Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms

References

• Abdollahzadeh, B., F. S. Gharehchopogh, N. Khodadadi, and S. Mirjalili. 2022. “Mountain Gazelle Optimizer: A New Nature-Inspired Metaheuristic Algorithm for Global Optimization Problems.” Advances in Engineering Software 174: 103282. https://doi.org/10.1016/j.advengsoft.2022.103282.
   Web of Science ®Google Scholar
• Abdulwahid, W. M., and B. Pradhan. 2017. “Landslide Vulnerability and Risk Assessment for Multi-Hazard Scenarios Using Airborne Laser Scanning Data (LiDAR).” Landslides 14 (3): 1057–1076. https://doi.org/10.1007/s10346-016-0744-0.
   Web of Science ®Google Scholar
• Abedini, M., B. Ghasemian, A. Shirzadi, H. Shahabi, K. Chapi, B. T. Pham, B. B. Ahmad, and D. Tien Bui. 2019. “A Novel Hybrid Approach of Bayesian Logistic Regression and Its Ensembles for Landslide Susceptibility Assessment.” Geocarto International 34 (13): 1427–1457. https://doi.org/10.1080/10106049.2018.1499820.
   Web of Science ®Google Scholar
• Abedini, M., and S. Tulabi. 2018. “Assessing LNRF, FR, and AHP Models in Landslide Susceptibility Mapping Index: A Comparative Study of Nojian Watershed in Lorestan Province, Iran.” Environmental Earth Sciences 77 (11): 1–13. https://doi.org/10.1007/s12665-018-7524-1.
   Web of Science ®Google Scholar
• Abualigah, L., M. Abd Elaziz, P. Sumari, Z. W. Geem, and A. H. Gandomi. 2022. “Reptile Search Algorithm (RSA): A Nature-Inspired Meta-Heuristic Optimizer.” Expert Systems with Applications 191: 116158. https://doi.org/10.1016/j.eswa.2021.116158.
   Web of Science ®Google Scholar
• Al-Najjar, H. A., and B. Pradhan. 2021. “Spatial Landslide Susceptibility Assessment Using Machine Learning Techniques Assisted by Additional Data Created with Generative Adversarial Networks.” Geoscience Frontiers 12 (2): 625–637. https://doi.org/10.1016/j.gsf.2020.09.002.
   Web of Science ®Google Scholar
• Alqadhi, S., J. Mallick, S. Talukdar, A. A. Bindajam, T. K. Saha, M. Ahmed, and R. A. Khan. 2021. “Combining Logistic Regression-Based Hybrid Optimized Machine Learning Algorithms with Sensitivity Analysis to Achieve Robust Landslide Susceptibility Mapping.” Geocarto International 37 (25): 9518–9543. https://doi.org/10.1080/10106049.2021.2022009.
   Web of Science ®Google Scholar
• Aslam, B., A. Zafar, and U. Khalil. 2023. “Comparative Analysis of Multiple Conventional Neural Networks for Landslide Susceptibility Mapping.” Natural Hazards 115 (1): 673–707. https://doi.org/10.1007/s11069-022-05570-x.
   Web of Science ®Google Scholar
• Azarafza, M., M. Azarafza, H. Akgün, P. M. Atkinson, and R. Derakhshani. 2021. “Deep Learning-Based Landslide Susceptibility Mapping.” Scientific Reports 11 (1): 24112. https://doi.org/10.1038/s41598-021-03585-1.
   PubMed Web of Science ®Google Scholar
• Azarafza, M., A. Ghazifard, H. Akgün, and E. Asghari-Kaljahi. 2018. “Landslide Susceptibility Assessment of South Pars Special Zone, Southwest Iran.” Environmental Earth Sciences 77 (24): 1–29. https://doi.org/10.1007/s12665-018-7978-1.
