308 related articles for article (PubMed ID: 34773156)
1. An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.
Moghadam SV; Sharafati A; Feizi H; Marjaie SMS; Asadollah SBHS; Motta D
Environ Monit Assess; 2021 Nov; 193(12):798. PubMed ID: 34773156
[TBL] [Abstract][Full Text] [Related]
2. Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China.
Ji X; Shang X; Dahlgren RA; Zhang M
Environ Sci Pollut Res Int; 2017 Jul; 24(19):16062-16076. PubMed ID: 28537025
[TBL] [Abstract][Full Text] [Related]
3. Performance assessment of artificial neural networks and support vector regression models for stream flow predictions.
Ateeq-Ur-Rauf ; Ghumman AR; Ahmad S; Hashmi HN
Environ Monit Assess; 2018 Nov; 190(12):704. PubMed ID: 30406854
[TBL] [Abstract][Full Text] [Related]
4. Predicting river dissolved oxygen time series based on stand-alone models and hybrid wavelet-based models.
Xu C; Chen X; Zhang L
J Environ Manage; 2021 Oct; 295():113085. PubMed ID: 34147993
[TBL] [Abstract][Full Text] [Related]
5. Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study.
Antanasijević D; Pocajt V; Povrenović D; Perić-Grujić A; Ristić M
Environ Sci Pollut Res Int; 2013 Dec; 20(12):9006-13. PubMed ID: 23764983
[TBL] [Abstract][Full Text] [Related]
6. Application of deep learning approaches to predict monthly stream flows.
Dalkilic HY; Kumar D; Samui P; Dixon B; Yesilyurt SN; Katipoğlu OM
Environ Monit Assess; 2023 May; 195(6):705. PubMed ID: 37212953
[TBL] [Abstract][Full Text] [Related]
7. Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia.
Wong YJ; Shimizu Y; Kamiya A; Maneechot L; Bharambe KP; Fong CS; Nik Sulaiman NM
Environ Monit Assess; 2021 Jun; 193(7):438. PubMed ID: 34159431
[TBL] [Abstract][Full Text] [Related]
8. Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.
Najafzadeh M; Ghaemi A
Environ Monit Assess; 2019 May; 191(6):380. PubMed ID: 31104155
[TBL] [Abstract][Full Text] [Related]
9. An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland.
Deo RC; Şahin M
Environ Monit Assess; 2016 Feb; 188(2):90. PubMed ID: 26780409
[TBL] [Abstract][Full Text] [Related]
10. Application of machine learning in river water quality management: a review.
Cojbasic S; Dmitrasinovic S; Kostic M; Turk Sekulic M; Radonic J; Dodig A; Stojkovic M
Water Sci Technol; 2023 Nov; 88(9):2297-2308. PubMed ID: 37966184
[TBL] [Abstract][Full Text] [Related]
11. Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea.
Kim S; Alizamir M; Zounemat-Kermani M; Kisi O; Singh VP
J Environ Manage; 2020 Sep; 270():110834. PubMed ID: 32507742
[TBL] [Abstract][Full Text] [Related]
12. Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.
Heddam S; Kisi O
Environ Sci Pollut Res Int; 2017 Jul; 24(20):16702-16724. PubMed ID: 28560629
[TBL] [Abstract][Full Text] [Related]
13. Estimation of daily dissolved oxygen concentration for river water quality using conventional regression analysis, multivariate adaptive regression splines, and TreeNet techniques.
Nacar S; Mete B; Bayram A
Environ Monit Assess; 2020 Nov; 192(12):752. PubMed ID: 33159587
[TBL] [Abstract][Full Text] [Related]
14. Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.
Kim S; Alizamir M; Seo Y; Heddam S; Chung IM; Kim YO; Kisi O; Singh VP
Math Biosci Eng; 2022 Sep; 19(12):12744-12773. PubMed ID: 36654020
[TBL] [Abstract][Full Text] [Related]
15. Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China.
Wen X; Fang J; Diao M; Zhang C
Environ Monit Assess; 2013 May; 185(5):4361-71. PubMed ID: 23001527
[TBL] [Abstract][Full Text] [Related]
16. Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA.
Heddam S
Environ Technol; 2014 Aug; 35(13-16):1650-7. PubMed ID: 24956755
[TBL] [Abstract][Full Text] [Related]
17. Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.
Najah A; El-Shafie A; Karim OA; El-Shafie AH
Environ Sci Pollut Res Int; 2014 Feb; 21(3):1658-1670. PubMed ID: 23949111
[TBL] [Abstract][Full Text] [Related]
18. Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA.
Heddam S
Environ Sci Pollut Res Int; 2014; 21(15):9212-27. PubMed ID: 24705953
[TBL] [Abstract][Full Text] [Related]
19. Application of ANN and SVM for prediction nutrients in rivers.
Stamenković LJ
J Environ Sci Health A Tox Hazard Subst Environ Eng; 2021; 56(8):867-873. PubMed ID: 34061713
[TBL] [Abstract][Full Text] [Related]
20. Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?
Liu M; Lu J
Environ Sci Pollut Res Int; 2014 Sep; 21(18):11036-53. PubMed ID: 24894753
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]