Abstract
Landslides are a geological phenomenon that is causing considerable economic and human losses annually in various regions of the world. In some cases, the complex behaviors of some such phenomena cause that single machine learning models fail in modeling them well. To overcome this issue, this paper presents two novel genetic-algorithm (GA)-based ensemble models constructed with the decision tree (DT), k-nearest neighbors (KNN), and Naive Bayes (NB) models based on the bagging and random sub-space (RS) methods for landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan. Sixteen factors, including topographic, climatic, human, and geological factors were used as possible factors that influence landslide occurrence in the study area. In addition to this, one hundred and ninety two landslide locations were employed for training and testing the models. The GA was used in this study for feature selection based on three models: DT, KNN, and NB. Model performance evaluation based on the area under the receiver operating characteristic (AUROC) curve indicated that the ensemble models outperform the standalone ones. The values of the AUROC curves in the validation phases for the five models, namely, the GA-based DT, KNN, NB, bagging-based, and RS-based ensemble model, were 0.63, 0.69, 0.63, 0.89, and 0.95, respectively. The results of this study suggest that simple models can be combined using the bagging and RS methods to produce integrated models that have higher accuracy than that of any of the individual simple models.
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References
Abramson LW, Lee TS, Sharma S, Boyce GM (2001) Slope stability and stabilization methods. Wiley, New York
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
Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11(6):1063–1078
Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31
Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11(1):1–16
Bai S-B, Cheng C, Wang J, Thiebes B, Zhang Z-G (2013) Regional scale rainfall-and earthquake-triggered landslide susceptibility assessment in Wudu County, China. J Mt Sci 10(5):743–753
Berrar D (2018) Bayes’ theorem and naive Bayes classifier. In: Encyclopedia of bioinformatics and computational biology: ABC of bioinformatics. Elsevier, The Netherlands, pp 403–412
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
Budimir M, Atkinson P, Lewis H (2014) Earthquake-and-landslide events are associated with more fatalities than earthquakes alone. Nat Hazards 72(2):895–914
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211
Chacón J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Env 65(4):341–411
Chen W, Hong H, Li S, Shahabi H, Wang Y, Wang X, Ahmad BB (2019) Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. J Hydrol 575:864–873
Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017) Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327
Chen W, Shahabi H, Shirzadi A, Li T, Guo C, Hong H, Ma M (2018) A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int 33(12):1398–1420
Chigira M, Yagi H (2006) Geological and geomorphological characteristics of landslides triggered by the 2004 Mid Niigta prefecture earthquake in Japan. Eng Geol 82(4):202–221
Chowdhuri I, Pal SC, Arabameri A, Ngo PTT, Chakrabortty R, Malik S, Roy P (2020) Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environ Earth Sci 79(20):1–28
Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41(6):720–730
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4):327–343
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844
Holcombe E, Anderson M (2010) Tackling landslide risk: Helping land use policy to reflect unplanned housing realities in the Eastern Caribbean. Land Use Policy 27(3):798–800
Holec J, Bednarik M, Šabo M, Minár J, Yilmaz I, Marschalko M (2013) A small-scale landslide susceptibility assessment for the territory of Western Carpathians. Nat Hazards 69(1):1081–1107
Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529
Goward SN, Markham B, Dye DG, Dulaney W, Yang J (1991) Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sens Environ 35(2–3):257–277
Jiang L, Cai Z, Wang D, Jiang S (2007) Survey of improving k-nearest-neighbor for classification. In: Paper presented at the Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007)
Kornejady A, Pourghasemi HR, Afzali SF (2019) Presentation of RFFR new ensemble model for landslide susceptibility assessment in Iran. In: Landslides: theory, practice and modelling. Springer, pp 123–143
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491
Lee S, Oh H-J (2012) Ensemble-based landslide susceptibility maps in Jinbu area, Korea In: Terrigenous mass movements. Springer, pp 193–220
Mi C, Chen J, Zhang Z, Huang S, Postolache O (2021) Visual sensor network task scheduling algorithm at automated container terminal. IEEE Sens J 2:110078
Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemom J Chemom Soc 18(6):275–285