   Web of Science ®Google Scholar
• Balogun, A. L., F. Rezaie, Q. B. Pham, L. Gigović, S. Drobnjak, Y. A. Aina, M. Panahi, S. T. Yekeen, and S. Lee. 2021. “Spatial Prediction of Landslide Susceptibility in Western Serbia Using Hybrid Support Vector Regression (SVR) with GWO, BAT and COA Algorithms.” Geoscience Frontiers 12 (3): 101104. https://doi.org/10.1016/j.gsf.2020.10.009.
   Web of Science ®Google Scholar
• Berhane, G., M. Kebede, and N. Alfarrah. 2021. “Landslide Susceptibility Mapping and Rock Slope Stability Assessment Using Frequency Ratio and Kinematic Analysis in the Mountains of Mgulat Area, Northern Ethiopia.” Bulletin of Engineering Geology and the Environment 80 (1): 285–301. https://doi.org/10.1007/s10064-020-01905-9.
   Web of Science ®Google Scholar
• Bozzolan, E., E. A. Holcombe, F. Pianosi, I. Marchesini, M. Alvioli, and T. Wagener. 2023. “A Mechanistic Approach to Include Climate Change and Unplanned Urban Sprawl in Landslide Susceptibility Maps.” Science of the Total Environment 858: 159412. https://doi.org/10.1016/j.scitotenv.2022.159412.
   PubMed Web of Science ®Google Scholar
• Brabb, E. E. 1984. “Innovative Approaches to Landslide Hazard Mapping.” Paper presented at proceedings 4th international symposium on landslides, Toronto, Canada, September 1984.
   Google Scholar
• Carrara, A., M. Cardinali, R. Detti, F. Guzzetti, V. Pasqui, and P. Reichenbach. 1991. “GIS Techniques and Statistical Models in Evaluating Landslide Hazard.” Earth Surface Processes and Landforms 16 (5): 427–445. https://doi.org/10.1002/esp.3290160505.
   Web of Science ®Google Scholar
• Chang, Z., J. Huang, F. Huang, K. Bhuyan, S. R. Meena, and F. Catani. 2023. “Uncertainty Analysis of non-Landslide Sample Selection in Landslide Susceptibility Prediction Using Slope Unit-Based Machine Learning Models.” Gondwana Research 117: 307–320. https://doi.org/10.1016/j.gr.2023.02.007.
   Web of Science ®Google Scholar
• Chen, Z., H. Zhou, F. Ye, B. Liu, and W. Fu. 2022. “Landslide Susceptibility Mapping Along the Anninghe Fault Zone in China Using SVM and ACO-PSO-SVM Models.” Lithosphere 2022 (1): 5216125. https://doi.org/10.2113/2022/5216125.
   Web of Science ®Google Scholar
• Chou, J. S., and D. N. Truong. 2021. “A Novel Metaheuristic Optimizer Inspired by Behavior of Jellyfish in Ocean.” Applied Mathematics and Computation 389: 125535. https://doi.org/10.1016/j.amc.2020.125535.
   Web of Science ®Google Scholar
• Dehghani, M., Z. Montazeri, E. Trojovská, and P. Trojovský. 2023. “Coati Optimization Algorithm: A new bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.” Knowledge-Based Systems 259: 110011. https://doi.org/10.1016/j.knosys.2022.110011.
   Web of Science ®Google Scholar
• Dou, J., A. P. Yunus, D. T. Bui, A. Merghadi, M. Sahana, Z. Zhu, C. Chen, Z. Han, and B. T. Pham. 2020. “Improved Landslide Assessment Using Support Vector Machine with Bagging, Boosting, and Stacking Ensemble Machine Learning Framework in a Mountainous Watershed, Japan.” Landslides 17 (3): 641–658. https://doi.org/10.1007/s10346-019-01286-5.
   Web of Science ®Google Scholar
• Eker, A. M., M. Dikmen, S. Cambazoğlu, H. S. B. Düzgün, and H. Akgün. 2012. “Application of Artificial Neural Network and Logistic Regression Methods to Landslide Susceptibility Mapping and Comparison of the Results for the Ulus District, Bartın.” Special issue, Journal of the Faculty of Engineering and Architecture of Gazi University 27 (1): 163–173.
