Causal Discovery for Feature Selection in Physical Process-Based Hydrological Systems-Reference-Cited by-同舟云学术

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Causal Discovery for Feature Selection in Physical Process-Based Hydrological Systems

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
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Author:

Sheth Paras¹,Liu Ting²,Doner Durmus¹,Deng Qi³,Wei Yuhang⁴,Muenich Rebecca²,Sabo John³,Candan K. Selcuk¹,Liu Huan¹

Affiliation:

1. Arizona State University,School of Computing and Augmented Intelligence

2. Arizona State University,School of Sustainable Engineering and the Built Environment

3. Tulane University,ByWater Institute,Department of River-Coastal Science and Engineering

4. Arizona State University,Future H2O, Julie Ann Wrigley Global Futures Laboratory

Funder

Arm

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020794.pdf?arnumber=10020794

Reference61 articles.

1. D’ya like dags? a survey on structure learning and causal discovery;vowels,2021

2. K*: An Instance-based Learner Using an Entropic Distance Measure

3. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

4. Transferable time-series forecasting under causal conditional shift;li,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning;ACM Transactions on Spatial Algorithms and Systems;2024-06-30

2. STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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