An assessment of data mining and bivariate statistical methods for landslide susceptibility mapping

Document Type : Article


1 Department of Soil Science, Tabriz Branch, Islamic Azad University, Tabriz, Iran

2 Department of Environmental Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

3 Department of Water Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran


Landslides are recognized as one of the environmental challenges that lead to land degradation, reduce fertility, and cause significant damage to the ecosystem. Therefore, proper identification of landslide-prone areas through modeling will be significantly helpful for land development managers and planners by providing them with appropriate management strategies to prevent land degradation. In this research, landslide susceptibility mapping was carried out for West Azerbaijan province in Iran using Frequency Ratio (FR), Shannon Entropy (SE), Random Forest (RF), and an ensemble of random forest and bagging (RF-BA) methods. Based on field surveys, local interviews, and review of similar studies, 12 factors influencing landslide occurrence, namely altitude, slope angle, slope aspect, distance from fault, distance from river, distance from road, drainage density, road density, rainfall, soil, land use, and lithology, were identified. In the field surveys, 110 landslides in the area were specified; 70% of the data (77 landslides) were randomly selected and utilized for modeling and the remaining 30% (33 landslides) for validation. The results of the ROC curve showed the accuracy of 0.92, 0.91, 0.89, and 0.88 with the RF-BA, RF, FR, and SE models, respectively.


  • References:

