243 related articles for article (PubMed ID: 36715809)
1. Evaluation of machine learning algorithms for groundwater quality modeling.
Sahour S; Khanbeyki M; Gholami V; Sahour H; Kahvazade I; Karimi H
Environ Sci Pollut Res Int; 2023 Apr; 30(16):46004-46021. PubMed ID: 36715809
[TBL] [Abstract][Full Text] [Related]
2. Probability mapping of groundwater contamination by hydrocarbon from the deep oil reservoirs using GIS-based machine-learning algorithms: a case study of the Dammam aquifer (middle of Iraq).
Al-Mayahi HM; Al-Abadi AM; Fryar AE
Environ Sci Pollut Res Int; 2021 Mar; 28(11):13736-13751. PubMed ID: 33196994
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan.
Rasool U; Yin X; Xu Z; Rasool MA; Senapathi V; Hussain M; Siddique J; Trabucco JC
Chemosphere; 2022 Sep; 303(Pt 3):135265. PubMed ID: 35691394
[TBL] [Abstract][Full Text] [Related]
5. Prediction of groundwater drawdown using artificial neural networks.
Gholami V; Sahour H
Environ Sci Pollut Res Int; 2022 May; 29(22):33544-33557. PubMed ID: 35031998
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Groundwater potential assessment using GIS-based ensemble learning models in Guanzhong Basin, China.
Wang Z; Wang J; Yu D; Chen K
Environ Monit Assess; 2023 May; 195(6):690. PubMed ID: 37199816
[TBL] [Abstract][Full Text] [Related]
8. Comparative evaluation of machine learning models for groundwater quality assessment.
Bedi S; Samal A; Ray C; Snow D
Environ Monit Assess; 2020 Nov; 192(12):776. PubMed ID: 33219864
[TBL] [Abstract][Full Text] [Related]
9. The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach.
Sumdang N; Chotpantarat S; Cho KH; Thanh NN
Ecotoxicol Environ Saf; 2023 Mar; 253():114665. PubMed ID: 36863158
[TBL] [Abstract][Full Text] [Related]
10. Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand.
Thanh NN; Chotpantarat S; Ngu NH; Thunyawatcharakul P; Kaewdum N
Environ Res; 2024 Jul; 252(Pt 2):118952. PubMed ID: 38636644
[TBL] [Abstract][Full Text] [Related]
11. Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model.
Mohseni U; Pande CB; Chandra Pal S; Alshehri F
Chemosphere; 2024 Mar; 352():141393. PubMed ID: 38325619
[TBL] [Abstract][Full Text] [Related]
12. Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.
Golkarian A; Naghibi SA; Kalantar B; Pradhan B
Environ Monit Assess; 2018 Feb; 190(3):149. PubMed ID: 29455381
[TBL] [Abstract][Full Text] [Related]
13. Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential.
Naghibi SA; Dolatkordestani M; Rezaei A; Amouzegari P; Heravi MT; Kalantar B; Pradhan B
Environ Monit Assess; 2019 Mar; 191(4):248. PubMed ID: 30919064
[TBL] [Abstract][Full Text] [Related]
14. Groundwater quality assessment by multi-model comparison: a comprehensive study during dry and wet periods in semi-arid regions.
Wang Z; Wang Y
Environ Sci Pollut Res Int; 2023 Apr; 30(18):51571-51594. PubMed ID: 36810824
[TBL] [Abstract][Full Text] [Related]
15. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.
Wu Z; Zhu M; Kang Y; Leung EL; Lei T; Shen C; Jiang D; Wang Z; Cao D; Hou T
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313673
[TBL] [Abstract][Full Text] [Related]
16. Using machine learning algorithms to map the groundwater recharge potential zones.
Pourghasemi HR; Sadhasivam N; Yousefi S; Tavangar S; Ghaffari Nazarlou H; Santosh M
J Environ Manage; 2020 Jul; 265():110525. PubMed ID: 32275245
[TBL] [Abstract][Full Text] [Related]
17. Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods.
Taşan M; Taşan S; Demir Y
Environ Sci Pollut Res Int; 2023 Jan; 30(2):2866-2890. PubMed ID: 35941499
[TBL] [Abstract][Full Text] [Related]
18. A multi-step approach to evaluate the sustainable use of groundwater resources for human consumption and agriculture.
Bordbar M; Busico G; Sirna M; Tedesco D; Mastrocicco M
J Environ Manage; 2023 Dec; 347():119041. PubMed ID: 37783086
[TBL] [Abstract][Full Text] [Related]
19. Groundwater quality modeling and determining critical points: a comparison of machine learning to Best-Worst Method.
Nasiri Khiavi A; Mostafazadeh R; Adhami M
Environ Sci Pollut Res Int; 2023 Nov; 30(54):115758-115775. PubMed ID: 37889408
[TBL] [Abstract][Full Text] [Related]
20. Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality.
Masood A; Aslam M; Pham QB; Khan W; Masood S
Environ Sci Pollut Res Int; 2022 Apr; 29(18):26860-26876. PubMed ID: 34860346
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]