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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2018-07-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s13201-018-0742-6 |