基于CNN和MC的水文时间序列预测组合模型

对于水位精准的预测是预防洪涝灾害的有效措施。在深度学习不断发展的背景下，提出基于卷积神经网络和马尔科夫链的水文时间序列预测组合模型，该模型解决了现有算法未考虑站点之间空间的相关性、多维输入的时候会提高特征提取中数据重建的复杂度，以及单一模型只考虑水位时间序列线性部分而未考虑非线性部分所导致的预测精度低的问题。该组合模型首先运用卷积神经网络训练水位时间序列和降雨量时间序列对未来水位进行预测，并结合原始时间序列计算得到残差序列，再将使用马尔科夫链训练残差序列得到的残差预测结果和卷积神经网络预测的值相加得到最终的结果。实验表明，该方法与现有算法相比,在预报准确率上能够取得更好的效果。

Combined Hydrological Time Series Forecasting Model Based on CNN and MC

Accurate forecast of water level is an effective measure to prevent flood disasters. Under the background of the continuous development of in-depth learning, a combined hydrological time series forecast model based on convolutional neural network and Markov chain is proposed. The model solves the problems that the existing algorithms do not consider the spatial correlation between stations, multi-dimensional input will increase the complexity of data reconstruction in feature extraction, and the single model only considers the linear part of water level time series without considering the non-linear part, which leads to the low forecast accuracy. Firstly, the combined model uses convolutional neural network to train water level time series and rainfall time series to predict future water level and calculates residual series with original time series. Then, the residual forecast results obtained by Markov chain training residual series and the value of convolution neural network forecast are added together to get the final result. Experiments show that this method can achieve better forecast accuracy than the existing algorithms.