131 related articles for article (PubMed ID: 30065216)
1. Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms.
Tien Bui D; Shahabi H; Shirzadi A; Chapi K; Pradhan B; Chen W; Khosravi K; Panahi M; Bin Ahmad B; Saro L
Sensors (Basel); 2018 Jul; 18(8):. PubMed ID: 30065216
[TBL] [Abstract][Full Text] [Related]
2. 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]
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. Land subsidence modelling using tree-based machine learning algorithms.
Rahmati O; Falah F; Naghibi SA; Biggs T; Soltani M; Deo RC; Cerdà A; Mohammadi F; Tien Bui D
Sci Total Environ; 2019 Jul; 672():239-252. PubMed ID: 30959291
[TBL] [Abstract][Full Text] [Related]
5. Spatial prediction of ground subsidence susceptibility using an artificial neural network.
Lee S; Park I; Choi JK
Environ Manage; 2012 Feb; 49(2):347-58. PubMed ID: 22005969
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models.
Zhang L; Arabameri A; Santosh M; Pal SC
Environ Sci Pollut Res Int; 2023 Jul; 30(31):77830-77849. PubMed ID: 37266775
[TBL] [Abstract][Full Text] [Related]
8. Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.
Lee S; Park I
J Environ Manage; 2013 Sep; 127():166-76. PubMed ID: 23702378
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms.
Gayen A; Pourghasemi HR; Saha S; Keesstra S; Bai S
Sci Total Environ; 2019 Jun; 668():124-138. PubMed ID: 30851678
[TBL] [Abstract][Full Text] [Related]
11. Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches.
Widya LK; Rezaie F; Lee W; Lee CW; Nurwatik N; Lee S
J Environ Manage; 2024 Jul; 364():121291. PubMed ID: 38875975
[TBL] [Abstract][Full Text] [Related]
12. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China.
Chen W; Peng J; Hong H; Shahabi H; Pradhan B; Liu J; Zhu AX; Pei X; Duan Z
Sci Total Environ; 2018 Jun; 626():1121-1135. PubMed ID: 29898519
[TBL] [Abstract][Full Text] [Related]
13. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.
Naghibi SA; Pourghasemi HR; Dixon B
Environ Monit Assess; 2016 Jan; 188(1):44. PubMed ID: 26687087
[TBL] [Abstract][Full Text] [Related]
14. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India.
Arora A; Arabameri A; Pandey M; Siddiqui MA; Shukla UK; Bui DT; Mishra VN; Bhardwaj A
Sci Total Environ; 2021 Jan; 750():141565. PubMed ID: 32882492
[TBL] [Abstract][Full Text] [Related]
15. Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS.
Lee S
Environ Manage; 2004 Aug; 34(2):223-32. PubMed ID: 15559946
[TBL] [Abstract][Full Text] [Related]
16. Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling.
Pourghasemi HR; Gayen A; Lasaponara R; Tiefenbacher JP
Environ Res; 2020 May; 184():109321. PubMed ID: 32199317
[TBL] [Abstract][Full Text] [Related]
17. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping.
Shirzadi A; Soliamani K; Habibnejhad M; Kavian A; Chapi K; Shahabi H; Chen W; Khosravi K; Thai Pham B; Pradhan B; Ahmad A; Bin Ahmad B; Tien Bui D
Sensors (Basel); 2018 Nov; 18(11):. PubMed ID: 30400627
[TBL] [Abstract][Full Text] [Related]
18. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran).
Tien Bui D; Shirzadi A; Shahabi H; Chapi K; Omidavr E; Pham BT; Talebpour Asl D; Khaledian H; Pradhan B; Panahi M; Bin Ahmad B; Rahmani H; Gróf G; Lee S
Sensors (Basel); 2019 May; 19(11):. PubMed ID: 31146336
[TBL] [Abstract][Full Text] [Related]
19. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment.
Nhu VH; Mohammadi A; Shahabi H; Ahmad BB; Al-Ansari N; Shirzadi A; Clague JJ; Jaafari A; Chen W; Nguyen H
Int J Environ Res Public Health; 2020 Jul; 17(14):. PubMed ID: 32650595
[TBL] [Abstract][Full Text] [Related]
20. A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.
Ghasemian B; Shahabi H; Shirzadi A; Al-Ansari N; Jaafari A; Kress VR; Geertsema M; Renoud S; Ahmad A
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214473
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
