Application of penalized linear regression and ensemble methods for drought forecasting in Northeast China
Application of penalized linear regression and ensemble methods for drought forecasting in Northeast China
复制标题
惩罚线性回归和集合方法在东北干旱预报中的应用
DOI：
10.1007/s00703-019-00675-8
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发表时间：
2019-06
影响因子：
2
通讯作者：
Chi Daocai
中科院分区：
文献类型：
--
作者：
Li Zeng;Chen Taotao;Wu Qi;Xia Guimin;Chi Daocai
有效的干旱预报有助于减轻干旱的一些影响。机器学习算法以其高效、准确的特点被越来越多地应用于干旱预测模型的开发。研究了几种基于惩罚线性回归和决策树(DT)集成方法的机器学习模型对东北中国地区以标准化降水-蒸散指数(SPEI)为代表的干旱状况的预测能力。比较了基于岭回归(RR)和套索回归(LR)的惩罚线性回归模型与普通最小二乘(OLS)回归模型的预测性能。此外，还利用AdaBoost和随机森林(RF)模型探讨了集成方法对提高预报性能的适用性。使用上述机器学习模型在3、6、12和24个月的不同时间尺度上预测了SPI，并使用这些指数预测了短期和长期干旱条件。预测结果表明，惩罚线性回归模型提供了更好的预测结果，集成方法的预测效果一致优于DT模型。总体而言，LR模型是预测中国东北地区不同时间尺度SPI的最佳模型。
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.
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