基于时域卷积网络的水文模型

Hydrological model based on temporal convolutional network

水位预测是防洪预警工作的辅助决策支持。为了进行准确的水位预测,为预防自然灾害提供科学依据,提出一种结合改进的灰狼优化(MGWO)算法与时域卷积网络(TCN)的预测模型MGWO-TCN。针对标准灰狼优化(GWO)算法存在早熟停滞的不足引入差分进化(DE)算法,扩展灰狼种群的多样性;改进灰狼种群更新时的收敛因子和变异时的变异算子,以自适应的形式对参数进行调整,提升算法的收敛速度,均衡算法的全局与局部搜索能力;利用MGWO算法对TCN的重要参数寻优,提升TCN的预测性能。将MGWO-TCN预测模型用于河流水位预测,预测结果的均方根误差(RMSE)为0.039。实验结果表明,与对比模型相比,MGWO-TCN预测模型具有更好的寻优能力和更高的预测精度。

Water level prediction is an auxiliary decision support for flood warning work.For accurate water level prediction and providing scientific basis for natural disaster prevention,a prediction model combining Modified Gray Wolf Optimization(MGWO)algorithm and Temporal Convolutional Network(TCN)was proposed,namely MGWO-TCN.In view of the shortage of premature and stagnation in the original Gray Wolf Optimization(MGWO)algorithm,the idea of Differential Evolution(DE)algorithm was introduced to extend the diversity of the grey wolf population.The convergence factor during update and the mutation operator during mutation of the grey wolf population were improved to adjust the parameters in the adaptive manner,thereby improving the convergence speed and balancing the global and local search capabilities of the algorithm.The proposed MGWO algorithm was used to optimize the important parameters of TCN to improve the prediction performance of TCN.The proposed prediction model MGWO-TCN was used for river water level prediction,and the Root Mean Square Error(RMSE)of the model’s prediction results was 0.039.Experimental results show that compared with the comparison model,the proposed MGWO-TCN has better optimization ability and higher prediction accuracy.