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Application of penalized linear regression and ensemble methods for drought forecasting in Northeast China

  • Zeng Li
  • Taotao Chen
  • Qi Wu
  • Guimin Xia
  • Daocai ChiEmail author
Original Paper

Abstract

Effective drought prediction can be conducive to mitigating some of the effects of drought. Machine learning algorithms are increasingly used for developing drought prediction models due to their high efficiency and accuracy. This study explored the ability of several machine learning models based on penalized linear regression and decision tree (DT)-based ensemble methods to predict drought conditions represented by the Standardized Precipitation–Evapotranspiration Index (SPEI) in Northeast China. We compared the forecasting performance of the penalized linear regression models based on ridge regression (RR) and lasso regression (LR) with the ordinary least squares (OLS) regression model. In addition, the AdaBoost and Random Forests (RF) models were also used to explore the suitability of ensemble methods for improving the forecasting performance. The SPEI was forecast at the different timescales of 3, 6, 12, and 24 months using the aforementioned machine learning models and the indices were used to predict short-term and long-term drought conditions. The prediction results indicated that the penalized linear regression models provided better prediction results and the ensemble methods consistently outperformed the DT model. Overall, the LR models were the optimum models for forecasting the SPEI at different timescales in Northeast China.

Keywords

Drought forecasting Standardized precipitation Evapotranspiration index (SPEI) Penalized linear regression Ensemble methods Machine learning 

Notes

Acknowledgements

This work was supported by the National Science Foundation of China (Grants Nos. 51679142 and 51709173).

Supplementary material

703_2019_675_MOESM1_ESM.pdf (411 kb)
Supplementary file1 (PDF 411 kb)

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Zeng Li
    • 1
  • Taotao Chen
    • 1
  • Qi Wu
    • 1
  • Guimin Xia
    • 1
  • Daocai Chi
    • 1
    Email author
  1. 1.College of Water ResourcesShenyang Agricultural UniversityShenyangChina

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