Skip to main content

Advertisement

SpringerLink
Log in

Different slope units division-based geohazard susceptibility evaluation of support vector machine optimized by sparrow search algorithm

  • Review
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

Regional geohazards susceptibility assessment is an important task for government departments to prevent and mitigate geohazards and plan land space. Pingshan County in Hebei Province, China is taken as the research area. Flow accumulation grading values of 500, 750, 1000, 1250, and 1500 based on the hydrological analysis method (HAM) and elevation filtering grading values of 10, 15, 20, 25, and 30 based on the surface curvature watershed method (SCWM) use different slope units as evaluation units. The sparrow search algorithm is introduced to optimize the support vector machine model (SSA-SVM) and evaluate geohazard susceptibility. According to the area under the receiver operating characteristic curve (AUC), the optimal grading values are determined. Then the generalization ability of the SSA-SVM model is discussed with frequency ratio. The results show that the AUC values reach the maximum of 0.989 and 0.994 respectively when the flow accumulation grading value in the HAM is 1250 and the elevation filtering grading value in the SCWM is 20. The frequency ratios of the highly susceptible areas are 1.88 and 1.63, respectively. Therefore, selecting the appropriate grading value when dividing the slope unit can make the prediction more accurate. The slope unit divided by the SCWM is more accurate. Besides, the slope units divided by the HAM possess higher geohazard point density and stronger generalization ability in highly susceptible areas. The optimal slope unit grading values and the geohazard susceptibility evaluation model proposed can provide reference for strategies to prevent and mitigate geohazards and related research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig.8
Fig. 9

Similar content being viewed by others

References

  • Abdollahi S, Pourghasemi HR, Ghanbarian GA, Safaeian R (2019) Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions. Bull Eng Geol Env 78:4017–4034

    Article  Google Scholar 

  • Ba Q, Chen Y, Deng S, Yang J, Li H (2018) A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inf 11(3):373–388

    Article  Google Scholar 

  • Carrara A, Crosta G, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94(3–4):353–378

    Article  Google Scholar 

  • Chang M, Dou X, Tang L, Xu H (2022) Risk assessment of multi-disaster in mining area of Guizhou, China. Int J Disaster Risk Reduct 78:103128

    Article  Google Scholar 

  • Chen G, Li S (2020) Research on location fusion of spatial geological disaster based on fuzzy SVM. Comput Commun 153:538–544

    Article  Google Scholar 

  • Chen Z, Liang S, Ke Y, Yang Z, Zhao H (2019) Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin. NW China Geocarto Int 34(4):348–367

    Article  Google Scholar 

  • Chen Z, Song D, Juliev M, Pourghasemi HR (2021) Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model. Environmental Earth Sciences 80(8):1–19

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102(3–4):496–510

    Article  Google Scholar 

  • Du G, Zhang Y, Yang Z, Iqbal J, Tong B, Guo C, Wu R (2017) Estimation of seismic landslide Hazard in the eastern Himalayan syntaxis region of Tibetan plateau. Acta Geol Sinica-Engl Ed 91:658–668

    Article  Google Scholar 

  • Gao R, Wang C, Liang Z, Han S, Li B (2021) A research on susceptibility mapping of multiple geological hazards in Yanzi river basin. China ISPRS Int J Geo-Inf 10(4):218

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299

    Article  Google Scholar 

  • Hepdeniz K (2020) Using the analytic hierarchy process and frequency ratio methods for landslide susceptibility mapping in Isparta-Antalya highway (D-685), Turkey. Arab J Geosci 13:1–16. https://doi.org/10.1007/s12665-012-1842-5

    Article  Google Scholar 

  • Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City. China Arabian J Geosci 9(2):1–26

    Google Scholar 

  • Huang P, Peng L, Pan H (2020) Linking the random forests model and GIS to assess geo-hazards risk: a case study in Shifang County, China. IEEE Access 8:28033–28042

    Article  Google Scholar 

  • Huang J, Zeng X, Ding L, Yin Y, Li Y (2022) Landslide susceptibility evaluation using different slope units based on BP neural network. Comput Intell Neurosci. https://doi.org/10.1155/2022/9923775

