160 related articles for article (PubMed ID: 37517093)
1. Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computing.
Choubin B; Shirani K; Hosseini FS; Taheri J; Rahmati O
J Environ Manage; 2023 Nov; 345():118685. PubMed ID: 37517093
[TBL] [Abstract][Full Text] [Related]
2. Land subsidence susceptibility mapping using PWRSTFAL framework and analytic hierarchy process: fuzzy method (case study: Damaneh-Daran Plain in the west of Isfahan Province, Iran).
Chitsazan M; Rahmani G; Ghafoury H
Environ Monit Assess; 2022 Feb; 194(3):192. PubMed ID: 35169888
[TBL] [Abstract][Full Text] [Related]
3. Spatial modeling of land subsidence using machine learning models and statistical methods.
Sekkeravani MA; Bazrafshan O; Pourghasemi HR; Holisaz A
Environ Sci Pollut Res Int; 2022 Apr; 29(19):28866-28883. PubMed ID: 34993808
[TBL] [Abstract][Full Text] [Related]
4. Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique.
Hussain MA; Chen Z; Khan J
Environ Sci Pollut Res Int; 2024 Feb; 31(8):12271-12287. PubMed ID: 38231332
[TBL] [Abstract][Full Text] [Related]
5. Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms.
Zhao R; Arabameri A; Santosh M
Environ Sci Pollut Res Int; 2024 Feb; 31(10):15443-15466. PubMed ID: 38300491
[TBL] [Abstract][Full Text] [Related]
6. Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya, Turkey.
Orhan O
Environ Monit Assess; 2021 Mar; 193(4):174. PubMed ID: 33751245
[TBL] [Abstract][Full Text] [Related]
7. Modeling the two- and three-dimensional displacement field in Lorca, Spain, subsidence and the global implications.
Fernandez J; Prieto JF; Escayo J; Camacho AG; Luzón F; Tiampo KF; Palano M; Abajo T; Pérez E; Velasco J; Herrero T; Bru G; Molina I; López J; Rodríguez-Velasco G; Gómez I; Mallorquí JJ
Sci Rep; 2018 Oct; 8(1):14782. PubMed ID: 30283152
[TBL] [Abstract][Full Text] [Related]
8. Advancing remote sensing and machine learning-driven frameworks for groundwater withdrawal estimation in Arizona: Linking land subsidence to groundwater withdrawals.
Majumdar S; Smith R; Conway BD; Lakshmi V
Hydrol Process; 2022 Nov; 36(11):e14757. PubMed ID: 36636486
[TBL] [Abstract][Full Text] [Related]
9. Urban growth and land subsidence: Multi-decadal investigation using human settlement data and satellite InSAR in Morelia, Mexico.
Cigna F; Tapete D
Sci Total Environ; 2022 Mar; 811():152211. PubMed ID: 34890679
[TBL] [Abstract][Full Text] [Related]
10. Land subsidence and its relation with groundwater aquifers in Beijing Plain of China.
Chen B; Gong H; Chen Y; Li X; Zhou C; Lei K; Zhu L; Duan L; Zhao X
Sci Total Environ; 2020 Sep; 735():139111. PubMed ID: 32464408
[TBL] [Abstract][Full Text] [Related]
11. Earth fissure hazard prediction using machine learning models.
Choubin B; Mosavi A; Alamdarloo EH; Hosseini FS; Shamshirband S; Dashtekian K; Ghamisi P
Environ Res; 2019 Dec; 179(Pt A):108770. PubMed ID: 31577962
[TBL] [Abstract][Full Text] [Related]
12. Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR.
Li H; Zhu L; Dai Z; Gong H; Guo T; Guo G; Wang J; Teatini P
Sci Total Environ; 2021 Dec; 799():149244. PubMed ID: 34365261
[TBL] [Abstract][Full Text] [Related]
13. Calibration of a Land Subsidence Model Using InSAR Data via the Ensemble Kalman Filter.
Li L; Zhang M; Katzenstein K
Ground Water; 2017 Nov; 55(6):871-878. PubMed ID: 28542717
[TBL] [Abstract][Full Text] [Related]
14. Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities.
Rahmati O; Golkarian A; Biggs T; Keesstra S; Mohammadi F; Daliakopoulos IN
J Environ Manage; 2019 Apr; 236():466-480. PubMed ID: 30771667
[TBL] [Abstract][Full Text] [Related]
15. [Primary investigation of formation and genetic mechanism of land subsidence based on PS-InSAR technology in Beijing].
Lei KC; Chen BB; Jia SM; Wang SF; Luo Y
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Aug; 34(8):2185-9. PubMed ID: 25474959
[TBL] [Abstract][Full Text] [Related]
16. Deformation of the aquifer system under groundwater level fluctuations and its implication for land subsidence control in the Tianjin coastal region.
Yang J; Cao G; Han D; Yuan H; Hu Y; Shi P; Chen Y
Environ Monit Assess; 2019 Feb; 191(3):162. PubMed ID: 30771016
[TBL] [Abstract][Full Text] [Related]
17. Global land subsidence mapping reveals widespread loss of aquifer storage capacity.
Hasan MF; Smith R; Vajedian S; Pommerenke R; Majumdar S
Nat Commun; 2023 Oct; 14(1):6180. PubMed ID: 37794012
[TBL] [Abstract][Full Text] [Related]
18. Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence.
Mohammadifar A; Gholami H; Golzari S
Environ Sci Pollut Res Int; 2023 Feb; 30(10):26580-26595. PubMed ID: 36369445
[TBL] [Abstract][Full Text] [Related]
19. Detecting Land Subsidence in Shanghai by PS-Networking SAR Interferometry.
Liu G; Luo X; Chen Q; Huang D; Ding X
Sensors (Basel); 2008 Aug; 8(8):4725-4741. PubMed ID: 27873782
[TBL] [Abstract][Full Text] [Related]
20. Analysis of the influence of groundwater on land subsidence in Beijing based on the geographical weighted regression (GWR) model.
Yu H; Gong H; Chen B; Liu K; Gao M
Sci Total Environ; 2020 Oct; 738():139405. PubMed ID: 32535280
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]