Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm

References

• Abu El-Magd SA, Ali SA, Pham QB. 2021. Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Sci Inform. 14(3):1227–1243.
   Web of Science ®Google Scholar
• Aditian A, Kubota T, Shinohara Y. 2018. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology. 318:101–111.
   Web of Science ®Google Scholar
• Al-Najjar HAH, Pradhan B. 2021. Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geosci Front. 12(2):625–637.
   Web of Science ®Google Scholar
• Ali SA, Parvin F, Vojteková J, Costache R, Linh NTT, Pham QB, Vojtek M, Gigović L, Ahmad A, Ghorbani MA. 2021. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms. Geosci Front. 12(2):857–876.
   Web of Science ®Google Scholar
• Althuwaynee OF, Pradhan B, Lee S. 2016. A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int J Remote Sens. 37(5):1190–1209.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Baumol WJ, Quandt RE. 1964. Rules of thumb and optimally imperfect decisions. Am Econ Rev. 54(2):23–46.
   Web of Science ®Google Scholar
• Berhane G, Kebede M, Alfarah N, Hagos E, Grum B, Giday A, Abera T. 2020. Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia. J Afr Earth Sci. 164:103795.
   Web of Science ®Google Scholar
• Boyen X, Wehenkel L. 1999. Automatic induction of fuzzy decision trees and its application to power system security assessment. Fuzzy Sets Syst. 102(1):3–19.
   Web of Science ®Google Scholar
• Breiman L. 1996. Bagging predictors. Mach Learn. 24(2):123–140.
   Web of Science ®Google Scholar
• Brenning A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci. 5(6):853–862.
   Web of Science ®Google Scholar
• Bui DT, Tsangaratos P, Ngo P-TT, Pham TD, Pham BT. 2019. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ. 668:1038–1054.
   PubMed Web of Science ®Google Scholar
• Cantarino I, Carrion MA, Goerlich F, Martinez Ibañez V. 2019. A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 16(2):265–282.
   Web of Science ®Google Scholar
• Chen H, Lin Z, Tan C. 2019. Random subspace-based ensemble modeling for near-infrared spectral diagnosis of colorectal cancer. Anal Biochem. 567:38–44.
   PubMed Web of Science ®Google Scholar
• Chen W, Hong H, Panahi M, Shahabi H, Wang Y, Shirzadi A, Pirasteh S, Alesheikh AA, Khosravi K, Panahi S, et al. 2019. Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with whale optimization algorithm (WOA) and grey wolf optimizer (GWO). Appl Sci. 9(18):3755.
   Google Scholar
• Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J. 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.
   Web of Science ®Google Scholar
• Chuang RY, Wu BS, Liu H-C, Huang H-H, Lu C-H. 2021. Development of a statistics-based nowcasting model for earthquake-triggered landslides in Taiwan. Eng Geol. 289:106177.
   Web of Science ®Google Scholar
• Costache R, Bui DT. 2019. Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania. Sci Tot Environ. 691:1098–1118.
   Google Scholar
• Costanzo D, Rotigliano E, Irigaray C, Jiménez-Perálvarez JD, Chacón J. 2012. Factors selection in landslide susceptibility modelling on large scale following the GIS matrix method: application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci. 12(2):327–340.
   Web of Science ®Google Scholar
• Dang V-H, Hoang N-D, Nguyen L-M-D, Bui DT, Samui P. 2020. A novel GIS-based random forest machine algorithm for the spatial prediction of shallow landslide susceptibility. Forests. 11(1):118.
   Web of Science ®Google Scholar
• Das S, Sarkar S, Kanungo DP. 2022. GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya. Environ Monit Assess. 194(4):1–28.
   Google Scholar
• DeLong ER, DeLong DM, Clarke-Pearson DL. 1988. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 44(3):837–845.
   PubMed Web of Science ®Google Scholar
• Díaz Morales R, Navia Vázquez Á. 2016. Improving the efficiency of IRWLS SVMs using parallel Cholesky factorization. Pattern Recog Lett. 84:91–98.
   Web of Science ®Google Scholar
• Dung NV, Hieu N, Phong TV, Amiri M, Costache R, Al-Ansari N, Prakash I, Le HV, Nguyen HBT, Pham BT. 2021. Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin. Geomatics Nat Hazards Risk. 12(1):1688–1714.
   Web of Science ®Google Scholar
• Elkano M, Galar M, Sanz J, Bustince H. 2016. Fuzzy rule-based classification systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the fuzzy reasoning Method. Inf Sci. 332:94–114.