    Article  Google Scholar 

  • Jia N, Mitani Y, Xie M, Djamaluddin I (2012) Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area. Comput Geotech 45:1–10

    Article  Google Scholar 

  • Lv J, Sun W, Wang H, Zhang F (2021) Coordinated approach fusing RCMDE and sparrow search algorithm-based SVM for fault diagnosis of rolling bearings. Sensors 21(16):5297

    Article  Google Scholar 

  • Ma D, Duan H, Cai X, Li Z, Li Q, Zhang Q (2018) A global optimization-based method for the prediction of water inrush hazard from mining floor. Water 10(11):1618

    Article  Google Scholar 

  • Mallick J, Alqadhi S, Talukdar S, AlSubih M, Ahmed M, Khan RA, Kahla NB, Abutayeh SM (2021) Risk assessment of resources exposed to rainfall induced landslide with the development of GIS and RS based ensemble metaheuristic machine learning algorithms. Sustainability 13:457. https://doi.org/10.3390/su13020457

    Article  Google Scholar 

  • Mowen X, Tetsuro ESAKI, Cheng Q, Lin J (2007) Spatial three-dimensional landslide susceptibility mapping tool and its applications. Earth Sci Front 14(6):73–84

    Article  Google Scholar 

  • Nagaveni C, Kumar KP, Ravibabu MV (2019) Evaluation of TanDEMx and SRTM DEM on watershed simulated run off estimation. J Earth Syst Sci 128(1):1–11

    Article  Google Scholar 

  • Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71:523–547

    Article  Google Scholar 

  • Rossi M, Guzzetti F, Reichenbach P, Mondini AC, Peruccacci S (2010) Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 114(3):129–142

    Article  Google Scholar 

  • Rotigliano E, Cappadonia C, Conoscenti C, Costanzo D, Agnesi V (2012) Slope units-based flow susceptibility model: using validation tests to select controlling factors. Nat Hazards 61(1):143–153

    Article  Google Scholar 

  • Ruff M, Czurda K (2008) Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology 94(3–4):314–324

    Article  Google Scholar 

  • Sahana M, Rehman S, Sajjad H, Hong H (2020) Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: a study of sundarban biosphere reserve. India J Catena 189:104450. https://doi.org/10.1016/j.catena.2019.104450

    Article  Google Scholar 

  • Schlögel R, Marchesini I, Alvioli M, Reichenbach P, Rossi M, Malet JP (2018) Optimizing landslide susceptibility zonation: effects of DEM spatial resolution and slope unit delineation on logistic regression models. Geomorphology 301:10–20

    Article  Google Scholar 

  • Senouci R, Taibi N-E, Teodoro AC, Duarte L, Mansour H, Yahia Meddah R (2021) GIS-based expert knowledge for landslide susceptibility mapping (LSM): case of mostaganem coast district. West of Algeria J Sustain 13:630. https://doi.org/10.3390/su13020630

    Article  Google Scholar 

  • Sun L, Ma C, Li Y (2019) Multiple geo-environmental hazards susceptibility assessment: a case study in Luoning County, Henan Province, China. Geomat Nat Haz Risk 10(1):2009–2029

    Article  Google Scholar 

  • 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 Env 79(9):4657–4670

    Article  Google Scholar 

  • Sun X, Yu C, Li Y, Rene NN (2022) Susceptibility mapping of typical geological hazards in Helong city affected by volcanic activity of Changbai mountain, Northeastern China. ISPRS Int J Geo Inf 11(6):344

    Article  Google Scholar 

  • Tan Q, Huang Y, Hu J, Zhou P, Hu J (2021) Application of artificial neural network model based on GIS in geological hazard zoning. Neural Comput Appl 33(2):591–602

    Article  Google Scholar 

  • Tang RX, Yan EC, Wen T, Yin XM, Tang W (2021) Comparison of logistic regression, information value, and comprehensive evaluating model for landslide susceptibility mapping. J Sustain 13:3803. https://doi.org/10.3390/su13073803

