基于splice-LSTM的多因素西江水位预测模型研究

Research on Xijiang River water level prediction model of multi-factor based on splice-LSTM

准确的水位和水量等水文时间序列预测是水资源管理的重要依据。受上游支流流量、水位等因素影响,传统的单因素水位预测模型不能有效考虑众多因素,水位预测精度面临严峻挑战。以典型西江干线梧州站水位精准预测为研究对象,建立了基于splice-LSTM的多因素水位预测模型,采用拼接的长短期记忆网络(LSTM)和全连接线性模型(Linear),对2020~2021年西江干线多站点的流量数据进行分析,预测梧州站点的水位。研究结果表明:(1)由于splice-LSTM中引入了非线性层,提高了近期历史输入数据的权重,使得模型预测值更加接近历史真实值,降低了预测误差,Linear部分可以提高模型对于线性成分的敏感性,使得模型在水位峰值处的预测更加准确;(2) splice-LSTM模型与传统单因素的ARIMA模型、LSTM模型相比,在水位预测方面准确度分别提升14.4%,10.1%。研究成果可为西江船闸运行调度中心精准预调度船舶提供参考。

Accurate hydrologic time series prediction of water level and water volume is an important basis for water resources management and plays an important role in water transfer detection.Affected by factors such as the flow and water level of upstream tributaries,traditional single-factor water level prediction models cannot effectively consider these factors and water level prediction of Xijiang River faces severe challenges.Taking the typical Wuzhou Station on Xijiang River mainstream as the research object,a multi-factor water level prediction model based on splice-LSTM is established.The spliced Long Short-term Memory network(LSTM)and the fully connected linear model(Linear)were used to analyze and predict the flow and water level of the Xijiang River mainstream like Wuzhou station and other stations from 2020 to 2021.Research results show that:①The splice-LSTM can link to a non-linear layer and thus increase the weight of recent historical input data,making the model prediction closer to the historical value and reducing the prediction error.The linear part can improve the sensitivity of the model to linear components and the model to linear components,making the model′s prediction at the water level peak more accurate.②Compared with the traditional single factor ARIMA model and LSTM model,the accuracy of the split-LSTM model in water level prediction has increased by 14.4%and 10.1%respectively.The research results can provide a scientific reference for the precise pre-scheduling of ships by the Xijiang Shiplock Operation and Dispatching Center.