Application of Particle Swarm Optimization and Extreme Learning Machine Forecasting Models for Regional Groundwater Depth Using Nonlinear Prediction Models as Preprocessor
Publication: Journal of Hydrologic Engineering
Volume 23, Issue 12
Abstract
To solve the low-precision problem of traditional methods for groundwater depth prediction, a nonlinear prediction model based on empirical mode decomposition (EMD), phase space reconstruction (PSR), particle swarm optimization (PSO), and extreme learning machine (ELM) was proposed to predict the groundwater depth at Friendship Farm in Heilongjiang Province, China. In this study, the original time series of groundwater depth was preprocessed (decomposed and reconstructed) using EMD and PSR, and then different PSO-ELM models were constructed for the prediction of groundwater depth. The results indicated that the models had a good prediction effect and estimated the following indicators well: the posterior error ratio (), small error frequency (), relative mean square error (), fitting accuracy ratio (), and test forecast effect index (). Comparison of PSR-ELM, PSR-PSO-ELM, and EMD-PSR-PSO-ELM showed a good agreement of root mean square error (RMSE). The results exhibited that the RMSE of PSR-ELM and EMD-PSR-PSO-ELM reduced from 0.4965 to 0.1694 m, and that of PSR-PSO-ELM and EMD-PSR-PSO-ELM reduced from 0.3418 to 0.1694 m, respectively. The results also showed that EMD and PSO effectively improved the prediction performance of the ELM model. This paper also analyzes the effects of climatic factors and human activities on the dynamic changes of local groundwater depth. The results suggest that the effect of precipitation and agricultural production mainly reflected the dynamic groundwater depth.
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Acknowledgments
This study was supported by the National Natural Science Foundation of China (Nos. 51579044, 41071053, 51479032), National Key R&D Program of China (No. 2017YFC0406002), Science and Technology Program of Water Conservancy of Heilongjiang Province (Nos. 201319, 201501, 201503).
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©2018 American Society of Civil Engineers.
History
Received: Dec 14, 2017
Accepted: Jun 29, 2018
Published online: Sep 27, 2018
Published in print: Dec 1, 2018
Discussion open until: Feb 27, 2019
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