SpringerLink
Log in

Landslide susceptibility assessment for a transmission line in Gansu Province, China by using a hybrid approach of fractal theory, information value, and random forest models

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Landslide is one of the most common geological hazards, which causes a large number of property damage and loss of life in China every year. This case study was a 330-kV transmission line project located in Longnan City, Gansu Province, China, which is known as an area prone to landslides. A hybrid model of fractal theory-information value-random forests algorithm (FT-IV-RF) was proposed to evaluate the landslide susceptibility. First, sixteen landslide conditioning factors and pre-existing landslide events were selected as the initial evaluation indexes of landslide susceptibility from four datasets (geology, topography, climate and environment, and landslide inventory). Second, Pearson coefficient and sensitivity analyses were conducted to extract ten landslide conditioning factors with small correlation and large contribution to landslide occurrence from sixteen factors. Third, the weight of each class for a given factor were determined by using a combination of fractal theory and information value algorithms, which was regarded as one of input parameters and used to select the training samples in the random forest model. Four, k-means clustering was performed to classify the landslide susceptibility indices, which were predicted using the random forest model, into five levels to produce the landslide susceptibility map of the study area. Furthermore, the proposed model of FT-IV-RF model was validated by comparing with results obtained using information value (IV), back-propagation neural network (BPNN), and fuzzy logic (FL) models. Good agreements on the susceptibility estimation were observed among four models, and the hybrid model had the largest area under the curve (AUC) value of 0.996, indicating a good performance of the proposed hybrid model.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and material

The data and materials that support the findings of this study are available from the corresponding author, Yunfeng Ge, upon reasonable request.

Code availability

The codes that supports the findings of this study are available from the corresponding author, Yunfeng Ge, upon reasonable request.

References

  • Abedini M, Ghasemian B, Shirzadi A, Shahabi H, Chapi K, Pham BT, Tien Bui D (2019) A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int 34(13):1427–1457

    Article  Google Scholar 

  • Abuzied SM, Alrefaee HA (2019) Spatial prediction of landslide-susceptible zones in El-Qaá area, Egypt, using an integrated approach based on GIS statistical analysis. Bull Eng Geol Env 78(4):2169–2195

    Article  Google Scholar 

  • Abuzied SM, Pradhan B (2020) Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt. Georisk Assess Manage Risk Eng Syst Geohazards 1–26

  • Abuzied S, Ibrahim S, Kaiser M, Saleem T (2016) Geospatial susceptibility map** of earthquake-induced landslides in Nuweiba area, Gulf of Aqaba. Egypt J Mt Sci 13(7):1286–1303

    Article  Google Scholar 

  • Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility map** using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10(8):194

    Article  Google Scholar 

  • Ahmed B (2015) Landslide susceptibility map** using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12(6):1077–1095

    Article  Google Scholar 

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9(1):93–106

    Article  Google Scholar 

  • Azarafza M, Ghazifard A, Akgün H, Asghari-Kaljahi E (2018) Landslide susceptibility assessment of South Pars Special Zone, southwest Iran. Environ Earth Sci 77(24):805

    Article  Google Scholar 

  • Bai S, Wang J, Zhang Z, Cheng C (2012) Combined landslide susceptibility map** after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. CATENA 99:18–25

    Article  Google Scholar 

  • Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31

    Article  Google Scholar 

  • Benesty J, Chen J, Huang Y (2008) On the importance of the Pearson correlation coefficient in noise reduction. IEEE Trans Audio Speech Lang Process 16(4):757–765

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Bui DT, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59(3):1413–1444

    Article  Google Scholar 

  • Bui DT, Tsangaratos P, Nguyen VT, Van Liem N, Trinh PT (2020) Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188:104426

    Article  Google Scholar 

  • Cascini L (2008) Applicability of landslide susceptibility and hazard zoning at different scales. Eng Geol 102(3–4):164–177

    Article  Google Scholar 

  • Chao M, Ma X (2015) Convenient electrochemical determination of sunset yellow and tartrazine in foodsamples using a poly (L-phenylalanine)-modified glassy carbon electrode. Food Anal Methods 8(1):130–138

    Article  Google Scholar 

  • Che VB, Kervyn M, Suh CE, Fontijn K, Ernst GG, Del Marmol MA, Jacobs P (2012) Landslide susceptibility assessment in Limbe (SW Cameroon): a field calibrated seed cell and information value method. CATENA 92:83–98

    Article  Google Scholar 

  • Chen W, **e X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma JQ (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151:147–160

    Article  Google Scholar 

  • China Association of Geological Hazard Prevention (2018). Standard of classification for geological hazard (T/CAGHP 001-2018). Retrieved from http://www.caghp.org/standard.php

  • China Institute of Geo-Environment Monitoring (2020) Geological disaster bulletin of china (Report no. 2002–2019). China Geological Environment Information Site