    • Keesstra, SD., Quinton, JN., van der Putten, WH., Bardgett, RD. and Fresco, LO. “The significance of soils and soil science towards realization of the United Nations”, (2016).
    • Bordoni, M., Meisina, C., Valentino, R., Bittelli, M. and Chersich, S. “Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS”, Nat Hazards Earth Syst. Sci., 15(5), pp. 1025–1050 (2015).
    • Tsangaratos, P. and Benardos, A. “Estimating landslide susceptibility through a artificial neural network classifier”, Natural hazards, 74(3), pp.1489-1516 (2014).
    • Koehorst, B.A.N., Kjekstad, O., Patel, D., Lubkowski, Z., Knoeff, J.G. and Akkerman, G.J “Workpackage 6 Determination of Socio- Economic Impact of Natural Disasters”, Assessing socio economic Impact in Europe, pp.173 (2005).
    • Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M, Agwe J, Buys P, Kjekstad O, Lyon B. and Yetman, G. “Natural disaster hotspots: a global risk analysis. The World Bank Hazard Management Unit”, Washington, (2005).
    • Lee, S. and Choi, J. “Landslide susceptibility mapping using GIS and the weight-of-evidence model”, International Journal of Geographical Information Science, 18(8), pp. 789-814 (2004).
    • Li, Z., He, Y., Li, H., & Wang, Y. “Antecedent rainfall induced shallow landslide-A case study of Yunnan landslide, China”, Scientia Iranica, 26(1), 202-212, (2019).
    • Lee, S. and Sambath, T. 2006. “Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models”, Environmental Geology, 50(6), pp. 847-855 (2006).
    • Pak, A., Sarfaraz, M. “Lattice Boltzmann Method for Simulating Impulsive Water Waves Generated by Landslides”, Scientia Iranica, 21(2), 318-328, (2014).
    • Pandey, V.K., Pourghasemi, H.R. and Sharma, M.C. “Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya”, Geocarto International, 35(2), pp. 168-187 (2020).
    • Yilmaz, I. 2009. “Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey)”, Computers & Geosciences, 35(6), pp. 1125-1138 (2009).
    • Gadtaula, A. and Dhakal, S. “Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal”, Journal of Nepal Geological Society, 58, pp. 163-171 (2019).
    • Chen, Z., Liang, S., Ke, Y., Yang, Z. and Zhao, H. “Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China”, Geocarto International, 34(4), pp. 348-367 (2019).
    • Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., ... and Althuwaynee, O.F. “Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya”, Natural hazards, 65(1), pp. 135-165 (2013).
    • Nohani, E., Moharrami, M., Sharafi, S., Khosravi, K., Pradhan, B., Pham, B.T., Lee, S. and Melesse, A.M. “Landslide susceptibility mapping using different GIS-based bivariate models”, Water, 11(7): pp. 1402 (2019).
    • Pascale, S., Parisi, S., Mancini, A., Schiattarella, M., Conforti, M., Sole, A., … and Sdao, F. “Landslide susceptibility mapping using artificial neural network in the urban area of Senise and San Costantino Albanese (Basilicata, Southern Italy)”, In International Conference on Computational Science and Its Applications, Springer, Berlin, Heidelberg, pp. 473-488 (2013).
    • Aghdam, I.N., Varzandeh, M.H.M. and Pradhan, B. “Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran)”. Environmental Earth Sciences, 75(7), 553 (2016).
    • Pourghasemi, H.R., Jirandeh, A.G., Pradhan, B., Xu, C. and Gokceoglu, C. “Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran”, Journal of Earth System Science, 122(2), pp. 349-369 (2013).
    • Hong, H., Kornejady, A., Soltani, A., Termeh, S.V.R., Liu, J., Zhu, A.X. and Wang, Y. “Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)”, Earth Science Informatics, 11(4), pp. 605-622 (2018).
    • Kim, J.C., Lee, S., Jung, H.S. and Lee, S. “Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea”, Geocarto international, 33(9), pp. 1000-1015 (2018).
    • Oliveira, A., Fernandes, J., Bateira, C., Faria, A., and Gonçalves, J. “Influence of digital elevation MODELS ON landslide susceptibility with logistic regression model”, Revista do Departamento de Geografia, 36, pp. 33-47 (2018).
    • Park, S., Choi, C., Kim, B. and Kim, J. “Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea”, Environmental earth sciences, 68(5), pp. 1443-1464 (2013).
    • Dano, U.L., Balogun, A.L., Matori, A.N., Wan Yusouf, K., Abubakar, I. R., Mohamed, S., … and Pradhan, B. “Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia”, Water, 11(3), 615 (2019).
    • Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A. and Pirasteh, S. and … “Spatial prediction of landslide susceptibility using GIS-based data mining techniques of anfis with whale optimization algorithm (WOA) and grey wolf optimizer (GWO)”, Applied Sciences, 9(18), 3755 (2019).
    • Huang, F., Zhang, J., Zhou, C., Wang, Y., Huang, J. and Zhu, L. “A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction”, Landslides, 17(1), pp. 217-229 (2020).
    • Nguyen, V.V., Pham, B.T., Vu, B.T., Prakash, I., Jha, S., Shahabi, H., … and Tien Bui, D. “Hybrid machine learning approaches for landslide susceptibility modeling”, Forests, 10(2), pp. 157 (2019).
