A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution-Reference-Cited by-同舟云学术

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A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution

Published:2021-01 Issue: Volume:147 Page:103799
ISSN:0309-1708
Container-title:Advances in Water Resources
language:en
Short-container-title:Advances in Water Resources

Author:

Zeng Xiang,Schnier Spencer,Cai Ximing

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference91 articles.

1. Frequent Pattern Mining;Aggarwal,2014

2. Mining association between sets of items in massive database;Agrawal,1993

3. Fast algorithms for mining association rules;Agrawal,1994

4. Streamflow response to potential land use and climate changes in the James River watershed, Upper Midwest United States;Ahiablame;J. Hydrol,2017

5. Quantifying the relative impact of climate and human activities on streamflow;Ahn;J. Hydrol.,2014

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2. Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study;Neural Computing and Applications;2022-12-27

3. On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran;Arabian Journal of Geosciences;2022-12-24

4. Correlation between climate and flood indices in Northwestern Italy at different temporal scales;Journal of Hydrology and Hydromechanics;2022-05-19

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