   Web of Science ®Google Scholar
• Eker, A. M., M. Dikmen, S. Cambazoğlu, ŞH Düzgün, and H. Akgün. 2015. “Evaluation and Comparison of Landslide Susceptibility Mapping Methods: A Case Study for the Ulus District, Bartın, Northern Turkey.” International Journal of Geographical Information Science 29 (1): 132–158. https://doi.org/10.1080/13658816.2014.953164.
   Web of Science ®Google Scholar
• Fang, Z., Y. Wang, L. Peng, and H. Hong. 2021. “A Comparative Study of Heterogeneous Ensemble-Learning Techniques for Landslide Susceptibility Mapping.” International Journal of Geographical Information Science 35 (2): 321–347. https://doi.org/10.1080/13658816.2020.1808897.
   Web of Science ®Google Scholar
• GAR (Global Assessment Report On Disaster Risk Reduction). 2009. Global Assessment Report on Disaster Risk Reduction. Geneva, Switzerland: United Nations International Strategy for Disaster Reduction Secretariat.
   Google Scholar
• GAR (Global Assessment Report On Disaster Risk Reduction). 2019. Global Assessment Report on Disaster Risk Reduction. Geneva, Switzerland: United Nations Office for Disaster Risk Reduction.
   Google Scholar
• Ghorbanzadeh, O., K. Gholamnia, and P. Ghamisi. 2022. “The Application of ResU-net and OBIA for Landslide Detection from Multi-Temporal Sentinel-2 Images.” Big Earth Data, 1–26. https://doi.org/10.1080/20964471.2022.2031544.
   Google Scholar
• Guo, Z., Y. Shi, F. Huang, X. Fan, and J. Huang. 2021. “Landslide Susceptibility Zonation Method Based on C5. 0 Decision Tree and K-Means Cluster Algorithms to Improve the Efficiency of Risk Management.” Geoscience Frontiers 12 (6): 101249. https://doi.org/10.1016/j.gsf.2021.101249.
   Web of Science ®Google Scholar
• Guzzetti, F., A. Carrara, M. Cardinali, and P. Reichenbach. 1999. “Landslide Hazard Evaluation: A Review of Current Techniques and Their Application in a Multi-Scale Study, Central Italy.” Geomorphology 31 (1-4): 181–216. https://doi.org/10.1016/S0169-555X(99)00078-1.
   Web of Science ®Google Scholar
• Hodasová, K., and M. Bednarik. 2021. “Effect of Using Various Weighting Methods in a Process of Landslide Susceptibility Assessment.” Natural Hazards 105 (1): 481–499. https://doi.org/10.1007/s11069-020-04320-1.
   Web of Science ®Google Scholar
• Hong, H., Y. Miao, J. Liu, and A. X. Zhu. 2019. ““Exploring the Effects of the Design and Quantity of Absence Data on the Performance of Random Forest-Based Landslide Susceptibility Mapping.”.” Catena 176: 45–64. https://doi.org/10.1016/j.catena.2018.12.035.
   Web of Science ®Google Scholar
• Houssein, E. H., D. Oliva, E. Çelik, M. M. Emam, and R. M. Ghoniem. 2023. “Boosted Sooty Tern Optimization Algorithm for Global Optimization and Feature Selection.” Expert Systems with Applications 213: 119015. https://doi.org/10.1016/j.eswa.2022.119015.
   Web of Science ®Google Scholar
• Hu, Q., Y. Zhou, S. Wang, and F. Wang. 2020. “Machine Learning and Fractal Theory Models for Landslide Susceptibility Mapping: Case Study from the Jinsha River Basin.” Geomorphology 351: 106975. https://doi.org/10.1016/j.geomorph.2019.106975.
   Web of Science ®Google Scholar
• Huang, F., Z. Cao, J. Guo, S. H. Jiang, S. Li, and Z. Guo. 2020. “Comparisons of Heuristic, General Statistical and Machine Learning Models for Landslide Susceptibility Prediction and Mapping.” Catena 191: 104580. https://doi.org/10.1016/j.catena.2020.104580.