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63(2):397–406

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility map**. Environ Geol 54(2):311–324

    Article  Google Scholar 

  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Pham BT (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346

    Article  Google Scholar 

  • Eker AM, Dikmen M, Cambazoğlu S, Düzgün ŞH, Akgün H (2015) Evaluation and comparison of landslide susceptibility map** methods: a case study for the Ulus district, Bartın, northern Turkey. Int J Geogr Inf Sci 29(1):132–158

    Article  Google Scholar 

  • Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4):327–343

    Article  Google Scholar 

  • Fu YH (2000) Transform-formed fractals and analyses and forecast of marine environment data. Mar Sci Bull Tian** 19(1):88–91

    Google Scholar 

  • Ge Y, Tang H, Ez Eldin MAM, Wang L, Wu Q, **ong C (2017) Evolution process of natural rock joint roughness during direct shear tests. Int J Geomech 17(5):E4016013

    Article  Google Scholar 

  • Ge Y, Chen H, Zhao B, Tang H, Lin Z, **e Z, Zhong P (2018) A comparison of five methods in landslide susceptibility assessment: a case study from the 330-kV transmission line in Gansu Region, China. Environ Earth Sci 77(19):662

    Article  Google Scholar 

  • Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184

    Article  Google Scholar 

  • Hadmoko DS, Lavigne F, Samodra G (2017) Application of a semiquantitative and gis-based statistical model to landslide susceptibility zonation in Kayangan catchment, Java, Indonesia. Nat Hazards 87(1):437–468

    Article  Google Scholar 

  • Huang F, Zhang J, Zhou C, Wang Y, Huang J, Zhu L (2020) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17(1):217–229

    Article  Google Scholar 

  • Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11(2):167–194

    Article  Google Scholar 

  • Jade S, Sarkar S (1993) Statistical models for slope instability classification. Eng Geol 36(1–2):91–98

    Article  Google Scholar 

  • Klai A, Haddad R, Bouzid MK, Rabia MC (2020) Landslide susceptibility map** by fuzzy gamma operator and GIS, a case study of a section of the national road n° 11 linking Mateur to Béja (Nortshern Tunisia). Arab J Geosci 13(2):1–10

    Article  Google Scholar 

  • Kumar R, Anbalagan R (2015) Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS. J Earth Syst Sci 124(2):431–448

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility map** using gis and remote sensing data. Int J Remote Sens 26(7):1477–1491

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113

    Article  Google Scholar 

  • Liang SY, Wang YX, Wang Y (2010) Risk Assessment of geological hazard in Wudu area of Longnan City, China. Appl Mech Mater 39:232–237

    Article  Google Scholar 

  • Liu JP, Zeng ZP, Liu HQ, Wang HB (2011) A rough set approach to analyze factors affecting landslide incidence. Comput Geosci 37(9):1311–1317

    Article  Google Scholar 

  • Luzi L, Pergalani F, Terlien MTJ (2000) Slope vulnerability to earthquakes at subregional scale, using probabilistic techniques and geographic information systems. Eng Geol 58(3–4):313–336

    Article  Google Scholar 

  • Mattivi P, Franci F, Lambertini A, Bitelli G (2019) TWI computation: a comparison of different open source GISs. Open Geospat DataSoftw Standards 4(1):1–12

    Google Scholar 

  • Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3–4):379–400

    Article  Google Scholar 

  • Nguyen VV, Pham BT, Vu BT, Prakash I, Jha S, Shahabi H, Tien Bui D (2019) Hybrid machine learning approaches for landslide susceptibility modeling. Forests 10(2):157

    Article  Google Scholar 

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility map**: sultan mountains, sw turkey. J Asian Earth Sci 64:180–197

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I (2017) Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study. Geotech Geol Eng 35(6):2597–2611

    Article  Google Scholar 

  • Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA 162:177–192

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility map** at Haraz watershed, Iran. Nat Hazards 63(2):965–996

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Aghda SF, Sezer EA, Jirandeh AG, Pradhan B (2014) Assessment of fractal dimension and geometrical characteristics of the landslides identified in North of Tehran, Iran. Environ Earth Sci 71(8):3617–3626

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30

    Article  Google Scholar 

  • Qi S, Zhang YL, Zhang P, Ma JZ (2014) An assessment index system for landslide risk in Bailong River Basin. J Yangtze River Sci Res Inst 31(1):23–28

    Google Scholar 

  • Ramírez J, Górriz JM, Segovia F, Chaves R, Salas-Gonzalez D, López M, Padilla P (2010) Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neurosci Lett 472(2):99–103

    Article  Google Scholar 

  • Regmi AD, Yoshida K, Pourghasemi HR, DhitaL MR, Pradhan B (2014) Landslide susceptibility map** along Bhalubang—Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. J Mt Sci 11(5):1266–1285