   Web of Science ®Google Scholar
• Gameiro S, Riffel ES, de Oliveira GG, Guasselli LA. 2021. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation. Appl Geogr. 137:102598.
   Web of Science ®Google Scholar
• Giannoglou VG, Stavrakoudis DG, Theocharis JB, Petridis V. 2015. Genetic fuzzy rule based classification systems for coronary plaque characterization based on intravascular ultrasound images. Eng Appl Artif Intell. 38:203–220.
   Web of Science ®Google Scholar
• Gospodinov N, Kan R, Robotti C. 2013. Chi-squared tests for evaluation and comparison of asset pricing models. J Econ. 173(1):108–125.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Hong H, Naghibi SA, Pourghasemi H, Pradhan B. 2016. GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci. 9:1–26.
   Web of Science ®Google Scholar
• Hu X, Huang C, Mei H, Zhang H. 2021. Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace–based naïve Bayes tree in Zigui County of the Three Gorges Reservoir Area, China. Bull Eng Geol Environ. 80(7):5315–5329.
   Web of Science ®Google Scholar
• Huang J, Ma N, Ling S, Wu X. 2022. Comparing the prediction performance of logistic model tree with different ensemble techniques in susceptibility assessments of different landslide types. Geocarto Int.:1–31.
   Web of Science ®Google Scholar
• Jaafari A, Najafi A, Pourghasemi H, Rezaeian J, Sattarian A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Env Sci Tech. 11(4):909–926.
   Web of Science ®Google Scholar
• Jabar A, Masrour R. 2020. Magnetic properties on a decorated triangular lattice: A Monte Carlo simulation. Physica A. 538:122959.
   Web of Science ®Google Scholar
• Kamran KV, Feizizadeh B, Khorrami B, Ebadi Y. 2021. A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping. Appl Geomat. 13(4):837–851.
   Web of Science ®Google Scholar
• Karabulut M. 2013. Fuzzy unordered rule induction algorithm in text categorization on top of geometric particle swarm optimization term selection. Knowl Based Syst. 54:288–297.
   Web of Science ®Google Scholar
• Kluska J, Madera M. 2021. Extremely simple classifier based on fuzzy logic and gene expression programming. Inf Sci. 571:560–579.
   Web of Science ®Google Scholar
• Kopecký M, Čížková Š. 2010. Using topographic wetness index in vegetation ecology: does the algorithm matter? Appl Veg Sci. 13(4):450–459. http://www.jstor.org/stable/40927821.
   Web of Science ®Google Scholar
• Kopecký M, Macek M, Wild J. 2021. Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition. Sci Total Environ. 757:143785.
   PubMed Web of Science ®Google Scholar
• Kouziokas GN. 2020. SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting. Appl Soft Comput. 93:106410.
   Web of Science ®Google Scholar
• Lee S, Pradhan B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4(1):33–41.
   Google Scholar
• Li R, Wang N. 2019. Landslide susceptibility mapping for the Muchuan County (China): a comparison between bivariate statistical models (WoE, EBF, and IoE) and their ensembles with logistic regression. Symmetry. 11(6):762.
   Google Scholar
• Liu J, Duan Z. 2018. Quantitative assessment of landslide susceptibility comparing statistical index, index of entropy, and weights of evidence in the Shangnan Area, China. Entropy. 20(11):868. https://www.mdpi.com/1099-4300/20/11/868.
   PubMed Web of Science ®Google Scholar
• Liu JG, Mason PJ, Clerici N, Chen S, Davis A, Miao F, Deng H, Liang L. 2004. Landslide hazard assessment in the Three Gorges area of the Yangtze river using ASTER imagery: Zigui–Badong. Geomorphology. 61(1-2):171–187.
   Web of Science ®Google Scholar
• Longoni L, Papini M, Brambilla D, Arosio D, Zanzi L. 2016. The role of the spatial scale and data accuracy on deep-seated gravitational slope deformation modeling: The Ronco landslide, Italy. Geomorphology. 253:74–82.
   Web of Science ®Google Scholar
• Lucchese LV, de Oliveira GG, Pedrollo OC. 2021. Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping. Nat Hazards. 106(3):2381–2405.
   Web of Science ®Google Scholar
• Lucchese LV, de Oliveira GG, Pedrollo OC. 2022. A hybrid random forests and artificial neural networks bagging ensemble for landslide susceptibility modelling. Geocarto Int. 1–20.
