水利信息化Issue(4)：49-53,5.DOI:10.19364/j.1674-9405.2024.04.009
PCA-MLP神经网络模型在黄河宁夏段径流预测中的应用
Application of PCA-MLP neural network model in runoff prediction in Ningxia Section of Yellow River
窦淼 1侯祥宁1
作者信息
- 1. 黄河水利委员会宁蒙水文水资源局,内蒙古 包头 014000
- 折叠
摘要
Abstract
To improve the prediction accuracy of monthly runoff volume on a short-time scale and to simplify the structure of the neural network model,PCA-MLP neural network model combined with Principal Component Analysis(PCA)and Multi-Layer Perceptron(MLP)neural network is proposed to forecast the monthly runoff volume during the flood season.The model first employs PCA to determine the main influencing factors on runoff volume and then inputs the data of the main influencing factors into MLP neural network model to predict monthly runoff volume.Monthly runoff volume and influencing factors data from Qingtongxia Hydrological Station in Ningxia during the flood season from 2010 to 2019 were used as training samples to train the neural network model,with data from 2020 to 2022 used as testing samples for comparative analysis.The forecast results indicate that the factors influencing the flood season's runoff volume are mainly historical runoff and climatic characteristics.The prediction results of the testing set achieved a coefficient of determination of 0.851,demonstrating that the model can provide guidance for the prediction of monthly runoff volume in Ningxia during the flood season.关键词
径流预测/PCA-MLP神经网络模型/主成分分析/多层感知器神经网络Key words
runoff prediction/PCA-MLP neural network model/principal component analysis/multi-layer perceptron neural network分类
天文与地球科学引用本文复制引用
窦淼,侯祥宁..PCA-MLP神经网络模型在黄河宁夏段径流预测中的应用[J].水利信息化,2024,(4):49-53,5.