   Web of Science ®Google Scholar
• Huang, F., J. Chen, W. Liu, J. Huang, H. Hong, and W. Chen. 2022. “Regional Rainfall-Induced Landslide Hazard Warning Based on Landslide Susceptibility Mapping and a Critical Rainfall Threshold.” Geomorphology 408: 108236. https://doi.org/10.1016/j.geomorph.2022.108236.
   Web of Science ®Google Scholar
• Huang, W., M. Ding, Z. Li, J. Yu, D. Ge, Q. Liu, and J. Yang. 2023. “Landslide Susceptibility Mapping and Dynamic Response Along the Sichuan-Tibet Transportation Corridor Using Deep Learning Algorithms.” Catena 222: 106866. https://doi.org/10.1016/j.catena.2022.106866.
   Web of Science ®Google Scholar
• Huang, F., Z. Ye, S. Jiang, J. Huang, Z. Chang, and J. Chen. 2021. “Uncertainty Study of Landslide Susceptibility Prediction Considering the Different Attribute Interval Numbers of Environmental Factors and Different Data-Based Models.” Catena 202: 105250. https://doi.org/10.1016/j.catena.2021.105250.
   Web of Science ®Google Scholar
• Hung, L. Q., N. T. H. Van, P. Van Son, N. H. Khanh, and L. T. Binh. 2016. “Landslide Susceptibility Mapping by Combining the Analytical Hierarchy Process and Weighted Linear Combination Methods: A Case Study in the Upper Lo River Catchment (Vietnam).” Landslides 13 (5): 1285–1301. https://doi.org/10.1007/s10346-015-0657-3.
   Web of Science ®Google Scholar
• Jaafari, A., M. Panahi, D. Mafi-Gholami, O. Rahmati, H. Shahabi, A. Shirzadi, S. Lee, D. T. Bui, and B. Pradhan. 2022. “Swarm Intelligence Optimization of the Group Method of Data Handling Using the Cuckoo Search and Whale Optimization Algorithms to Model and Predict Landslides.” Applied Soft Computing 116: 108254. https://doi.org/10.1016/j.asoc.2021.108254.
   Web of Science ®Google Scholar
• Jafari, M., E. Salajegheh, and J. Salajegheh. 2021. “Elephant Clan Optimization: A Nature-Inspired Metaheuristic Algorithm for the Optimal Design of Structures.” Applied Soft Computing 113: 107892. https://doi.org/10.1016/j.asoc.2021.107892.
   Web of Science ®Google Scholar
• Ji, J., H. Cui, T. Zhang, J. Song, and Y. Gao. 2022. “A GIS-Based Tool for Probabilistic Physical Modelling and Prediction of Landslides: GIS-FORM Landslide Susceptibility Analysis in Seismic Areas.” Landslides 19 (9): 2213–2231. https://doi.org/10.1007/s10346-022-01885-9.
   Web of Science ®Google Scholar
• Jiao, Y., D. Zhao, Y. Ding, Y. Liu, Q. Xu, Y. Qiu, C. Liu, Z. Liu, Z. Zha, and R. Li. 2019. “Performance Evaluation for Four GIS-Based Models Purposed to Predict and map Landslide Susceptibility: A Case Study at a World Heritage Site in Southwest China.” Catena 183: 104221. https://doi.org/10.1016/j.catena.2019.104221.
   Web of Science ®Google Scholar
• Kim, H. G., D. K. Lee, C. Park, Y. Ahn, S. H. Kil, S. Sung, and G. S. Biging. 2018. “Estimating Landslide Susceptibility Areas Considering the Uncertainty Inherent in Modeling Methods.” Stochastic Environmental Research and Risk Assessment 32 (11): 2987–3019. https://doi.org/10.1007/s00477-018-1609-y.
   Web of Science ®Google Scholar
• Kornejady, A., M. Ownegh, and A. Bahremand. 2017. “Landslide Susceptibility Assessment Using Maximum Entropy Model with two Different Data Sampling Methods.” Catena 152: 144–162. https://doi.org/10.1016/j.catena.2017.01.010.
