| 基于机器学习模型的海河北系干旱预测研究 |
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| 引用本文： | 赵美言,胡涛,张玉虎,蒲晓,高峰.基于机器学习模型的海河北系干旱预测研究[J].干旱区地理,2020,43(4):880-888. |
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| 作者姓名： | 赵美言 胡涛 张玉虎 蒲晓 高峰 |
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| 作者单位： | 1.首都师范大学数学科学学院,北京 100048;2.首都师范大学资源环境与旅游学院,北京 100048;3.国家气象信息中心,北京 100081 |
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| 基金项目： | 北京市科技计划课题（编号：Z201100006720001）; 首都师范大学交叉研究院项目（编号：00719530011010, 00719530012012, 00719530012010）; 国家重点研发计划项目（编号：2017YFC0406002） |
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| 摘 要： | 提高干旱预测精度能为流域干旱应对及风险防范提供可靠数据支撑,构建比选合适的干旱模型是当前研究的热点。研究以4个时间尺度（3、6、9、12月）标准化降水指数（SPI）为表征指标,利用小波神经网络（WNN）、支持向量回归（SVR）、随机森林（RF）三种机器学习算法分别构建了海河北系干旱预测模型,利用Kendall、K-S、MAE三种检验方法判定模型表现及其稳定性。研究表明：（1） WNN、SVR模型呈现结果在不同时间尺度SPI存在差异,WNN最适合12个月尺度SPI干旱预测;SVR最适合6个月尺度SPI干旱预测。（2） 对3、12个月尺度SPI,RF预测性能最优（Kendall>0.898,MAE<0.05）;对6、9个月尺度SPI,SVR预测性能最优（Kendall>0.95,MAE<0.04）。（3） 模型预测性能稳定性存在区别,RF预测稳定性最高,其次为SVR。（4） 构建的三种模型表现异同主要是因为SVR转为凸优化问题解决了WNN易陷入局部最优解的不足,从而提高了模型预测性能,RF集成多样化回归树,降低了弱学习器的负面影响,提高了模型预测准确率及稳定性,同时,RF处理包含噪声的降水数据的能力更强。
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| 关 键 词： | 干旱 WNN SVR RF SPI 海河北系 |
| 收稿时间： | 2019-11-12 |
Drought prediction based on machine learning models in the northern part of Haihe River Basin |
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| ZHAO Mei-yan,HU Tao,ZHANG Yu-hu,PU Xiao,GAO Feng.Drought prediction based on machine learning models in the northern part of Haihe River Basin[J].Arid Land Geography,2020,43(4):880-888. |
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| Authors: | ZHAO Mei-yan HU Tao ZHANG Yu-hu PU Xiao GAO Feng |
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| Affiliation: | 1.School of Mathematical Sciences,Capital Normal University,Beijing 100048,China;2.College of Resources Environment & Tourism,Capital Normal University,Beijing 100048,China;3.National Meteorological Information Center,Beijing 100081,China |
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| Abstract: | Drought is one of the major natural disasters. Improving the accuracy of drought prediction can provide reliable data to support drought response and risk prevention. The construction of suitable drought prediction models is a current research hotspot. Machine learning models are widely used for drought forecasting such as artificial neural network （ANN）,wavelet neural network （WNN）,support vector regression （SVR） and random forest （RF）. This paper explored and compared the forecasting abilities and stabilities of the wavelet neural network （WNN）,support vector regression （SVR） and random forest （RF） in the northern part of the Haihe River Basin,China. The northern part of the Haihe River Basin is located in the upper reaches of Beijing and Tianjin,which is an important industrial and agricultural production area in China. The total area is 8.34×105 km2. It has a temperate monsoon climate with average annual precipitation of 490 mm. The models used in this paper are based on the standard precipitation index （SPI） at different time scales （3,6,9 and 12 months）. The SPI was calculated using daily precipitation data obtained at eight meteorological points in the northern part of the Haihe River Basin from 1960 to 2010. Then,the SPI series were predicted use the WNN,SVR and RF models separately. The effectiveness of the three machine learning models is compared by Kendall rank correlation（Kendall）,Kolmogorov-Smirnov（K-S） test and mean absolute error （MAE）. The following results were observed:（1） The prediction abilities of the WNN and SVR models vary at different time scales,with WNN performing best suited for SPI-12 and SVR best suited for SPI-6. （2） For the SPI-3 and SPI-12,the RF prediction performance was optimal （Kendall > 0.898,MAE < 0.05）. For the SPI-6 and SPI-9,the SVR prediction performance was optimal （Kendall > 0.95,MAE < 0.04）. （3） The stability of the model prediction performances differed,with RF being most stable,followed by SVR. （4） The variation in model predictions performance is due to the following: the convex optimization of SVR resolves the WNN weakness of falling into a local optimal solution,thereby improving the prediction performance of the model. The RF boosting diversified regression trees,which reduce the negative influence of weak learners,improve the prediction accuracy and stability of the model. Furthermore,the capacity of the RF model is strongest in its ability to cope with precipitation data that contains noise. This paper presents a comprehensive analysis of the drought prediction performance of multiple models at multiple time scales for SPI series and preliminarily explores the internal mechanisms of model differentiation. The result of this study provides alternative models and research ideas for the northern part of the Haihe River Basin and beyond． |
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| Keywords: | drought SVR RF WNN SPI the northern part of Haihe River Basin |
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