The structure of the proposed EMD-EEMD-RBFNN-LNN model.

<p>The proposed model has four stages, i.e., denoising, decomposition, component prediction and ensemble. The methods used in the four stages are empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), radial basis function neural network (RBFNN) and linear neural network (LNN), respectively.</p

Statistical characters of the denoising results of the six hydrological time series.

<p>Statistical characters of the denoising results of the six hydrological time series.</p

Four types of comparison models.

<p>Four types of comparison models.</p

The Lempel-Ziv complexity of the six hydrological time series.

<p>It shows the Lempel-Ziv complexity (LZC) of the six original series, denoised series and the IMFs.</p

The prediction model structure of IMFs of the case 2.

<p><b>Note</b>: n-number of the neuron nodes in the input layer.</p

The selected stations of the Haihe River Basin.

<p>This figure shows the locations of the 3 hydrological stations (Guantai, Xiangshuibao, Miyun Reservoir) and 44 meteorological stations (including Beijing). The precipitation data of the 44 meteorological stations are used to compute the annual mean precipitation of HRB.</p

The architecture of the linear neural network.

<p>The architecture of the linear neural network.</p

The decomposition results of the six denoised hydrological time series.

<p>The six series are decomposed into several IMFs and one residue. The IMFs are listed in the order from the highest frequency to the lowest frequency.</p

Data used in the forecasting processes.

<p>Data used in the forecasting processes.</p

Six cases studied in this paper.

<p>Six cases studied in this paper.</p