   Web of Science ®Google Scholar
• Kuyu, YÇ, and F. Vatansever. 2022. “GOZDE: A Novel Metaheuristic Algorithm for Global Optimization.” Future Generation Computer Systems 136: 128–152. https://doi.org/10.1016/j.future.2022.05.022.
   Web of Science ®Google Scholar
• Li, Y., W. Wang, G. Wang, and Q. Tan. 2022. “Actual Evapotranspiration Estimation Over the Tuojiang River Basin Based on a Hybrid CNN-RF Model.” Journal of Hydrology 610: 127788. https://doi.org/10.1016/j.jhydrol.2022.127788.
   Web of Science ®Google Scholar
• Lin, Q., S. Steger, M. Pittore, J. Zhang, L. Wang, T. Jiang, and Y. Wang. 2022. “Evaluation of Potential Changes in Landslide Susceptibility and Landslide Occurrence Frequency in China Under Climate Change.” Science of the Total Environment 850: 158049. https://doi.org/10.1016/j.scitotenv.2022.158049.
   PubMed Web of Science ®Google Scholar
• Liu, Q., A. Tang, and D. Huang. 2023. “Exploring the Uncertainty of Landslide Susceptibility Assessment Caused by the Number of Non–Landslides.” Catena 227: 107109. https://doi.org/10.1016/j.catena.2023.107109.
   Web of Science ®Google Scholar
• Lucchese, L. V., G. G. de Oliveira, and O. C. Pedrollo. 2021. “Investigation of the Influence of Nonoccurrence Sampling on Landslide Susceptibility Assessment Using Artificial Neural Networks.” Catena 198: 105067. https://doi.org/10.1016/j.catena.2020.105067.
   Web of Science ®Google Scholar
• Lv, L., T. Chen, J. Dou, and A. Plaza. 2022. “A Hybrid Ensemble-Based Deep-Learning Framework for Landslide Susceptibility Mapping.” International Journal of Applied Earth Observation and Geoinformation 108: 102713. https://doi.org/10.1016/j.jag.2022.102713.
   Web of Science ®Google Scholar
• Mandal, K., S. Saha, and S. Mandal. 2021. “Applying Deep Learning and Benchmark Machine Learning Algorithms for Landslide Susceptibility Modelling in Rorachu River Basin of Sikkim Himalaya, India.” Geoscience Frontiers 12 (5): 101203. https://doi.org/10.1016/j.gsf.2021.101203.
   Web of Science ®Google Scholar
• Mebrahtu, T. K., T. Heinze, S. Wohnlich, and M. Alber. 2022. “Slope Stability Analysis of Deep-Seated Landslides Using Limit Equilibrium and Finite Element Methods in Debre Sina Area, Ethiopia.” Bulletin of Engineering Geology and the Environment 81 (10): 403. https://doi.org/10.1007/s10064-022-02906-6.
   Web of Science ®Google Scholar
• Merghadi, A., A. P. Yunus, J. Dou, J. Whiteley, B. ThaiPham, D. T. Bui, R. Avtar, and B. Abderrahmane. 2020. “Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance.” Earth-Science Reviews 207: 103225. https://doi.org/10.1016/j.earscirev.2020.103225.
   Web of Science ®Google Scholar
• Momeny, M., A. A. Neshat, A. Gholizadeh, A. Jafarnezhad, E. Rahmanzadeh, M. Marhamati, B. Moradi, A. Ghafoorifar, and Y. D. Zhang. 2022. “Greedy Autoaugment for Classification of Mycobacterium Tuberculosis Image via Generalized Deep CNN Using Mixed Pooling Based on Minimum Square Rough Entropy.” Computers in Biology and Medicine 141: 105175. https://doi.org/10.1016/j.compbiomed.2021.105175.
