A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall
A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall
复制标题
具有互信息和粒子群优化的混合小波神经网络模型用于预测月降雨量
DOI：
10.1016/j.jhydrol.2015.04.047
复制
发表时间：
2015-08
影响因子：
6.4
通讯作者：
Jianxin Qin
中科院分区：
文献类型：
--
作者：
Xinguang He;Huade Guan;Jianxin Qin
本文将多分辨率分析（MRA）、互信息（MI）和粒子群优化（PSO）技术结合到人工神经网络（ANN）模型中，建立了一个混合小波神经网络（HWNN）模型，用于从前期月降雨量和气候指标有效地预测月降雨量。首先将标准化的月降水距平和大尺度气候指数用最大重叠离散小波变换（MODWT）分解为若干不同时间尺度的子序列分量。然后，在每个时间尺度上，一个小的和适当的子集的人工神经网络的输入确定的修改部分MI算法从一组候选子序列组件具有不同的滞后预测降雨异常子序列在相应的尺度。利用粒子群优化算法确定神经网络隐层的最佳神经元个数，然后从所选的预报子序列中对每个降水异常子序列进行预报。最后，将各预报距平子序列求和，再将标准化月降水量进行反变换，得到月降水量预报。使用澳大利亚255个雨量计站对所提出的HWNN方法进行了检验，并与基于未分解时间序列的参考方法进行了比较。预报性能与观测值进行了比较，并通过相对绝对误差和Nash-Sutcliffe效率的共同统计进行了评价。结果表明，HWNN模型对澳大利亚月降水量的预报精度较参考模型有明显提高，其中对澳大利亚东南部内陆站和西澳大利亚内陆站的预报精度提高更为显著。
In this paper, a hybrid wavelet neural network (HWNN) model is developed for effectively forecasting monthly rainfall from antecedent monthly rainfall and climate indices by incorporating the multiresolution analysis (MRA), mutual information (MI) and particle swarm optimization (PSO) into artificial neural network (ANN) models. The standardized monthly rainfall anomaly and large-scale climate indices are first decomposed by using the maximal overlap discrete wavelet transform (MODWT) into a certain number of subseries components with different time scales. Then at each time scale, a small and appropriate subset of ANN inputs is identified by the modified partial MI algorithm from a set of candidate subseries components with different lags for forecasting the rainfall anomaly subseries at the corresponding scale. The optimal number of neurons in the hidden layers of ANNs is determined by PSO algorithm, and then each of rainfall anomaly subseries is forecasted from the selected predictor subseries. Finally, the monthly rainfall forecast is achieved by summing all the predicted anomaly subseries and applying the inverse transform of standardized monthly rainfall. The proposed HWNN method is examined with 255 rain gauge stations over Australia, and compared to the reference methods based on the undecomposed time series. The forecasting performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error and Nash–Sutcliffe efficiency. The results show that the HWNN model improves the monthly rainfall forecasting accuracy over Australia in comparison to the reference models, and the improvement is more significant for the inland stations in southeast Australia and stations in west Australia.
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DOI：
10.2307/2985274
发表时间：
1968
期刊：
Applied statistics
影响因子：
--
作者：
R. Rimmer;Norman R. Draper;Harry Smith
通讯作者：
R. Rimmer;Norman R. Draper;Harry Smith
DOI：
10.7146/dpb.v19i339.6570
发表时间：
1990-11
期刊：
--
影响因子：
--
作者：
M. F. Møller
通讯作者：
M. F. Møller
影响因子：
5.8
作者：
Abbot, John;Marohasy, Jennifer
通讯作者：
Marohasy, Jennifer
影响因子：
6.4
作者：
Ashish Sharma
通讯作者：
Ashish Sharma
影响因子：
1.9
作者：
P. Sprent;N. Draper;Harry Smith
通讯作者：
P. Sprent;N. Draper;Harry Smith