Short-term prediction of groundwater level using improved random forest regression with a combination of random features

Short-term prediction of groundwater level using improved random forest regression with a combination of random features

Abstract To solve the problem where by the available on-site input data are too scarce to predict the level of groundwater, this paper proposes an algorithm to make this prediction called the canonical correlation forest algorithm with a combination of random features. To assess the effectiveness of...

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Bibliographic Details
Main Authors: Xuanhui Wang, Tailian Liu, Xilai Zheng, Hui Peng, Jia Xin, Bo Zhang
Format: Article
Language:English
Published: SpringerOpen 2018-07-01
Series:Applied Water Science
Subjects:
Groundwater level prediction
Improved random forest regression
Least squares support vector regression
Daguhe River groundwater source field
Online Access:http://link.springer.com/article/10.1007/s13201-018-0742-6

Internet

http://link.springer.com/article/10.1007/s13201-018-0742-6

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