Articles | Volume 21, issue 4
https://doi.org/10.5194/npg-21-777-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/npg-21-777-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
28 Jul 2014
Research article |
| 28 Jul 2014
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques
A. R. Ganguly
Northeastern University, Boston, MA, USA
E. A. Kodra
Northeastern University, Boston, MA, USA
A. Agrawal
Northwestern University, Evanston, IL, USA
A. Banerjee
University of Minnesota, Twin Cities, MN, USA
S. Boriah
University of Minnesota, Twin Cities, MN, USA
Sn. Chatterjee
University of Minnesota, Twin Cities, MN, USA
So. Chatterjee
University of Minnesota, Twin Cities, MN, USA
A. Choudhary
Northwestern University, Evanston, IL, USA
D. Das
Northeastern University, Boston, MA, USA
J. Faghmous
University of Minnesota, Twin Cities, MN, USA
P. Ganguli
Northeastern University, Boston, MA, USA
S. Ghosh
Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
K. Hayhoe
Texas Tech University, Lubbock, TX, USA
C. Hays
University of Nebraska, Lincoln, NE, USA
W. Hendrix
Northwestern University, Evanston, IL, USA
Q. Fu
University of Minnesota, Twin Cities, MN, USA
J. Kawale
University of Minnesota, Twin Cities, MN, USA
D. Kumar
Northeastern University, Boston, MA, USA
V. Kumar
University of Minnesota, Twin Cities, MN, USA
W. Liao
Northwestern University, Evanston, IL, USA
University of Minnesota, Twin Cities, MN, USA
R. Mawalagedara
Northeastern University, Boston, MA, USA
V. Mithal
University of Minnesota, Twin Cities, MN, USA
R. Oglesby
University of Nebraska, Lincoln, NE, USA
K. Salvi
Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
P. K. Snyder
University of Minnesota, Twin Cities, MN, USA
K. Steinhaeuser
University of Minnesota, Twin Cities, MN, USA
D. Wang
Northeastern University, Boston, MA, USA
D. Wuebbles
University of Illinois, Urbana-Champaign, IL, USA
Viewed
Total article views: 8,429 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Feb 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,563 | 3,561 | 305 | 8,429 | 242 | 266 |
- HTML: 4,563
- PDF: 3,561
- XML: 305
- Total: 8,429
- BibTeX: 242
- EndNote: 266
Total article views: 6,589 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Jul 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,456 | 2,867 | 266 | 6,589 | 216 | 255 |
- HTML: 3,456
- PDF: 2,867
- XML: 266
- Total: 6,589
- BibTeX: 216
- EndNote: 255
Total article views: 1,840 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Feb 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,107 | 694 | 39 | 1,840 | 26 | 11 |
- HTML: 1,107
- PDF: 694
- XML: 39
- Total: 1,840
- BibTeX: 26
- EndNote: 11
Cited
29 citations as recorded by crossref.
- Uniting remote sensing, crop modelling and economics for agricultural risk management E. Benami et al. 10.1038/s43017-020-00122-y
- Does statistical model perform at par with computationally expensive general circulation model for decadal prediction? R. Sahastrabuddhe & S. Ghosh 10.1088/1748-9326/abfeed
- Data Analysis-Oriented Stochastic Scheduling for Cost Efficient Resource Allocation in NFV Based MEC Network B. Li et al. 10.1109/TVT.2023.3234285
- Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling W. Tsai et al. 10.3389/frwa.2020.583000
- Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
- Review: visual analytics of climate networks T. Nocke et al. 10.5194/npg-22-545-2015
- SOSeas Web App: An assessment web-based decision support tool to predict dynamic risk of drowning on beaches using deep neural networks J. García-Alba et al. 10.1080/1755876X.2021.1999107
- 地球表层特征参量反演与模拟的机理<bold>-</bold>学习耦合范式 焕. 沈 & 良. 张 10.1360/SSTe-2022-0089
- Review of Statistical Analysis of Hydrologic Variables: Methods and Applications edited by Ramesh S. V. Teegavarapu, Jose D. Salas, and Jery R. StedingerASCE, Reston, VA; 2019; ISBN: 9780784415177; 556 pp.; $195.00. C. Prakash Khedun & V. Singh 10.1061/(ASCE)HE.1943-5584.0002082
- Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes P. Bhasme et al. 10.1016/j.jhydrol.2022.128618
- Intercomparison of model response and internal variability across climate model ensembles D. Kumar & A. Ganguly 10.1007/s00382-017-3914-4
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data A. Karpatne et al. 10.1109/TKDE.2017.2720168
- Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al. 10.3390/w11091808
- Evaluating wind extremes in CMIP5 climate models D. Kumar et al. 10.1007/s00382-014-2306-2
- Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa S. Kusangaya et al. 10.1016/j.jhydrol.2018.01.017
- Forecast of Convective Events Via Hybrid Model: Wrf and Machine Learning Algorithms Y. da Silva et al. 10.2139/ssrn.4113508