   Web of Science ®Google Scholar
• Lv L, Chen T, Dou J, Plaza A. 2022. A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping. Int J Appl Earth Obs Geoinf. 108:102713.
   Web of Science ®Google Scholar
• Ma J, Xia D, Guo H, Wang Y, Niu X, Liu Z, Jiang S. 2022. Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study. Landslides. 19(10):2489–2511.
   Web of Science ®Google Scholar
• Małka A. 2021. Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models. Nat Hazards. 107(1):639–674.
   Web of Science ®Google Scholar
• Manchar N, Benabbas C, Hadji R, Bouaicha F, Grecu F. 2018. Landslide susceptibility assessment in constantine region (NE Algeria) by means of statistical models. Studia Geotech Mech. 40(3):208–219.
   Web of Science ®Google Scholar
• Mehrabi M, Moayedi H. 2021. Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms. Environ Earth Sci. 80(24):1–20.
   Web of Science ®Google Scholar
• Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B. 2020. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth Sci Rev. 207:103225.
   Web of Science ®Google Scholar
• Moayedi H, Osouli A, Tien Bui D, Foong LK. 2019. Spatial landslide susceptibility assessment based on novel neural-metaheuristic geographic information system based ensembles. Sensors (Basel, Switzerland), 19(21):4698.
   PubMed Web of Science ®Google Scholar
• Motevalli A, Pourghasemi HR, Zabihi M. 2018. 2.12 - Assessment of GIS-based machine learning algorithms for spatial modeling of landslide susceptibility: case study in Iran. In B. Huang, editor. Comprehensive geographic information systems. Elsevier; p. 258–280.
   Google Scholar
• Mousavi SM, Abdullah S, Niaki STA, Banihashemi S. 2021. An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: Medical diagnosis applications. Knowl Based Syst. 220:106943.
   Web of Science ®Google Scholar
• N, Ramos-Bernal R, Vázquez-Jiménez R, Sánchez Tizapa S, Arroyo Matus R. 2020. Characterization of susceptible landslide zones by an accumulated index. In Landslides - investigation and monitoring. London (UK): IntechOpen.
   Google Scholar
• Ngo T-TT, Pham HT, Acosta J, Derrible S. 2022. Predicting bike-sharing demand using random forest. 2(2):13–21.
   Google Scholar
• Ngo TQ, Dam ND, Al-Ansari N, Amiri M, Phong TV, Prakash I, Le HV, Nguyen HBT, Pham BT. 2021. Landslide susceptibility mapping using single machine learning models: a case study from Pithoragarh District, India. Adv Civ Eng. 2021:1–19.
   Web of Science ®Google Scholar
• Nguyen B-Q-V, Kim Y-T. 2021. Landslide spatial probability prediction: a comparative assessment of naïve Bayes, ensemble learning, and deep learning approaches. Bull Eng Geol Environ. 80(6):4291–4321.
   Web of Science ®Google Scholar
• Nguyen B-Q-V, Lee S-R, Kim Y-T. 2020. Spatial probability assessment of landslide considering increases in pore-water pressure during rainfall and earthquakes: Case studies at Atsuma and Mt. Umyeon. CATENA. 187:104317.
   Web of Science ®Google Scholar
• Nohani E, Moharrami M, Sharafi S, Khosravi K, Pradhan B, Pham BT, Lee S, Melesse AF. 2019. Landslide susceptibility mapping using different GIS-based bivariate models. Water. 11(7):1402.
   Web of Science ®Google Scholar
• Palacios A, Sánchez L, Couso I, Destercke S. 2016. An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia. Neurocomputing. 176:60–71.
   Web of Science ®Google Scholar
• Panahi M, Gayen A, Pourghasemi HR, Rezaie F, Lee S. 2020. Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms. Sci Total Environ. 741:139937.
   PubMed Web of Science ®Google Scholar
• Paryani S, Neshat A, Pradhan B. 2021a. Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms. Egypt J Remote Sens Space Sci. 24(3):845–855.
   Web of Science ®Google Scholar
• Paryani S, Neshat A, Pradhan B. 2021b. Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches. Theor Appl Climatol. 146(1-2):489–509.
   Web of Science ®Google Scholar
• Patel D, Sampath S. 2020. Random sampling in reproducing kernel subspaces of Lp(Rn). J Math Anal Appl. 491(1):124270.
