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Computational Intelligence and Neuroscience
Volume 2017, Article ID 8734214, 11 pages
Research Article

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan

Correspondence should be addressed to Ching-Hsue Cheng; wt.ude.hcetnuy@gnehchc

Received 20 June 2017; Revised 18 September 2017; Accepted 27 September 2017; Published 9 November 2017

Academic Editor: Amparo Alonso-Betanzos

Copyright © 2017 Jun-He Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.