- Machine learning in agricultural and applied economics H. Storm et al. 10.1093/erae/jbz033
- Climate Adaptation Informatics: Water Stress on Power Production A. Ganguly et al. 10.1109/MCSE.2015.106
- Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science S. Kawamleh 10.1086/714877
- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard et al. 10.1145/3514228
- Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin Y. Tian et al. 10.1016/j.scitotenv.2017.12.025
- Edge Detection Reveals Abrupt and Extreme Climate Events S. Bathiany et al. 10.1175/JCLI-D-19-0449.1
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists C. Shen 10.1029/2018WR022643
- Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
- Forecast of convective events via hybrid model: WRF and machine learning algorithms Y. Uchôa da Silva et al. 10.1016/j.acags.2022.100099
- Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science A. Agrawal & A. Choudhary 10.1063/1.4946894
- Forecasting and uncertainty: A survey S. Makridakis & N. Bakas 10.3233/RDA-150114
- Deep materials informatics: Applications of deep learning in materials science A. Agrawal & A. Choudhary 10.1557/mrc.2019.73
- A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science J. Faghmous & V. Kumar 10.1089/big.2014.0026
28 citations as recorded by crossref.
- Uniting remote sensing, crop modelling and economics for agricultural risk management E. Benami et al. 10.1038/s43017-020-00122-y
- Does statistical model perform at par with computationally expensive general circulation model for decadal prediction? R. Sahastrabuddhe & S. Ghosh 10.1088/1748-9326/abfeed
- Data Analysis-Oriented Stochastic Scheduling for Cost Efficient Resource Allocation in NFV Based MEC Network B. Li et al. 10.1109/TVT.2023.3234285
- Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling W. Tsai et al. 10.3389/frwa.2020.583000
- Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
- Review: visual analytics of climate networks T. Nocke et al. 10.5194/npg-22-545-2015
- SOSeas Web App: An assessment web-based decision support tool to predict dynamic risk of drowning on beaches using deep neural networks J. García-Alba et al. 10.1080/1755876X.2021.1999107
- 地球表层特征参量反演与模拟的机理<bold>-</bold>学习耦合范式 焕. 沈 & 良. 张 10.1360/SSTe-2022-0089
- Review of Statistical Analysis of Hydrologic Variables: Methods and Applications edited by Ramesh S. V. Teegavarapu, Jose D. Salas, and Jery R. StedingerASCE, Reston, VA; 2019; ISBN: 9780784415177; 556 pp.; $195.00. C. Prakash Khedun & V. Singh 10.1061/(ASCE)HE.1943-5584.0002082
- Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes P. Bhasme et al. 10.1016/j.jhydrol.2022.128618
- Intercomparison of model response and internal variability across climate model ensembles D. Kumar & A. Ganguly 10.1007/s00382-017-3914-4
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data A. Karpatne et al. 10.1109/TKDE.2017.2720168
- Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al. 10.3390/w11091808
- Evaluating wind extremes in CMIP5 climate models D. Kumar et al. 10.1007/s00382-014-2306-2
- Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa S. Kusangaya et al. 10.1016/j.jhydrol.2018.01.017
- Forecast of Convective Events Via Hybrid Model: Wrf and Machine Learning Algorithms Y. da Silva et al. 10.2139/ssrn.4113508
- Machine learning in agricultural and applied economics H. Storm et al. 10.1093/erae/jbz033
- Climate Adaptation Informatics: Water Stress on Power Production A. Ganguly et al. 10.1109/MCSE.2015.106
- Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science S. Kawamleh 10.1086/714877
- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard et al. 10.1145/3514228
- Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin Y. Tian et al. 10.1016/j.scitotenv.2017.12.025
- Edge Detection Reveals Abrupt and Extreme Climate Events S. Bathiany et al. 10.1175/JCLI-D-19-0449.1
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists C. Shen 10.1029/2018WR022643
- Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
- Forecast of convective events via hybrid model: WRF and machine learning algorithms Y. Uchôa da Silva et al. 10.1016/j.acags.2022.100099
- Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science A. Agrawal & A. Choudhary 10.1063/1.4946894
- Forecasting and uncertainty: A survey S. Makridakis & N. Bakas 10.3233/RDA-150114
- Deep materials informatics: Applications of deep learning in materials science A. Agrawal & A. Choudhary 10.1557/mrc.2019.73
1 citations as recorded by crossref.
Saved (final revised paper)
Saved (final revised paper)
Discussed (final revised paper)
Latest update: 29 Jun 2024
