Subject Areas : journal of Artificial Intelligence in Electrical Engineering
Solmaz Abdollahizad
1
,
Kayvan Asghari
2
,
majid samadzamin
3
1 -
2 -
3 -
Keywords:
Abstract :
1. Chen, W., H.R. Pourghasemi, and Z. Zhao, A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto international, 2017. 32(4): p. 367-385.
2. Feizizadeh, B. and T. Blaschke, An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. International Journal of Geographical Information Science, 2014. 28(3): p. 610-638.
3. Chen, W., Z. Sun, and J. Han, Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models. Applied sciences, 2019. 9(1): p. 171.
4. Kose, D.D. and T. Turk, GIS-based fully automatic landslide susceptibility analysis by weight-of-evidence and frequency ratio methods. Physical Geography, 2019. 40(5): p. 481-501.
5. Li, Y. and W. Chen, Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water, 2020. 12(1): p. 113.
6. Yan, F., et al., A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model. Geomorphology, 2019. 327: p. 170-187.
7. Wang, Y., et al., Comparative study of landslide susceptibility mapping with different recurrent neural networks. Computers & Geosciences, 2020. 138: p. 104445.
8. Pham, B.T., et al., A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto International, 2020. 35(12): p. 1267-1292.
9. Pourghasemi, H.R. and O. Rahmati, Prediction of the landslide susceptibility: Which algorithm, which precision? Catena, 2018. 162: p. 177-192.
10. Lee, S., et al., Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea. Geocarto international, 2020. 35(15): p. 1665-1679.
11. Ebrahimy, H., et al., A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods. Environmental Earth Sciences, 2020. 79: p. 1-12.
12. Park, S.-J., et al., Landslide susceptibility mapping and comparison using decision tree models: A Case Study of Jumunjin Area, Korea. Remote Sensing, 2018. 10(10): p. 1545.
13. Gheshlaghi, H.A. and B. Feizizadeh, An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping. Journal of African Earth Sciences, 2017. 133: p. 15-24.
14. Zhu, A.-X., et al., A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods. Catena, 2019. 183: p. 104188.
15. Fang, Z., et al., Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers & Geosciences, 2020. 139: p. 104470.
16. Phong, T.V., et al., Landslide susceptibility modeling using different artificial intelligence methods: A case study at Muong Lay district, Vietnam. Geocarto International, 2021. 36(15): p. 1685-1708.
17. Bui, D.T., et al., Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena, 2020. 188: p. 104426.
18. Ghorbanzadeh, O., et al., Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 2019. 11(2): p. 196.
19. Hong, H., et al., Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena, 2018. 163: p. 399-413.
20. Ahmadlou, M., et al., Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto International, 2019. 34(11): p. 1252-1272.
21. Pourghasemi, H.R., et al., Assessment of landslide-prone areas and their zonation using logistic regression, logitboost, and naïvebayes machine-learning algorithms. Sustainability, 2018. 10(10): p. 3697.
22. Kan, G., et al., A new hybrid data-driven model for event-based rainfall–runoff simulation. Neural Computing and Applications, 2017. 28: p. 2519-2534.
23. Nguyen, A.T. and L. Hens, Human ecology of climate change hazards in Vietnam. Cham, Switzerland: Springer International Publishing, 2019.
24. Aditian, A., T. Kubota, and Y. Shinohara, Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 2018. 318: p. 101-111.
25. Tian, Y., et al., Geomorphometry and statistical analyses of landslides triggered by the 2015 Mw 7.8 Gorkha earthquake and the Mw 7.3 aftershock, Nepal. Frontiers in Earth Science, 2020. 8: p. 407.
26. Sarker, I.H., Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2021. 2(3): p. 160.
27. Nagar, A. and J. Sharma, A Comparative Evaluation of Supervised Learning and Unsupervised Learning. resmilitaris, 2022. 12(5): p. 1498-1507.
28. Chou, S.-Y., J.-S.R. Jang, and Y.-H. Yang, FrameCNN: A weakly-supervised learning framework for frame-wise acoustic event detection and classification. Recall, 2017. 14: p. 55-64.