    • Pham, B. T., Prakash, I., Dou, J., Singh, S.K., Trinh, P.T., Tran, H.T., … and Bui, D.T. “A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers”, Geocarto International, pp. 1-25 (2019).
    • Bui, D.T., Hoang, N.D., Nguyen, H. and Tran, X.L. “Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: A case study in Lang Son Province, Vietnam”, Advanced Engineering Informatics, 42, 100978 (2019).
    • Tien Bui, D., Shahabi, H., Omidvar, E., Shirzadi, A., Geertsema, M. and Clague, J.J., ... and Barati, Z. “Shallow landslide prediction using a novel hybrid functional machine learning algorithm”, Remote Sensing, 11(8): pp. 931 (2019).
    • Chen, W., Shahabi, H., Zhang, S.,  Khosravi, K.,  Shirzadi, A.,  Chapi, K.,  Pham, B.T. and … “Landslide susceptibility modeling based on GIS and novel bagging-based kernel logistic regression”, Applied Sciences, 8(12): 2540 (2018).
    • Shirzadi, A., Soliamani, K., Habibnejhad, M., Kavian, A., Chapi, K., Shahabi, H. and … “Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping”,  Sensors, 18(11): pp. 3777 (2018).
    • Pham, B.T., Khosravi, K., Prakash, I. “Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area, Uttarakhand, India”, Environmental Processes, 4 (3): pp. 711-730 (2017).
    • Stocking, M. and Murnaghan, N. “Handbook for the field assessment of land degradation”, Earthscan, (2001).
    • Hong, H., Liu, J., & Zhu, A. X. “Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble”, Science of the total environment, 718, 137231, (2020).
    • Wu, Y., Ke, Y., Chen, Z., Liang, S., Zhao, H., & Hong, H. “Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping”, Catena, 187, 104396, (2020).
    • He, S., Pan, P., Dai, L., Wang, H. and Liu, J. “Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China”, Geomorphology, 171, pp. 30-41 (2012).
    • Xu, C., Dai, F., Xu, X. and Lee, Y.H. “GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China”, Geomorphology, 145, pp. 70-80 (2012).]
    • Li, X., Yang, H., Zhang, J., Qian, G., Yu, H., Cai, J. Time-Domain Analysis of Tamper Displacement during Dynamic Compaction Based on Automatic Control. Coatings, 11(9). doi: 10.3390/coatings11091092, (2021)
    • Yang, W., Chen, X., Xiong, Z., Xu, Z., Liu, G., Zhang, X. A privacy-preserving aggregation scheme based on negative survey for vehicle fuel consumption data. Information sciences, 570, 526-544. doi: 10.1016/j.ins.2021.05.009, (2021).
    • Li, B., Feng, Y., Xiong, Z., Yang, W., & Liu, G. Research on AI security enhanced encryption algorithm of autonomous IoT systems. Information sciences, 575, 379-398. doi: 10.1016/j.ins.2021.06.016, (2021).
    • Li, B., Yang, J., Yang, Y., Li, C., & Zhang, Y. Sign Language/Gesture Recognition Based on Cumulative Distribution Density Features Using UWB Radar. IEEE transactions on instrumentation and measurement, 1. doi: 10.1109/TIM.2021.3092072, (2021).
    • Weng, L., He, Y., Peng, J., Zheng, J., & Li, X. Deep cascading network architecture for robust automatic modulation classification. Neurocomputing (Amsterdam), 455, 308-324. doi: 10.1016/j.neucom.2021.05.010, (2021).
    • Chao, L., Zhang, K., Wang, J., Feng, J., & Zhang, M. A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm. Remote sensing (Basel, Switzerland), 13(12), 2414. doi: 10.3390/rs13122414, (2021).
    • Zhang, K., Wang, S., Bao, H., & Zhao, X.. Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China. Natural hazards and earth system sciences, 19(1), 93-105. doi: 10.5194/nhess-19-93-2019, (2019).
    • Zuo, Y., Jiang, S., Wu, S., Xu, W., Zhang, J., Feng, R., Santosh, M. Terrestrial heat flow and lithospheric thermal structure in the Chagan Depression of the Yingen‐Ejinaqi Basin, north central China. Basin research, 32(6), 1328-1346. doi: 10.1111/bre.12430, (2020).
    • Xu, J., Wu, Z., Chen, H., Shao, L., Zhou, X., Wang, S. Study on Strength Behavior of Basalt Fiber-Reinforced Loess by Digital Image Technology (DIT) and Scanning Electron Microscope (SEM). Arabian journal for science and engineering. doi: 10.1007/s13369-021-05787-1, (2021).
    • Zhang, K., Chao, L., Wang, Q., Huang, Y., Liu, R., Hong, Y., Ye, J. Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China. Water Science and Engineering, 12(2), 85-97. doi: 10.1016/j.wse.2019.06.001, (2019).
    • Kordestani, H., Zhang, C., Masri, S. F., & Shadabfar, M. An empirical time‐domain trend line‐based bridge signal decomposing algorithm using Savitzky–Golay filter. Structural control and health monitoring, 28(7), n/a-n/a. doi: 10.1002/stc.2750, (2021).
    • Zhou, J. Liu, J. Lei, L. Yu and J. -N. Hwang, "GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation," in IEEE Transactions on Image Processing, doi: 10.1109/TIP.2021.3109518, (2021).
    • Hong, H., Chen, W., Xu, C., Youssef, A. M., Pradhan, B., & Tien Bui, D. “Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy”, Geocarto international, 32(2), 139-154, (2017).