    Article  Google Scholar 

  • Sema HV, Guru B, Veerappan R (2017) Fuzzy gamma operator model for preparing landslide susceptibility zonation map** in parts of Kohima Town, Nagaland, India. Model Earth Syst Environ 3(2):499–514

    Article  Google Scholar 

  • Shen LL, Liu LY, Xu C, Wang JP (2016) Multi-models based landslide susceptibility evaluation - illustrated with landslides triggered by Minxian earthquake. J Eng Geol 24(1):19–28

    Google Scholar 

  • Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, Revhaug I (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76(2):60

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293

    Article  Google Scholar 

  • Tang H, Wasowski J, Juang CH (2019) Geohazards in the three Gorges Reservoir Area, China—lessons learned from decades of research. Eng Geol 261:105267

    Article  Google Scholar 

  • Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136

    Article  Google Scholar 

  • Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through a artificial neural network classifier. Nat Hazards 74(3):1489–1516

    Article  Google Scholar 

  • Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Luu C (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA 188:104451

    Article  Google Scholar 

  • Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30(3):399–419

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. Spec Rep 176:11–33

    Google Scholar 

  • Wang S, Xu Q, Luo B (2017) Vulnerability analysis and susceptibility evaluation of landslides based on fractal theory in Nanjiang County. Hydrogeol Eng Geol 44(3):119–126

    Google Scholar 

  • Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A gis-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility map** in Trabzon, NE Turkey. CATENA 85(3):287

    Article  Google Scholar 

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility map** based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility map**: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3):251–266

    Article  Google Scholar 

  • Yokoi Y, Carr JR, Watters RJ (1995) Fractal character of landslides. Environ Eng Geosci 1(1):75–81

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility map** using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856

    Article  Google Scholar 

  • Zadeh LA (2008) Is there a need for fuzzy logic? Inf Sci 178(13):2751–2779

    Article  Google Scholar 

  • Zhang GR, Yin KL, Liu C, Tang C (2003) The hazard zoning of landslide supported by GIS in Xunyang region of Shanxi province. Chin J Geol Hazard Control 14(4):39–43

    Google Scholar 

  • Zhang J, Yin K, Wang J, Liu L, Huang F (2016) Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir. Chin J Rock Mech Eng 35:284–296

    Google Scholar 

  • Zhang TY, Han L, Zhang H, Zhao YH, Li XA, Zhao L (2019) GIS-based landslide susceptibility map** using hybrid integration approaches of fractal dimension with index of entropy and support vector machine. J Mt Sci 16(6):1275–1288

    Article  Google Scholar 

  • Zhang YX, Lan HX, Li LP, Wu YM, Chen JH, Tian NM (2020) Optimizing the frequency ratio method for landslide susceptibility assessment: a case study of the Caiyuan Basin in the southeast mountainous area of China. J Mt Sci 17(2):340–357

    Article  Google Scholar 

Download references

Acknowledgements

Authors are grateful to Mrs Lea Hickerson, who is a technical editor in the Office of Graduate Studies in Missouri University of Science and Technology, for language editing. Authors also would like to thank Mr. Geng Liu for helpful supporting during the paper revision process. The authors’ special appreciation goes to the editor and five anonymous reviewers of this manuscript for their useful comments.

Funding

This work was supported by the National Key R&D Program of China (No. 2018YFC1507200 & 2017YFC1501303), the National Natural Science Foundation of China (No. 42077264), and the Scientific Research Project of China Three Gorges Group Co., LTD (No. 2019056454).

Author information

Authors and Affiliations

  1. Faculty of Engineering, China University of Geosciences, Wuhan, Hubei, China

    Binbin Zhao, Yunfeng Ge & Hongzhi Chen

  2. Research Institute of Transmission and Transformation Projects, China Electric Power Research Institute Co., Ltd, State Grid Corporation of China, Bei**g, China

    Binbin Zhao

Authors
  1. Binbin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunfeng Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongzhi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Yunfeng Ge and Binbin Zhao; Methodology: Binbin Zhao, Yunfeng Ge and Hongzhi Chen; Formal analysis and investigation: Binbin Zhao, Yunfeng Ge, and Hongzhi Chen; Writing—original draft preparation: Binbin Zhao, Yunfeng Ge, and Hongzhi Chen; Writing—review and editing: Yunfeng Ge and Binbin Zhao; Funding acquisition: Yunfeng Ge; Resources: Binbin Zhao and Yunfeng Ge; Supervision: Yunfeng Ge.

Corresponding author

Correspondence to Yunfeng Ge.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, B., Ge, Y. & Chen, H. Landslide susceptibility assessment for a transmission line in Gansu Province, China by using a hybrid approach of fractal theory, information value, and random forest models. Environ Earth Sci 80, 441 (2021). https://doi.org/10.1007/s12665-021-09737-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-021-09737-w

Keywords

Search

Navigation