Precipitation prediction based on decomposition algorithm-based models
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Abstract:
Prediction of hydrological time series is a challenging issue due to complicated hydrologic processes, which would greatly impact the water resources management and hydraulic engineering design. Related studies indicated that combined models, which are based on the decomposition-prediction-reconstruction mode usually perform much better for the prediction of hydrological time series than single models. A great number of studies have been conducted on diverse combinations and applications of combined models, however, a comprehensive evaluation of the applicability and stability of different combined models is lacking, leaving a research gap for this important issue. Four commonly used decomposition methods were applied, namely empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), modified ensemble empirical mode decomposition (MEEMD), and variational mode decomposition (VMD). The four decomposition methods were further combined with five representative prediction models, namely multivariable linear regression (MLR), random forest (RF), back propagation (BP), convolutional neural networks (CNN), and long short-term memory (LSTM), to establish a total of 20 combined models. These 20 combined models were used to predict the annual precipitation and flood season precipitation and conducted a comparative analysis of the models in the Miyun basin and Guanting basin in North China.in North China.Results showed that: (1) The single models predicted both annual precipitation and flood season precipitation more accurately in the Miyun basin than in the Guanting basin, but the single model’s performances were overall poor in the two basins. (2) The prediction results from the combined models after coupling with decomposition algorithms become much better than those from the single models, and the positive errors could be offset by the negative errors during the prediction processes when using the combined models, which could improve the overall prediction accuracy of precipitation. (3) Compared with the EMD and other algorithms, the VMD algorithm has the most significant effect on improving the prediction accuracy of precipitation, and the applicability and stability of the combined models is VMD-MLR> VMD-LSTM> VMD-BP> VMD-CNN.Moreover, the results indicated a single model can not accurately grasp the complex characteristics of the precipitation time series. The prediction accuracy of a single model could be approved through parameters optimization, however, the effect is not obvious. Compared with a single model, the combined models based on decomposition algorithms can effectively improve the prediction results. In the combined models, the effectiveness of decomposition algorithms (such as EMD and VMD) in decomposing the original time series directly affects the models' prediction results. After combining with the decomposition algorithm, the models' performance improves significantly, and their applicability and stability are greatly enhanced. After combining with the decomposition algorithm, even some simple models (such as MLR) can be used to accurately predict precipitation time series with complex variability patterns. Different model combinations and predictors lead to differences in prediction results among combined models. Therefore, more influencing factors (such as climate indicators) and more complex combined models based on the decomposition-prediction-reconstruction mode should be explored in future research to optimize the prediction model and prediction process, to further improve the prediction accuracy and reliability of the precipitation in this study area.
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