   Web of Science ®Google Scholar
• Pavlov G, Shi D, Maydeu-Olivares A. 2020. Chi-square difference tests for comparing nested models: An evaluation with non-normal data. Struct Equ Model. 27(6):908–917.
   Web of Science ®Google Scholar
• Peethambaran B, Anbalagan R, Shihabudheen K. 2019. Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system-A comparative study. Nat Hazards. 96(1):121–147.
   Web of Science ®Google Scholar
• Perotto-Baldiviezo HL, Thurow TL, Smith CT, Fisher RF, Wu XB. 2004. GIS-based spatial analysis and modeling for landslide hazard assessment in steeplands, southern Honduras. Agric Ecosyst Environ. 103(1):165–176.
   Web of Science ®Google Scholar
• Pham BT, Prakash I. 2019. A novel hybrid model of bagging-based naïve bayes trees for landslide susceptibility assessment. Bull Eng Geol Environ. 78(3):1911–1925.
   Web of Science ®Google Scholar
• Pham BT, Van Dao D, Acharya TD, Van Phong T, Costache R, Van Le H, Nguyen HBT, Prakash I. 2021. Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis. Environ Earth Sci. 80(20):1–13.
   Web of Science ®Google Scholar
• Pham BT, Phong TV, Nguyen-Thoi T, Trinh PT, Tran QC, Ho LS, Singh SK, Duyen TTT, Nguyen LT, Le HQ, et al. 2020. GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv Space Res. 66(6):1303–1320.
   Web of Science ®Google Scholar
• Pham QB, Achour Y, Ali SA, Parvin F, Vojtek M, Vojteková J, Al-Ansari N, Achu AL, Costache R, Khedher KM, et al. 2021. A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomatics Nat Hazards Risk. 12(1):1741–1777.
   Web of Science ®Google Scholar
• Phong TV, Ly H-B, Trinh PT, Prakash I. 2020. Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach. Vietnam J Earth Sci. 42(3):237–246.
   Google Scholar
• Phong TV, Phan TT, Prakash I, Singh SK, Shirzadi A, Chapi K, Ly H-B, Ho LS, Quoc NK, Pham BT. 2021. Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto Int. 36(15):1685–1708.
   Web of Science ®Google Scholar
• Pinto CA, Farinha JT, Singh S, Raposo H. 2021. Increasing the reliability of an electrical power system in a big European hospital through the petri nets and fuzzy inference system Mamdani modelling. Appl Sci. 11(6):2604.
   Google Scholar
• Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR. 2013. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. 6(7):2351–2365.
   Web of Science ®Google Scholar
• Pradhan AMS, Kim YT. 2016. Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. CATENA. 140:125–139.
   Web of Science ®Google Scholar
• Raghav H, Siva Kumar Konathala LN, Mishra N, Joshi B, Goyal R, Agrawal A, Sarkar B. 2021. Fe-decorated hierarchical molybdenum carbide for direct conversion of CO2 into ethylene: Tailoring activity and stability. J CO2 Util. 50:101607.
   Web of Science ®Google Scholar
• Razavi-Termeh SV, Shirani K, Pasandi M. 2021. Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models. Bull Eng Geol Environ. 80(3):2045–2067.
   Web of Science ®Google Scholar
• Saha S, Roy J, Pradhan B, Hembram TK. 2021. Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India. Adv Space Res. 68(7):2819–2840.
   Web of Science ®Google Scholar
• Sameen MI, Sarkar R, Pradhan B, Drukpa D, Alamri AM, Park H-J. 2020. Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests. Comput Geosci. 134:104336.
   Web of Science ®Google Scholar
• Sankar De D, Flores-Livas JA, Saha S, Genovese L, Goedecker S. 2018. Stable structures of exohedrally decorated C60-fullerenes. Carbon. 129:847–853.
   Web of Science ®Google Scholar
• Sharma A, Prakash C. 2022. Predicting landslide susceptibility of a mountainous region using a hybrid machine learning-based model. In Environmental concerns and remediation. Switzerland: Springer; p. 191–209.
   Google Scholar
• Sifa SF, Mahmud T, Tarin MA, Haque DME. 2020. Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh. Geol Ecol Landsc. 4(3):222–235.
   Google Scholar
• Sun B, Chen H, Wang J. 2015. An empirical margin explanation for the effectiveness of DECORATE ensemble learning algorithm. Knowl Based Syst. 78:1–12.
