摘要 水质预测是水资源管理和水污染防治的基础性、前提性工作,但黄河流域水质预测研究相对滞后。为了改善LSTM水质预测模型的性能、提高其泛化能力,根据水质变化具有周期性和非线性的特征,以黄河小浪底水库溶解氧含量为研究对象,构建了一种卷积神经网络CNN和长短时记忆网络LSTM结合的CNN-LSTM预测模型,经试验验证,该模型可以高效地提取水质特征信息并进行时间序列预测,预测误差比LSTM模型的更低,其预测值的平均绝对误差和均方根误差分别比LSTM模型的低19.72%和10.44%,对较大值和较小值的预测更为准确,且具有较好的泛化性能。 Water quality prediction is the basic and prerequisite work for the management of water resources and prevention and control of wa⁃ter pollution,but the research on water quality prediction in the Yellow River basin is relatively lagged behind.In order to improve the per⁃formance of the LSTM water quality prediction model and increase its generalization ability,according to the periodic and non⁃linear charac⁃teristics of water quality changes,taking the dissolved oxygen concentration of the Xiaolangdi Reservoir on the Yellow River as the research object,a combination of convolutional neural network CNN and length was constructed.The CNN⁃LSTM prediction model of the time memory network LSTM had been verified by experiments.The model can efficiently extract water quality feature information and perform time series prediction.The prediction error is lower than that of the LSTM model.The average absolute error of the predicted value and the root mean square error are 19.72%and 10.44%lower than that of the LSTM model respectively.The prediction of larger and smaller values is more ac⁃curate and it has better generalization performance.
出处 《人民黄河》 CAS 北大核心 2021年第5期96-99,109,共5页 Yellow River
基金 河南省高等学校重点科研项目(20A520041) 河南省重点科技攻关项目(202102210375,212102210518) 2021年度河南科技智库调研课题项目(HNKJZK-2021-61C)。
关键词 水质预测 长短时记忆网络 卷积神经网络 CNN-LSTM模型 小浪底水库 黄河 water quality prediction long and short⁃term memory network convolutional neural network CNN⁃LSTM model Xiaolangdi Reservoir Yellow River
