Shahabi, and D. T. Bui. 2019. “Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: Hybrid machine learning approaches.” Catena 175 (Apr): 203–218. https://doi.org/10.1016/j.catena.2018.12.018.
Pradhan, B. 2011. “Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modeling.” Environ. Ecol. Stat. 18 (3): 471–493. https://doi.org/10.1007/s10651-010-0147-7.
Qiu, D., R. Niu, Y. Zhao, and X. Wu. 2015. “Risk zoning of earthquake-induced landslides based on slope units: A case study on Lushan earthquake.” J. Jilin Univ. 45 (Aug): 1470–1478.
Research Center for Geographic Science and Resource Research, Chinese Academy of Sciences. 2021. “Natural resources database of Shandong province.” Accessed July 9, 2023. http://www.resdc.cn/.
Segoni, S., G. Pappafico, T. Luti, and F. Catani. 2020. “Landslide susceptibility assessment in complex geological settings: Sensitivity to geological information and insights on its parameterization.” Landslides 17 (Oct): 2443–2453. https://doi.org/10.1007/s10346-019-01340-2.
Shen, Z. J., M. Santosh, and A. Arabameri. 2023. “Application of novel hybrid model for land subsidence susceptibility mapping.” Geol. J. 58 (6): 2302–2320. https://doi.org/10.1002/gj.4603.
Singh, P., A. Sharma, U. Sur, and P. K. Rai. 2021. “Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India.” Environ. Dev. Sustainability 23 (Jul): 5233–5250. https://doi.org/10.1007/s10668-020-00811-0.
Song, Y., J. Wang, Y. Ge, and C. Xu. 2020. “An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data.” GISci. Remote Sens. 57 (5): 593–610. https://doi.org/10.1080/15481603.2020.1760434.
Su, Y., T. X. Li, S. K. Cheng, and X. Wang. 2020. “Spatial distribution exploration and driving factor identification for soil salinisation based on geodetector models in coastal area.” Ecol. Eng. 156 (Sep): 105961. https://doi.org/10.1016/j.ecoleng.2020.105961.
Sun, L. Y., B. J. Ma, L. Pei, X. H. Zhang, and J. L. Zhou. 2021. “The relationship of human activities and rainfall-induced landslide and debris flow hazards in Central China.” Nat. Hazards 107 (1): 147–169. https://doi.org/10.1007/s11069-021-04577-0.
Tekin, S., and T. Çan. 2022. “Slide type landslide susceptibility assessment of the Büyük Menderes watershed using artificial neural network method.” Environ. Sci. Pollut. Res. 29 (31): 47174–47188. https://doi.org/10.1007/s11356-022-19248-1.
Torizin, J., L.-C. Wang, M. Fuchs, B. Tong, D. Balzer, L.-Q. Wan, D. Kuhn, A. Li, and L. Chen. 2018. “Statistical landslide susceptibility assessment in a dynamic environment: A case study for Lanzhou City, Gansu Province, NW China.” J. Mountain Sci. 15 (6): 1299–1318. https://doi.org/10.1007/s11629-017-4717-0.
Tsopela, A., F.-V. Donzé, Y. Guglielmi, R. Castilla, and C. Gout. 2019. “Hydromechanical reactivation of natural discontinuities: Mesoscale experimental observations and DEM modeling.” Acta Geotech. 14 (Jun): 1585–1603. https://doi.org/10.1007/s11440-019-00791-0.
Wang, H., L. Zhang, H. Luo, J. He, and R. W. M. Cheung. 2021. “AI-powered landslide susceptibility assessment in Hong Kong.” Eng. Geol. 288 (Jun): 106103. https://doi.org/10.1016/j.enggeo.2021.106103.
Wang, X., F. Huang, X. Fan, H. Shahabi, A. Shirzadi, H. Bian, X. Ma, X. Lei, and W. Chen. 2022. “Landslide susceptibility modeling based on remote sensing data and data mining techniques.” Environ. Earth Sci. 81 (2): 50. https://doi.org/10.1007/s12665-022-10195-1.
Wu, W., Q. Zhang, V. P. Singh, G. Wang, J. Zhao, Z. Shen, and S. Sun. 2022. “A Data-Driven model on Google earth engine for landslide susceptibility assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau.” Remote Sens. 14 (18): 4662. https://doi.org/10.3390/rs14184662.
Xie, X., S. Pang, and J. Chen. 2020. “Hybrid recommendation model based on deep learning and Stacking integration strategy.” Intell. Data Anal. 24 (6): 1329–1344. https://doi.org/10.3233/IDA-194961.
Youssef, A. M., B. Pradhan, A. Dikshit, M. M. Al-Katheri, S. S. Matar, and A. M. Mahdi. 2022. “Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms: Comparison of their performance at Asir Region, KSA.” Bull. Eng. Geol. Environ. 81 (4): 165. https://doi.org/10.1007/s10064-022-02657-4.
Yu, X., Y. Xia, J. Zhou, and W. Jiang. 2023. “Landslide susceptibility mapping based on multitemporal remote sensing image change detection and multiexponential band math.” Sustainability 15 (3): 2226. https://doi.org/10.3390/su15032226.
Zhang, H., C. Yin, S. Wang, and B. Guo. 2023. “Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks.” Nat. Hazards 116 (2): 1931–1971. https://doi.org/10.1007/s11069-022-05748-3.
Zibo Municipal Bureau of Natural Resources and Planning. 2019. “Geological hazard survey data and geological hazard prevention plan of Boshan District.” Accessed March 16, 2023. https://gtj.zibo.gov.cn/.
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Published In
Natural Hazards Review
Volume 25 • Issue 3 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Aug 14, 2023
Accepted: Dec 20, 2023
Published online: Mar 27, 2024
Published in print: Aug 1, 2024
Discussion open until: Aug 27, 2024
ASCE Technical Topics:
- Comparative studies
- Disaster risk management
- Disasters and hazards
- Dynamic models
- Engineering fundamentals
- Geohazards
- Geotechnical engineering
- Infrastructure
- Land use
- Landslides
- Methodology (by type)
- Model accuracy
- Models (by type)
- Natural disasters
- Research methods (by type)
- Urban and regional development
- Urban areas
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