29. Du, Y., et al. Semi-Supervised Learning for Surface EMG-based Gesture Recognition. in IJCAI. 2017.
30. Farag, M.A., et al., Anti-acetylcholinesterase activity of essential oils and their major constituents from four Ocimum species. Zeitschrift für Naturforschung C, 2016. 71(11-12): p. 393-402.
31. Srinivas, M., G. Sucharitha, and A. Matta, Machine learning algorithms and applications. 2021: John Wiley & Sons.
32. Fernández-Delgado, M., et al., Do we need hundreds of classifiers to solve real world classification problems? The journal of machine learning research, 2014. 15(1): p. 3133-3181.
33. Teh, Y.W. and G.E. Hinton, Rate-coded restricted Boltzmann machines for face recognition. Advances in neural information processing systems, 2000. 13.
34. Graves, A. and A. Graves, Long short-term memory. Supervised sequence labelling with recurrent neural networks, 2012: p. 37-45.
35. Stoffel, M., D. Tiranti, and C. Huggel, Climate change impacts on mass movements—case studies from the European Alps. Science of the Total Environment, 2014. 493: p. 1255-1266.
36. Froude, M.J. and D.N. Petley, Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 2018. 18(8): p. 2161-2181.
37. Ma, Z., G. Mei, and F. Piccialli, Machine learning for landslides prevention: a survey. Neural Computing and Applications, 2021. 33(17): p. 10881-10907.
38. Chen, W., et al., A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 2017. 151: p. 147-160.
39. Tien Bui, D., et al., Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam. International Journal of Digital Earth, 2016. 9(11): p. 1077-1097.
40. Abdollahizad, S., et al., Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran. Earth Science Informatics, 2021. 14: p. 1861-1882.
41. Elmoulat, M., et al., Edge computing and artificial intelligence for landslides monitoring. Procedia Computer Science, 2020. 177: p. 480-487.
42. Tien Bui, D., et al., A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides. Remote Sensing, 2018. 10(10): p. 1538.
43. Alizadeh, M., et al., Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran. Sustainability, 2018. 10(10): p. 3376.
44. Van Dao, D., et al., A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena, 2020. 188: p. 104451.
45. Li, W., Z. Fang, and Y. Wang, Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China. Stochastic Environmental Research and Risk Assessment, 2021: p. 1-22.
46. Chen, X. and W. Chen, GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena, 2021. 196: p. 104833.
47. Kavzoglu, T., E. Kutlug Sahin, and I. Colkesen, An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Natural Hazards, 2015. 76: p. 471-496.
48. Luo, X., et al., Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features. Scientific reports, 2019. 9(1): p. 15369.
49. Schmidt, J., et al., Recent advances and applications of machine learning in solid-state materials science. npj computational materials, 2019. 5(1): p. 83.
50. Kalantar, B., et al., Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, 2018. 9(1): p. 49-69.
51. Kuhn, S., M.J. Cracknell, and A.M. Reading, Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia. Geophysics, 2018. 83(4): p. B183-B193.
52. Goetz, J., et al., Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & geosciences, 2015. 81: p. 1-11.
53. Song, Y., et al., Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the Three Gorges Reservoir Area (China). ISPRS International Journal of Geo-Information, 2018. 8(1): p. 4.
54. Miao, F., et al., Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides, 2018. 15: p. 475-488.
55. Zhu, C.H. and G.D. Hu, Time series prediction of landslide displacement using SVM model: application to Baishuihe landslide in Three Gorges reservoir area, China. Applied Mechanics and Materials, 2013. 239: p. 1413-1420.
56. Li, Y., et al., Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model. Scientific reports, 2019. 9(1): p. 19853.
57. Wang, Y., et al., A comparative study of different machine learning methods for reservoir landslide displacement prediction. Engineering Geology, 2022. 298: p. 106544.
58. Abdollahizad, S., et al., Stacking ensemble approach in data mining methods for landslide prediction. The Journal of Supercomputing, 2023. 79(8): p. 8583-8610.
59. Saseendran, A.T., et al., Impact of noise in dataset on machine learning algorithms. Mach. Learn. Res, 2019. 1: p. 1-8.
60. Gupta, S.K. and D.P. Shukla, Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas. Landslides, 2023. 20(5): p. 933-949.
61. Krawczyk, B., Learning from imbalanced data: open challenges and future directions. Progress in artificial intelligence, 2016. 5(4): p. 221-232.