Amighpey M, Arabi S. 2023. Study of land subsidence status due to uncontrolled groundwater extraction in Iran using comprehensive subsidence map of the country. Iranian Water Resources Research.19(5):145-156.
https://doi.org/10.22034/iwrr.2023.186215
Arabameri A, Rezaie F, Pal SC, Cerda A, Saha A, Chakrabortty R, Lee S. 2021. Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM. Geoscience Frontiers. 12(6): 101230.
https://doi.org/10.1016/j.gsf.2021.101230
Bates RL, Jackson JA. 1980. Glossary of Geology (Second edition): Falls Church, Virginia. American Geological Institute. 749 p.
Fiaschi S, Tessitore S, Bonì R, Di Martire D, Achilli V, Borgstrom S, Calcaterra D. 2017. From ERS-1/2 to Sentinel-1: Two decades of subsidence monitored through A-DInSAR techniques in the Ravenna area (Italy). GIScience and Remote Sensing. 54(3): 305-328. https://doi.org/10.1080/15481603.2016.1269404
Friston KJ, Penny W, Phillips C, Kiebel S, Hinton G, Ashburner J. 2002. Classical and Bayesian inference in neuroimaging: theory. Neuro Image. 16(2): 465-483.
https://doi.org/10.1006/nimg.2002.1090.
Gorriz JM, Martín-Clemente R, Puntonet CG, Ortiz A, Ramirez J, Suckling J. 2022. A hypothesis-driven method based on machine learning for neuroimaging data analysis. Neurocomputing. 510:159-171. https://doi.org/10.1016/j.neucom.2022.09.001
Hasibuan HS, Tambunan RP, Rukmana D, Permana CT, Elizandri BN, Putra GAY, Ristya Y. 2023. Policymaking and the spatial characteristics of land subsidence in North Jakarta. City and Environment Interactions. Volume 18, April 2023, 100103. https://doi.org/10.1016/j.cacint.2023.100103
Hastie T, Tibshirani R, Friedman J, Hastie T, Tibshirani R, Friedman J. 2009. The elements of statistical learning: Data mining, inference, and prediction, Random forests. Springer Series in Statistics. pp. 587-604.
Hothorn T, Hornik K, Zeileis A. 2006. Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics. 15(3): 651-674. https://doi.org/10.1198/106186006X133933
Jeanne P, Farr Tom G, Rutqvist J, Vasco D. 2019. Role of agricultural activity on land subsidence in the San Joaquin Valley, California. Journal of Hydrology. 569: 462-469. https://doi.org/10.1016/j.jhydrol.2018.11.077
Lee S, Park I, Choi JK. 2012. Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environmental Management. 49 (2): 347-358. https://doi.org/10.1007/s00267-011-9766-5
Madani K, AghaKouchak A, Mirchi A. 2016. Iran’s socio-economic drought: Challenges of a water-bankrupt nation. Iranian Studies. 49(6): 997-1016. https://doi.org/10.1080/00210862.2016.1259286
Mallik S, Das S, Chakraborty A, Mishra U, Talukdar S, Bera S, Ramana GV. 2023. Prediction of non-carcinogenic health risk using Hybrid Monte Carlo-machine learning approach. Human and Ecological Risk Assessment: An International Journal. 29(3-4): 777-800.
DOI:10.1080/10807039.2023.2188417
Motlagh ZK, Derakhshani R, Sayadi MH. 2023. Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach. Groundwater for Sustainable Development.23(1):101037. https://doi.org/10.1016/j.gsd.2023.101037
Nikita E. 2014. The use of generalized linear models and generalized estimating equations in bioarchaeological studies. American Journal of Physical Anthropology. 153(3):473-483. https://doi.org/10.1002/ajpa.22448
Park I, Choi J, Lee MJ, Lee S. 2012. Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping. Computers and Geosciences. 48: 228-238. DOI:10.1016/j.cageo.2012.01.005
Qiao X, Chu T, Krell E, Tissot P, Holland S, Ahmed M, Smilovsky D. 2024. Interpretation and attribution of coastal land subsidence: An InSAR and Machine Learning Perspective. Institute of Electrical and Electronics Engineers. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17: 4768-4783
https://doi.org/10.1109/JSTARS.2024.3361391
Rahmani P, Gholami H, Golzari S. 2024. An interpretable deep learning model to map land subsidence hazard. Environmental Science and Pollution Research. pp.1-13. https://doi.org/10.1007/s11356-024-32280-7
Sahu SR, Rawat KS. 2023. Analysis of land subsidencein coastal and urban areas by using various techniques-Literature Review. Indonesian Journal of Geography. 55(3): https://doi.org/10.22146/ijg.83675
Shirani K, Pasandi M, Ebrahimi B. 2021. Assesment of land subsidence in the Najafabad Plain of Isfahan using differential radar interferometry (DInSAR) technique. Journal of Water and Soil Science. 25 (1): 105-127.
https://doi.org/10.47176/jwss.25.1.147214
Su H, Xu T, Xion X, Tian A. 2024. Enhancement of land subsidence prediction capabilities using machine learning and SHAP value analysis with Sentinel-1 InSAR Data. Research Square. https://doi.org/10.21203/rs.3.rs-3926697/v1
Sun M, Du Y, Liu Q, Feng G, Peng X, Liao C. 2023. Understanding the Spatial-Temporal Characteristics of Land Subsidence in Shenzhen under Rapid Urbanization Based on MT-InSAR. Institute of Electrical and Electronics Engineers, Journal of Selected Topics in Applied Earth Observations and Remote Sensing.16:4153-4166. https://doi.org/10.1109/JSTARS.2023.3264652
Tzampoglou P, Loupasakis C. 2017. Updated ground water piezometry data of the Amyntaio Sub-Basin and their effect to the manifestation of the Land Subsidence Phenomena. 11th International Hydrogeological Congress of Greece, Athens, Greece. pp. 4-6.
Waltham AC. 1989. Ground subsidence. Chapman and Hall, New York. 202 p.
Xia R. 2009. Comparison of random forests and Cforest: variable importance measures and prediction accuracies. Utah State University, DigitalCommons@USU. https://doi.org/10.26076/74bd-59e1