Theory-Guided Data Science for Climate Change

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

Faghmous, J. H., & Kumar, V. (2014) · Big Data · doi:10.1089/big.2014.0026 · Cited by 223 · see citing works

Statistical significance of climate sensitivity predictors obtained by data mining

Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B. D., & Sanderson, B. M. (2014) · Geophysical Research Letters · doi:10.1002/2014gl059205 · Cited by 153 · see citing works

Sparse Group Lasso: Consistency and Climate Applications

Chatterjee, S., Steinhaeuser, K., Banerjee, A., Chatterjee, S., & Ganguly, A. (2012) · Proceedings of the 2012 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972825.5 · Cited by 72 · see citing works

Spatio-Temporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field

Faghmous, J., Nguyen, H., Le, M., & Kumar, V. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8771 · Cited by 7 · see citing works

Digital twin: values, challenges and enablers from a modeling perspective

Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752 · see citing works

Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives

Wu, J., Guo, S., Huang, H., Liu, W., & Xiang, Y. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2812301 · Cited by 614 · see citing works

Machine learning in agricultural and applied economics

Storm, H., Baylis, K., & Heckelei, T. (2019) · European Review of Agricultural Economics · doi:10.1093/erae/jbz033 · Cited by 227 · see citing works

Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes

Bhasme, P., Vagadiya, J., & Bhatia, U. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128618 · Cited by 171 · see citing works

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Chadalawada, J., Herath, H. M. V. V., & Babovic, V. (2020) · Water Resources Research · doi:10.1029/2019wr026933 · Cited by 170 · see citing works

Digital Twin: Values, Challenges and Enablers

Rasheed, A., San, O., & Kvamsdal, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01719 · Cited by 81 · see citing works

The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

Harfouche, A., Quinio, B., Saba, M., & Saba, P. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10352-8 · Cited by 55 · see citing works

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

Pawar, S. A., & San, O. (2021) · Physical Review Fluids · doi:10.1103/physrevfluids.6.050501 · Cited by 51 · see citing works

Bridging sustainability science, earth science, and data science through interdisciplinary education

Pennington, D. D., Ebert‐Uphoff, I., Freed, N., Martin, J. E., & Pierce, S. A. (2019) · Sustainability Science · doi:10.1007/s11625-019-00735-3 · Cited by 45 · see citing works

Human-Centric AI to Mitigate AI Biases

Harfouche, A., Quinio, B., & Bugiotti, F. (2023) · Journal of Global Information Management · doi:10.4018/jgim.331755 · Cited by 42 · see citing works

Time‐critical data management in clouds: Challenges and a Dynamic Real‐Time Infrastructure Planner (DRIP) solution

Koulouzis, S., Martin, P., Zhou, H., Hu, Y., Wang, J., Carval, T., Grenier, B., Heikkinen, J., de Laat, C. T. A. M., & Zhao, Z. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5269 · Cited by 40 · see citing works

Predicting particle trajectories in oceanic flows using artificial neural networks

Grossi, M. D., Kubát, M., & Özgökmen, T. M. (2020) · Ocean Modelling · doi:10.1016/j.ocemod.2020.101707 · Cited by 27 · see citing works

Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling

Chen, S., Feng, Y., Li, H., Ma, D., Mao, Q., Zhao, Y., & Liu, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-77678-y · Cited by 23 · see citing works

SDN-aware federation of distributed data

Koulouzis, S., Belloum, A. S. Z., Bubak, M., Zhao, Z., Živković, M., & de Laat, C. T. A. M. (2015) · Future Generation Computer Systems · doi:10.1016/j.future.2015.09.032 · Cited by 22 · see citing works

An Evolve-Then-Correct Reduced Order Model for Hidden Fluid Dynamics

Pawar, S. A., Ahmed, S. E., San, O., & Rasheed, A. (2020) · Mathematics · doi:10.3390/math8040570 · Cited by 23 · see citing works

Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

Das, M., & Ghosh, S. K. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9349-0 · Cited by 19 · see citing works

Physik‐informierte Künstliche Intelligenz zur Berechnung und Bemessung im Stahlbau

Kraus, M. A., & Taras, A. (2020) · Stahlbau · doi:10.1002/stab.202000074 · Cited by 15 · see citing works

Modeling of Seismic Energy Dissipation of Rocking Foundations Using Nonparametric Machine Learning Algorithms

Gajan, S. (2021) · Geotechnics · doi:10.3390/geotechnics1020024 · Cited by 13 · see citing works

Machine learning–based approach to predict ice meltdown in glaciers due to climate change and solutions

Pant, P., Rajawat, A. S., Goyal, S. B., Chakrabarti, P., Bedi, P., & Salau, A. O. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-28466-0 · Cited by 9 · see citing works

Accelerating Science: A Computing Research AgendaPreprint

Honavar, V., Hill, M. D., & Yelick, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.02006 · Cited by 11 · see citing works

Information and communications technology for a sustainable world [Climate Change]

Routray, S. K., & Mohanty, S. (2024) · IEEE Potentials · doi:10.1109/mpot.2024.3404114 · Cited by 7 · see citing works

Climate patterns of political division units obtained using automatic classification trees

