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.
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
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34 citations as recorded by crossref.
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34 citations as recorded by crossref.
- Urgency and importance of local-scale modeling tools to support climate adaptation and sustainable development H. Ouyang et al.
- Uniting remote sensing, crop modelling and economics for agricultural risk management E. Benami et al.
- Does statistical model perform at par with computationally expensive general circulation model for decadal prediction? R. Sahastrabuddhe & S. Ghosh
- Data Analysis-Oriented Stochastic Scheduling for Cost Efficient Resource Allocation in NFV Based MEC Network B. Li et al.
- Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling W. Tsai et al.
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- Review: visual analytics of climate networks T. Nocke et al.
- 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.
- 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
- 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
- Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes P. Bhasme et al.
- Intercomparison of model response and internal variability across climate model ensembles D. Kumar & A. Ganguly
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data A. Karpatne et al.
- Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al.
- Evaluating wind extremes in CMIP5 climate models D. Kumar et al.
- Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa S. Kusangaya et al.
- A framework of coupling split-window and machine learning (SW-ML) for land surface temperature retrieval from MODIS thermal infrared data Y. Guan et al.
- Forecast of Convective Events Via Hybrid Model: Wrf and Machine Learning Algorithms Y. da Silva et al.
- Machine learning in agricultural and applied economics H. Storm et al.
- Climate Adaptation Informatics: Water Stress on Power Production A. Ganguly et al.
- Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science S. Kawamleh
- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard et al.
- Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin Y. Tian et al.
- The Usefulness of Climate Modelling for Humanitarian Aid Resource Allocation: An Exploratory Literature Review J. Jäpölä & S. Van Passel
- Edge Detection Reveals Abrupt and Extreme Climate Events S. Bathiany et al.
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists C. Shen
- Evolution of soil moisture mapping from statistical models to integrated mechanistic and geoscience-aware approaches M. Zhang et al.
- Predicting Heavy Precipitation in Southwest Iran: A Machine Learning Classification Approach with Atmospheric Precursors and Feature Optimization K. Shahgholian et al.
- Coupled SWAT, Stationary Wavelet Transform, and Interpretable Machine Learning to Improve Watershed Streamflow Simulation C. Ren et al.
- Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang
- Forecast of convective events via hybrid model: WRF and machine learning algorithms Y. Uchôa da Silva et al.
- Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science A. Agrawal & A. Choudhary
- Forecasting and uncertainty: A survey S. Makridakis & N. Bakas
- Deep materials informatics: Applications of deep learning in materials science A. Agrawal & A. Choudhary
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