Multi-models for SPI drought forecasting in the north of Haihe River Basin, China
Multi-models for SPI drought forecasting in the north of Haihe River Basin, China
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DOI：
10.1007/s00477-017-1437-5
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发表时间：
2017-07
影响因子：
4.2
通讯作者：
Yuhu Zhang;Weiwei Li;Qiuhua Chen;Xiao Pu;Liu Xiang
中科院分区：
文献类型：
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作者：
Yuhu Zhang;Weiwei Li;Qiuhua Chen;Xiao Pu;Liu Xiang
干旱是最具破坏性的气候灾害之一。因此，干旱预报在减轻干旱的一些不利影响方面发挥着重要作用。数据驱动模型广泛用于干旱预测，如ARIMA模型、人工神经网络（ANN）模型、小波神经网络（WANN）模型、支持向量回归模型、灰色模型等。本研究使用三种数据驱动模型（ARIMA 模型、ANN 模型、WANN 模型）基于两个时间尺度（SPI、SPI-6 和 SPI-12）的标准降水指数进行干旱预报。然后选择最优的数据驱动模型和SPI时间尺度，对海河流域北部地区的干旱进行有效预报。通过 Kolmogorov-Smirnov (K-S) 检验、Kendall 等级相关性和相关系数 (R2) 比较三个数据模型的有效性。预测结果表明，WANN模型对海河流域以北地区SPI-6和SPI-12值的预测更加适合和有效。
Drought is one of the most devastating climate disasters. Hence, drought forecasting plays an important role in mitigating some of the adverse effects of drought. Data-driven models are widely used for drought forecasting such as ARIMA model, artificial neural network (ANN) model, wavelet neural network (WANN) model, support vector regression model, grey model and so on. Three data-driven models (ARIMA model; ANN model; WANN model) are used in this study for drought forecasting based on standard precipitation index of two time scales (SPI; SPI-6 and SPI-12). The optimal data-driven model and time scale of SPI are then selected for effective drought forecasting in the North of Haihe River Basin. The effectiveness of the three data-models is compared by Kolmogorov–Smirnov (K–S) test, Kendall rank correlation, and the correlation coefficients (R2). The forecast results shows that the WANN model is more suitable and effective for forecasting SPI-6 and SPI-12 values in the north of Haihe River Basin.