Coria, S. R., Gay-García, C., Villers-Ruı́z, L., Arenas, A. G., Sánchez-Meneses, Ó., Ávila-Barrón, O. R., Pérez-Meza, M., Cruz-Núñez, X., & Luna, G. L. M. (2016) · Atmósfera · doi:10.20937/atm.2016.29.04.06 · Cited by 7 · see citing works

Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flowsPreprint

Pawar, S. A., & San, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08901 · Cited by 7 · see citing works

Surface drifter trajectory prediction in the Gulf of Mexico using neural networks

Grossi, M. D., Jegelka, S., Lermusiaux, P. F. J., & Özgökmen, T. M. (2025) · Ocean Modelling · doi:10.1016/j.ocemod.2025.102543 · Cited by 6 · see citing works

Integrating sustainability into STEM curricula

Routray, S. K., & Mohanty, S. (2024) · IEEE Potentials · doi:10.1109/mpot.2024.3461233 · Cited by 6 · see citing works

Machine learning in agricultural economics

Baylis, K., Heckelei, T., & Storm, H. (2021) · Handbook of agricultural economics · doi:10.1016/bs.hesagr.2021.10.007 · Cited by 7 · see citing works

An Insight into Theory-Guided Climate Data Science—A Literature Review

Sheikh, R., & Jahirabadkar, S. A. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-10-8360-0_11 · Cited by 4 · see citing works

Efficient computations for linear feedback control problems for target velocity matching of Navier-Stokes flows via POD and LSTM-ROM

Lee, H. (2019) · Electronic Research Archive · doi:10.3934/era.2020128 · Cited by 3 · see citing works

Applications of Data Science and Artificial Intelligence in Public Policy

Goyal, H., & Shekhawat, S. (2023) · AIMS International Journal of Management · doi:10.26573/2021.16.3.3 · Cited by 3 · see citing works

Work-in-Progress: Data Science Framework for Environmental Protection Education

Sokač, M., Pufek, P., Milardic, M., Mihaljević, B., Puškarić, S., & Žagar, M. (2021) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon46332.2021.9453975 · Cited by 2 · see citing works

Role of the water balance constraint in the long short-term memory network: large-sample tests of rainfall-runoff predictionPreprint

Li, Q., & Zhao, T. (2024) · doi:10.5194/egusphere-2023-2841 · Cited by 2 · see citing works

Symbolic Analysis of Machine Behaviour and the Emergence of the Machine Language

Ritt, R., & O'Leary, P. L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04070-3_24 · Cited by 1 · see citing works

Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE)

Ganguly, A. R., Haupt, S. E., Hoffman, F. M., Kumar, V., Lall, U., Monteleoni, C., Jitendra Kumar, A., Singh, N., Hopkins, J., Karpatne, A., et al. (2021) · doi:10.2172/1769776 · Cited by 1 · see citing works

The Role of Climate Datasets in Understanding Climate ExtremesPreprint

Eresanya, E. O., Orimoloye, I. R., Durowoju, O. S., Daramola, M. T., Olowe, J. P., Folorunsho, A. H., & Olorunsaye, O. (2021) · Preprints.org · doi:10.20944/preprints202105.0139.v1

Real-Time Computing for a Parameterized Feedback Control Problem of Boussinesq Equations by POD and Deep Learning

Piao, G.-R., & Lee, H. (2023) · East Asian Journal on Applied Mathematics · doi:10.4208/eajam.2023-094.250623

Comment on egusphere-2023-2841

(2024) · doi:10.5194/egusphere-2023-2841-rc1

Comment on egusphere-2023-2841

(2024) · doi:10.5194/egusphere-2023-2841-rc2

Reply on RC1

Zhao, T. (2024) · doi:10.5194/egusphere-2023-2841-ac1

Reply on RC2

Zhao, T. (2024) · doi:10.5194/egusphere-2023-2841-ac2

Robustness of the long short-term memory network in rainfall-runoff prediction improved by the water balance constraintPreprint

Li, Q., & Zhao, T. (2024) · doi:10.5194/egusphere-2024-1449

Comment on egusphere-2024-1449

(2024) · doi:10.5194/egusphere-2024-1449-rc2

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

Faghmous, J. H., & Kumar, V. (2014) · Big Data · doi:10.1089/big.2014.0026 · Cited by 223 · see citing works

Data Science in Science: Special Issue on Data Science in Environmental and Climate Sciences

Friedrich, M., Mahieu, E., Smeekes, S., Raymaekers, J., Wilms, I., & Matteson, D. S. (2022) · Data Science in Science · doi:10.1080/26941899.2022.2081002

Spatiotemporal data science: theoretical advances and applications

Amato, F., Lombardo, L., Tonini, M., Marvuglia, A., Castro‐Camilo, D., & Guignard, F. (2022) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-022-02281-4 · Cited by 4 · see citing works

Statistical science: contributions to the Administration's research priority on climate change

Sansó, B., Berliner, L. M., Cooley, D., Craigmile, P. F., Cressie, N., Haran, M., Lund, R., Nychka, D. W., Paciorek, C. J., Sain, S. R., et al. (2014) · Research Online (University of Wollongong) · Cited by 1 · see citing works

An Insight into Theory-Guided Climate Data Science—A Literature Review

Sheikh, R., & Jahirabadkar, S. A. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-10-8360-0_11 · Cited by 4 · see citing works