Ngo PTT, Panahi M, Khosravi K, Ghorbanzadeh O, Kariminejad N, Cerda A, Lee S (2021) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front 12(2):505–519
Nefeslioglu H, Sezer E, Gokceoglu C, Bozkir A, Duman T (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. In: Mathematical problems in engineering
Nohani E, Moharrami M, Sharafi S, Khosravi K, Pradhan B, Pham BT, Melesse MA (2019) Landslide susceptibility mapping using different GIS-based bivariate models. Water 11(7):1402
Panov P, Džeroski S (2007) Combining bagging and random subspaces to create better ensembles. In: Paper presented at the international symposium on intelligent data analysis
Peterson LE (2009) K-Nearest Neighbor. Scholarpedia 4(2):1883
Pham BT, Phong TV, Nguyen-Thoi T, Parial KK, Singh S, Ly H-B, Prakash I (2020) Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto Int 2:1–23
Pham BT, Prakash I (2019) Evaluation and comparison of LogitBoost ensemble, fisher’s linear discriminant analysis, logistic regression and support vector machines methods for landslide susceptibility mapping. Geocarto Int 34(3):316–333
Popescu ME (2002) Landslide causal factors and landslide remediatial options. In: Paper presented at the 3rd international conference on landslides, slope stability and safety of infra-structures
Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996
Raileanu LE, Stoffel K (2004) Theoretical comparison between the gini index and information gain criteria. Ann Math Artif Intell 41(1):77–93
Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742
Rish I (2001) An empirical study of the naive Bayes classifier. In: Paper presented at the IJCAI 2001 workshop on empirical methods in artificial intelligence
Rosi A, Tofani V, Tanteri L, Stefanelli CT, Agostini A, Catani F, Casagli N (2018) The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15(1):5–19
Roth RA (1983) Factors affecting landslide-susceptibility in San Mateo county, California. Bull Assoc Eng Geol 20(4):353–372
Sezer EA, Nefeslioglu HA, Osna T (2017) An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software. Comput Geosci 98:26–37
Shadman Roodposhti M, Aryal J, Shahabi H, Safarrad T (2016) Fuzzy shannon entropy: A hybrid gis-based landslide susceptibility mapping method. Entropy 18(10):343
Tien BD, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Math Problems Eng. 2:1589
Tien Bui D, Shirzadi A, Shahabi H, Geertsema M, Omidvar E, Clague JJ, Bin Ahmad B (2019) New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed. Forests 10(9):743
Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. CATENA 145:164–179
Wang G, Lei X, Chen W, Shahabi H, Shirzadi A (2020a) Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry 12(3):325
Wang Y, Fang Z, Hong H (2019) Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ 666:975–993
Wang Y, Feng L, Li S, Ren F, Du Q (2020) A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China. Catena 188:104425
Wu W, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31(8):2097–2110
Xu Q, Fan X-M, Huang R-Q, Van Westen C (2009) Landslide dams triggered by the Wenchuan Earthquake, Sichuan Province, south west China. Bull Eng Geol Env 68(3):373–386
Youssef AM, Al-Kathery M, Pradhan B (2015a) Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19(1):113–134
Youssef AM, Pradhan B, Jebur MN, El-Harbi HM (2015b) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73(7):3745–3761
Zêzere J, Pereira S, Melo R, Oliveira S, Garcia RA (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267
Zhang H, Li D (2007) Naïve Bayes text classifier. In: Paper presented at the 2007 IEEE International Conference on Granular Computing (GRC 2007)
Zhong L, Fang Z, Liu F, Yuan B, Zhang G, Lu J (2021) Bridging the theoretical bound and deep algorithms for open set domain adaptation. In: IEEE transactions on neural networks and learning systems
Zhang L, Gao T, Cai G, Hai KL (2022) Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm. J Energy Storage 49:104092
Zheng W, Liu X, Yin L (2021) Research on image classification method based on improved multi-scale relational network. PeerJ Comput Sci 7:e613
Zhao H, Yao L, Mei G, Liu T, Ning Y (2017) A fuzzy comprehensive evaluation method based on AHP and entropy for a landslide susceptibility map. Entropy 19(8):396
Zhou W, Yu L, Zhou Y, Qiu W, Wu M-W, Luo T (2018) Local and global feature learning for blind quality evaluation of screen content and natural scene images. IEEE Trans Image Process 27(5):2086–2095
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Edited by Dr. Mehdi Abdolmaleki (ASSOCIATE EDITOR) / Dr. Maya Ilieva (ASSOCIATE EDITOR) / Prof. Savka Dineva (CO-EDITOR-IN-CHIEF).
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Mabdeh, A.N., Al-Fugara, A., Ahmadlou, M. et al. GIS-based landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan using genetic algorithm-based ensemble models. Acta Geophys. 70, 1253–1267 (2022). https://doi.org/10.1007/s11600-022-00767-x
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DOI: https://doi.org/10.1007/s11600-022-00767-x