   Web of Science ®Google Scholar
• Sun D, Xu J, Wen H, Wang D. 2021. Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest. Eng Geol. 281:105972.
   Web of Science ®Google Scholar
• Sun H, Lv G, Mo J, Lv X, Du G, Liu Y. 2019. Application of KPCA combined with SVM in Raman spectral discrimination. Optik. 184:214–219.
   Web of Science ®Google Scholar
• Sun L, Zou B, Fu S, Chen J, Wang F. 2019. Speech emotion recognition based on DNN-decision tree SVM model. Speech Commun. 115:29–37.
   Web of Science ®Google Scholar
• Sun X, Chen J, Bao Y, Han X, Zhan J, Peng W. 2018. Landslide susceptibility mapping using logistic regression analysis along the Jinsha River and its tributaries close to Derong and Deqin County, Southwestern China. IJGI. 7(11):438.
   Google Scholar
• Sureiman O. 2013. Conceptual model on application of chi-square test in education and social sciences. Educ Res Rev. 8(15):1231–1241.
   Google Scholar
• Talukdar S, Eibek KU, Akhter S, Ziaul S, Towfiqul Islam ARM, Mallick J. 2021. Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh. Ecol Indic. 126:107612.
   Web of Science ®Google Scholar
• Thai Pham B, Tien Bui D, Prakash I. 2018. Landslide susceptibility modelling using different advanced decision trees methods. Civil Eng Environ Syst. 35(1-4):139–157.
   Web of Science ®Google Scholar
• Tran T-H, Dam ND, Jalal FE, Al-Ansari N, Ho LS, Phong TV, Iqbal M, Le HV, Nguyen HBT, Prakash I, et al. 2021. GIS-based soft computing models for landslide susceptibility mapping: A case study of pithoragarh district, uttarakhand state, India. Math Prob Eng. 2021:1–19.
   Web of Science ®Google Scholar
• Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Van Phong T, Ly H-B, Le T-T, Trinh PT. 2020. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena. 188:104451.
   Web of Science ®Google Scholar
• Vu DQ, Nguyen DD, Bui Q-AT, Trong DK, Prakash I, Pham BT. 2021. Estimation of California bearing ratio of soils using random forest based machine learning. J Sci Transport Technol. 1(5):48–61.
   Google Scholar
• Wang X, Lv D, Sun L, Wang W, Tu X-h, Ma Z-h 2021. Magnetic behaviors of a ferrimagnetic decorated kagome-like lattice under an external magnetic field. J Magn Magn Mater. 538:168259.
   Web of Science ®Google Scholar
• Wu B, Qiu W, Jia J, Liu N. 2021. Landslide susceptibility modeling using bagging-based positive-unlabeled learning. IEEE Geosci Remote Sens Lett. 18(5):766–770.
   Web of Science ®Google Scholar
• Xi C, Han M, Hu X, Liu B, He K, Luo G, Cao X. 2022. Effectiveness of Newmark-based sampling strategy for coseismic landslide susceptibility mapping using deep learning, support vector machine, and logistic regression. Bull Eng Geol Environ. 81(5):1–21.
   Web of Science ®Google Scholar
• Xi W, Li G, Moayedi H, Nguyen H. 2019. A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China. Geomatics Nat Hazards Risk. 10(1):1750–1771.
   Web of Science ®Google Scholar
• Xing X, Wu C, Li J, Li X, Zhang L, He R. 2021. Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method. Nat Hazards. 106(1):97–117.
   Web of Science ®Google Scholar
• Zhao C, Chen W, Wang Q, Wu Y, Yang B. 2015. A comparative study of statistical index and certainty factor models in landslide susceptibility mapping: a case study for the Shangzhou District, Shaanxi Province, China. Arab J Geosci. 8(11):9079–9088.
   Web of Science ®Google Scholar
• Zhao Z, Chen J, Xu K, Xie H, Gan X, Xu H. 2021. A spatial case-based reasoning method for regional landslide risk assessment. Int J Appl Earth Obs Geoinf. 102:102381.
   Web of Science ®Google Scholar
• Zhou X, Wen H, Zhang Y, Xu J, Zhang W. 2021. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization. Geosci Front. 12(5):101211.
   Web of Science ®Google Scholar
• Zounemat-Kermani M, Batelaan O, Fadaee M, Hinkelmann R. 2021. Ensemble machine learning paradigms in hydrology: A review. J Hydrol. 598:126266.
   Web of Science ®Google Scholar