   PubMed Web of Science ®Google Scholar
• Ngo, P. T. T., M. Panahi, K. Khosravi, O. Ghorbanzadeh, N. Kariminejad, A. Cerda, and S. Lee. 2021. “Evaluation of Deep Learning Algorithms for National Scale Landslide Susceptibility Mapping of Iran.” Geoscience Frontiers 12 (2): 505–519. https://doi.org/10.1016/j.gsf.2020.06.013.
   Web of Science ®Google Scholar
• Nikoobakht, S., M. Azarafza, H. Akgün, and R. Derakhshani. 2022. “Landslide Susceptibility Assessment by Using Convolutional Neural Network.” Applied Sciences 12 (12): 1–22. https://doi.org/10.3390/app12125992.
   Google Scholar
• Okalp, K., and H. Akgün. 2016. “National Level Landslide Susceptibility Assessment of Turkey Utilizing Public Domain Dataset.” Environmental Earth Sciences 75 (9): 1–21. https://doi.org/10.1007/s12665-016-5640-3.
   Web of Science ®Google Scholar
• Okalp, K., and H. Akgün. 2022. “Landslide Susceptibility Assessment in Medium-Scale: Case Studies from the Major Drainage Basins of Turkey.” Environmental Earth Sciences 81 (8): 244. https://doi.org/10.1007/s12665-022-10355-3.
   Web of Science ®Google Scholar
• Ozturk, D., and N. Uzel-Gunini. 2022. “Investigation of the Effects of Hybrid Modeling Approaches, Factor Standardization, and Categorical Mapping on the Performance of Landslide Susceptibility Mapping in Van, Turkey.” Natural Hazards 114 (3): 2571–2604. https://doi.org/10.1007/s11069-022-05480-y.
   Web of Science ®Google Scholar
• Petley, D. 2012. “Global Patterns of Loss of Life from Landslides.” Geology 40 (10): 927–930. https://doi.org/10.1130/G33217.1.
   Web of Science ®Google Scholar
• Pham, B. T., I. Prakash, and D. T. Bui. 2018. “Spatial Prediction of Landslides Using a Hybrid Machine Learning Approach Based on Random Subspace and Classification and Regression Trees.” Geomorphology 303: 256–270. https://doi.org/10.1016/j.geomorph.2017.12.008.
   Web of Science ®Google Scholar
• Pham, B. T., D. Tien Bui, H. R. Pourghasemi, P. Indra, and M. B. Dholakia. 2017. “Landslide Susceptibility Assesssment in the Uttarakhand Area (India) Using GIS: A Comparison Study of Prediction Capability of Naïve Bayes, Multilayer Perceptron Neural Networks, and Functional Trees Methods.” Theoretical and Applied Climatology 128 (1-2): 255–273. https://doi.org/10.1007/s00704-015-1702-9.
   Web of Science ®Google Scholar
• Pourghasemi, H. R., and O. Rahmati. 2018. “Prediction of the Landslide Susceptibility: Which Algorithm, Which Precision?” Catena 162: 177–192. https://doi.org/10.1016/j.catena.2017.11.022.
   Web of Science ®Google Scholar
• Reichenbach, P., M. Rossi, B. D. Malamud, M. Mihir, and F. Guzzetti. 2018. “A Review of Statistically-Based Landslide Susceptibility Models.” Earth-Science Reviews 180: 60–91. https://doi.org/10.1016/j.earscirev.2018.03.001.
   Web of Science ®Google Scholar
• Roy, P., S. C. Pal, R. Chakrabortty, I. Chowdhuri, S. Malik, and B. Das. 2020. “Threats of Climate and Land Use Change on Future Flood Susceptibility.” Journal of Cleaner Production 272: 122757. https://doi.org/10.1016/j.jclepro.2020.122757.
   Web of Science ®Google Scholar
• Sameen, M. I., B. Pradhan, and S. Lee. 2020. “Application of Convolutional Neural Networks Featuring Bayesian Optimization for Landslide Susceptibility Assessment.” Catena 186: 104249. https://doi.org/10.1016/j.catena.2019.104249.
