155 related articles for article (PubMed ID: 35589754)
1. Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data.
Nielsen AH; Iosifidis A; Karstoft H
Sci Rep; 2022 May; 12(1):8395. PubMed ID: 35589754
[TBL] [Abstract][Full Text] [Related]
2. Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne
Zewdie GK; Lary DJ; Levetin E; Garuma GF
Int J Environ Res Public Health; 2019 Jun; 16(11):. PubMed ID: 31167504
[TBL] [Abstract][Full Text] [Related]
3. Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.
Zhao N; Charland K; Carabali M; Nsoesie EO; Maheu-Giroux M; Rees E; Yuan M; Garcia Balaguera C; Jaramillo Ramirez G; Zinszer K
PLoS Negl Trop Dis; 2020 Sep; 14(9):e0008056. PubMed ID: 32970674
[TBL] [Abstract][Full Text] [Related]
4. Application of Offshore Visibility Forecast Based on Temporal Convolutional Network and Transfer Learning.
Lu Z; Zheng C; Yang T
Comput Intell Neurosci; 2020; 2020():8882279. PubMed ID: 33133176
[TBL] [Abstract][Full Text] [Related]
5. A Graph Neural Network with Spatio-Temporal Attention for Multi-Sources Time Series Data: An Application to Frost Forecast.
Lira H; Martí L; Sanchez-Pi N
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214389
[TBL] [Abstract][Full Text] [Related]
6. Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study.
Sudarshan VK; Brabrand M; Range TM; Wiil UK
Comput Biol Med; 2021 Aug; 135():104541. PubMed ID: 34166880
[TBL] [Abstract][Full Text] [Related]
7. Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.
Kashyap GR; Sridhara S; Manoj KN; Gopakkali P; Das B; Jha PK; Prasad PVV
Int J Biometeorol; 2024 Jun; 68(6):1179-1197. PubMed ID: 38676745
[TBL] [Abstract][Full Text] [Related]
8. Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources.
Eide SS; Riegler MA; Hammer HL; Bremnes JB
Sensors (Basel); 2022 Apr; 22(7):. PubMed ID: 35408416
[TBL] [Abstract][Full Text] [Related]
9. Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.
Zewdie GK; Lary DJ; Liu X; Wu D; Levetin E
Environ Monit Assess; 2019 Jun; 191(7):418. PubMed ID: 31175476
[TBL] [Abstract][Full Text] [Related]
10. Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods.
Braz FJ; Ferreira J; Gonçalves F; Weege K; Almeida J; Baldo F; Gonçalves P
Sensors (Basel); 2022 Jun; 22(12):. PubMed ID: 35746265
[TBL] [Abstract][Full Text] [Related]
11. Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study.
Elamrani Abou Elassad Z; Mousannif H; Al Moatassime H
Traffic Inj Prev; 2020; 21(3):201-208. PubMed ID: 32125890
[No Abstract] [Full Text] [Related]
12. Weather forecasting based on data-driven and physics-informed reservoir computing models.
Mammedov YD; Olugu EU; Farah GA
Environ Sci Pollut Res Int; 2022 Apr; 29(16):24131-24144. PubMed ID: 34825327
[TBL] [Abstract][Full Text] [Related]
13. Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning.
Chattopadhyay A; Nabizadeh E; Hassanzadeh P
J Adv Model Earth Syst; 2020 Feb; 12(2):e2019MS001958. PubMed ID: 32714491
[TBL] [Abstract][Full Text] [Related]
14. Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.
Srivastava AK; Safaei N; Khaki S; Lopez G; Zeng W; Ewert F; Gaiser T; Rahimi J
Sci Rep; 2022 Feb; 12(1):3215. PubMed ID: 35217689
[TBL] [Abstract][Full Text] [Related]
15. Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.
Zewdie GK; Liu X; Wu D; Lary DJ; Levetin E
Environ Monit Assess; 2019 Jun; 191(Suppl 2):261. PubMed ID: 31254085
[TBL] [Abstract][Full Text] [Related]
16. Revealing recurrent regimes of mid-latitude atmospheric variability using novel machine learning method.
Mukhin D; Hannachi A; Braun T; Marwan N
Chaos; 2022 Nov; 32(11):113105. PubMed ID: 36456324
[TBL] [Abstract][Full Text] [Related]
17. Comparative optimization of global solar radiation forecasting using machine learning and time series models.
Belmahdi B; Louzazni M; El Bouardi A
Environ Sci Pollut Res Int; 2022 Feb; 29(10):14871-14888. PubMed ID: 34625894
[TBL] [Abstract][Full Text] [Related]
18. Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data.
Chattopadhyay A; Hassanzadeh P; Pasha S
Sci Rep; 2020 Jan; 10(1):1317. PubMed ID: 31992743
[TBL] [Abstract][Full Text] [Related]
19. A generative adversarial network approach to (ensemble) weather prediction.
Bihlo A
Neural Netw; 2021 Jul; 139():1-16. PubMed ID: 33662648
[TBL] [Abstract][Full Text] [Related]
20. Deep learning for twelve hour precipitation forecasts.
Espeholt L; Agrawal S; Sønderby C; Kumar M; Heek J; Bromberg C; Gazen C; Carver R; Andrychowicz M; Hickey J; Bell A; Kalchbrenner N
Nat Commun; 2022 Sep; 13(1):5145. PubMed ID: 36050311
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]