    Article  Google Scholar 

  • Tempola F, Muhammad M, Khairan A (2018, September) Naive bayes classifier for prediction of volcanic status in indonesia. In: 2018 5th International conference on information technology, computer, and electrical engineering (ICITACEE), pp. 365–369. IEEE

  • Tian Y, Xiao C, Wu L (2010, June) Slope unit-based landslide susceptibility zonation. In: 2010 18th international conference on geoinformatics, pp. 1–5. IEEE

  • Tuerxun W, Chang X, Hongyu G, Zhijie J, Huajian Z (2021) Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm. Ieee Access 9:69307–69315

    Article  Google Scholar 

  • Xingli J, Qingmiao D, Hongzhi Y (2019) Susceptibility zoning of karst geological hazards using machine learning and cloud model. Clust Comput 22(4):8051–8058

    Article  Google Scholar 

  • Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang river watershed, China. Geomorphology 145:70–80

    Article  Google Scholar 

  • Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. J Syst Sci Control Eng 8:22–34. https://doi.org/10.1080/21642583.2019.1708830

    Article  Google Scholar 

  • Yan Z, Wang X, Fu Y (2012) Study on early warning model of coal mining engineering with fuzzy AHP. Syst Eng Proc 5:113–118

    Article  Google Scholar 

  • Yan H, Zhang J, Zhou N, Shi P, Dong X (2022) Coal permeability alteration prediction during CO2 geological sequestration in coal seams: a novel hybrid artificial intelligence approach. Geomech Geophys Geo-Energy Geo-Resour 8(3):1–11

    Google Scholar 

  • Yang Y, Zhang W (2021) Assessment of landslide susceptibility based on weighted information value model in Pingshan county. Open Access Libr J 8(12):1–14

    Google Scholar 

  • Yao X, Guo HX, Zhu J, Shi Y (2022) Dynamic selection of emergency plans of geological disaster based on case-based reasoning and prospect theory. Nat Hazards 110(3):2249–2275

    Article  Google Scholar 

  • Yu C, 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(11):1848

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Project of the National Natural Science Foundation of China (42277166), Hebei University of Geology Science and Technology Innovation Team Project (KJCXTD-2021-08); Natural Science Foundation of Hebei Province (D2019403182). Authors are also grateful to the reviewers and editors, for their insightful comments and suggestions for the improvement and expansion of the work.

Funding

Partial financial support was received from National Natural Science Foundation of China, The research leading to these results received funding from Guoliang Du under Grant Agreement No 42277166. Partial financial support was received from Hebei University of Geology Science and Technology Innovation Team Project. The research leading to these results received funding from Aihong Zhou under Grant Agreement No KJCXTD-2021-08. Partial financial support was received from Natural Science Foundation of Hebei Province, The research leading to these results received funding from Chao Liu under Grant Agreement No D2019403182. The authors have no financial or proprietary interests in any material discussed in this article.

Author information

Authors and Affiliations

  1. School of Urban Geology and Engineering, Hebei Geosciences University, Shijiazhuang, 050031, Hebei, People’s Republic of China

    M. Hou, Y. Yuan, A. Zhou, C. Liu & J. Le

  2. Hebei Province Underground Artificial Environment Intelligent Development and Control Technology Innovation Center, Shijiazhuang, 050031, Hebei, People’s Republic of China

    Y. Yuan, A. Zhou & C. Liu

  3. Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources, Shijiazhuang, 050031, Hebei, People’s Republic of China

    Y. Yuan, A. Zhou & C. Liu

Authors
  1. M. Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Y. Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. Le
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MH, YY and AZ. The first draft of the manuscript was written by MH, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by CL, JL. The first draft of the manuscript was written by MH, YY and AZ all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. CL provided advice on the structure of the paper and guided the whole process of the paper modification.

Corresponding author

Correspondence to Y. Yuan.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article.All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Editorial responsibility: Shahid Hussain.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, M., Yuan, Y., Zhou, A. et al. Different slope units division-based geohazard susceptibility evaluation of support vector machine optimized by sparrow search algorithm. Int. J. Environ. Sci. Technol. 21, 3365–3380 (2024). https://doi.org/10.1007/s13762-023-05223-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-023-05223-x

Keywords

Search

Navigation