   Web of Science ®Google Scholar
• Schuster, Robert L. 1996. “Socioeconomic Significance of Landslides.” In Landslides: Investigation and Mitigation, Transportation Research Board Special Report 247, edited by A. K. Turner, and R. L. Schuster, 12–35. Washington, DC, USA: National Academy of Sciences.
   Google Scholar
• Shirani, K., M. Pasandi, and A. Arabameri. 2018. “Landslide Susceptibility Assessment by Dempster–Shafer and Index of Entropy Models, Sarkhoun Basin, Southwestern Iran.” Natural Hazards 93 (3): 1379–1418. https://doi.org/10.1007/s11069-018-3356-2.
   Web of Science ®Google Scholar
• Sun, D., S. Shi, H. Wen, J. Xu, X. Zhou, and J. Wu. 2021. “A Hybrid Optimization Method of Factor Screening Predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping.” Geomorphology 379: 107623. https://doi.org/10.1016/j.geomorph.2021.10762.
   Web of Science ®Google Scholar
• Taalab, K., T. Cheng, and Y. Zhang. 2018. “Mapping Landslide Susceptibility and Types Using Random Forest.” Big Earth Data 2 (2): 159–178. https://doi.org/10.1080/20964471.2018.1472392.
   Google Scholar
• Tien Bui, D., T. A. Tuan, H. Klempe, B. Pradhan, and I. Revhaug. 2016. “Spatial Prediction Models for Shallow Landslide Hazards: A Comparative Assessment of the Efficacy of Support Vector Machines, Artificial Neural Networks, Kernel Logistic Regression, and Logistic Model Tree.” Landslides 13 (2): 361–378. https://doi.org/10.1007/s10346-015-0557-6.
   Web of Science ®Google Scholar
• Trinh, T., B. T. Luu, T. H. T. Le, D. H. Nguyen, T. Van Tran, T. H. Van Nguyen, K. Q. Nguye, and L. T. Nguyen. 2022. “A Comparative Analysis of Weight-Based Machine Learning Methods for Landslide Susceptibility Mapping in Ha Giang Area.” Big Earth Data, 1–30. https://doi.org/10.1080/20964471.2022.2043520.
   Google Scholar
• Ullah, K., Y. Wang, Z. Fang, L. Wang, and M. Rahman. 2022. “Multi-hazard Susceptibility Mapping Based on Convolutional Neural Networks.” Geoscience Frontiers 13 (5): 101425. https://doi.org/10.1016/j.gsf.2022.101425.
   Web of Science ®Google Scholar
• Wang, Y., Z. Fang, and H. Hong. 2019. “Comparison of Convolutional Neural Networks for Landslide Susceptibility Mapping in Yanshan County, China.” Science of the Total Environment 666: 975–993. https://doi.org/10.1016/j.scitotenv.2019.02.263.
   PubMed Web of Science ®Google Scholar
• Wang, Y., L. Feng, S. Li, F. Ren, and Q. Du. 2020. “A Hybrid Model Considering Spatial Heterogeneity for Landslide Susceptibility Mapping in Zhejiang Province, China.” Catena 188: 104425. https://doi.org/10.1016/j.catena.2019.104425.
   Web of Science ®Google Scholar
• Wang, H., L. Zhang, H. Luo, J. He, and R. W. M. Cheung. 2021. “AI-powered Landslide Susceptibility Assessment in Hong Kong.” Engineering Geology 288: 106103. https://doi.org/10.1016/j.enggeo.2021.106103.
   Web of Science ®Google Scholar
• Wei, X., L. Zhang, P. Gardoni, Y. Chen, L. Tan, D. Liu, C. Du, and H. Li. 2023. “Comparison of Hybrid Data-Driven and Physical Models for Landslide Susceptibility Mapping at Regional Scales.” Acta Geotechnica, 1–24. https://doi.org/10.1007/s11440-023-01841-4.
   Web of Science ®Google Scholar
• Wu, Y., Y. Ke, Z. Chen, S. Liang, H. Zhao, and H. Hong. 2020. “Application of Alternating Decision Tree with AdaBoost and Bagging Ensembles for Landslide Susceptibility Mapping.” Catena 187: 104396. https://doi.org/10.1016/j.catena.2019.104396.
   Web of Science ®Google Scholar
• Xie, Y., W. Sun, M. Ren, S. Chen, Z. Huang, and X. Pan. 2023. “Stacking Ensemble Learning Models for Daily Runoff Prediction Using 1D and 2D CNNs.” Expert Systems with Applications 217: 119469. https://doi.org/10.1016/j.eswa.2022.119469.
   Web of Science ®Google Scholar
• Yang, Z., J. Li, J. Hyyppä, J. Gong, J. Liu, and B. Yang. 2023. “A Comprehensive and up-to-Date Web-Based Interactive 3D Emergency Response and Visualization System Using Cesium Digital Earth: Taking Landslide Disaster as an Example.” Big Earth Data, 1–23. https://doi.org/10.1080/20964471.2023.2172823.
   Google Scholar
• Yi, Y., Z. Zhang, W. Zhang, Q. Xu, C. Deng, and Q. Li. 2019. “GIS-based Earthquake-Triggered-Landslide Susceptibility Mapping with an Integrated Weighted Index Model in Jiuzhaigou Region of Sichuan Province, China.” Natural Hazards and Earth System Sciences 19 (9): 1973–1988. https://doi.org/10.5194/nhess-19-1973-2019.
   Web of Science ®Google Scholar
• Yin, G., J. Luo, F. Niu, Z. Lin, and M. Liu. 2021. “Machine Learning-Based Thermokarst Landslide Susceptibility Modeling Across the Permafrost Region on the Qinghai-Tibet Plateau.” Landslides 18 (7): 2639–2649. https://doi.org/10.1007/s10346-021-01669-7.
   Web of Science ®Google Scholar
• Youssef, A. M., and H. R. Pourghasemi. 2021. “Landslide Susceptibility Mapping Using Machine Learning Algorithms and Comparison of Their Performance at Abha Basin, Asir Region, Saudi Arabia.” Geoscience Frontiers 12 (2): 639–655. https://doi.org/10.1016/j.gsf.2020.05.010.
   Web of Science ®Google Scholar
• Youssef, A. M., B. Pradhan, A. Dikshit, M. M. Al-Katheri, S. S. Matar, and A. M. Mahdi. 2022. “Landslide Susceptibility Mapping Using CNN-1D and 2D Deep Learning Algorithms: Comparison of Their Performance at Asir Region, KSA.” Bulletin of Engineering Geology and the Environment 81 (4): 165. https://doi.org/10.1007/s10064-022-02657-4.
   Web of Science ®Google Scholar
• Zhang, J., X. Ma, J. Zhang, D. Sun, X. Zhou, C. Mi, and H. Wen. 2023. “Insights Into Geospatial Heterogeneity of Landslide Susceptibility Based on the SHAP-XGBoost Model.” Journal of Environmental Management 332: 117357. https://doi.org/10.1016/j.jenvman.2023.117357.
   PubMed Web of Science ®Google Scholar
• Zhao, Z., and J. Chen. 2023. “A Robust Discretization Method of Factor Screening for Landslide Susceptibility Mapping Using Convolution Neural Network, Random Forest, and Logistic Regression Models.” International Journal of Digital Earth 16 (1): 408–429. https://doi.org/10.1080/17538947.2023.2174192.
   Web of Science ®Google Scholar
• Zhong, C., G. Li, and Z. Meng. 2022. “Beluga Whale Optimization: A Novel Nature-Inspired Metaheuristic Algorithm.” Knowledge-Based Systems 251: 109215. https://doi.org/10.1016/j.knosys.2022.109215.
   Web of Science ®Google Scholar
• Zhou, X., H. Wen, Y. Zhang, J. Xu, and W. Zhang. 2021. “Landslide Susceptibility Mapping Using Hybrid Random Forest with GeoDetector and RFE for Factor Optimization.” Geoscience Frontiers 12 (5): 101211. https://doi.org/10.1016/j.gsf.2021.101211.
   Web of Science ®Google Scholar