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:2019,28(2):226-232
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K-Similarity降噪的LSTM神经网络水质多因子预测模型
(1.浙江理工大学 信息学院, 杭州 310018;2.聚光科技(杭州)股份有限公司, 杭州 310052)
Water Quality Multi-Factor Prediction Model Using LSTM Neural Network Based on K-Similarity Noise Reduction
(1.School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China;2.Focused Photonics(Hangzhou) Inc., Hangzhou 310052, China)
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投稿时间:2018-07-31    修订日期:2018-08-30
中文摘要: 针对水质预测问题,以地表水水质监测因子作为研究对象,提出了一种基于长短期记忆(LSTM)神经网络的水质多因子预测模型,同时利用提出的K-Similarity降噪法对模型的输入数据进行降噪,提高模型预测性能.通过与BP神经网络、RNN和传统的LSTM神经网络预测模型进行对比实验,证明了所提出的方法均方误差最小,预测结果更准确.
Abstract:In view of the water quality prediction problem, taking the surface water quality monitoring factors as the research object, a Long Short-Term Memory (LSTM) neural network based model is proposed for water quality multi-factor prediction. At the same time, the proposed K-Similarity method is used to denoise the input data of the model to improve the prediction performance of the model. Compared with BP neural network, RNN, and traditional LSTM neural network prediction model, the experiment shows that the proposed method has the least square error and the prediction result is more accurate.
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基金项目:浙江省公益技术应用研究项目(2014C31G2060072)
引用文本:
刘晶晶,庄红,铁治欣,程晓宁,丁成富.K-Similarity降噪的LSTM神经网络水质多因子预测模型.计算机系统应用,2019,28(2):226-232
LIU Jing-Jing,ZHUANG Hong,TIE Zhi-Xin,CHENG Xiao-Ning,DING Cheng-Fu.Water Quality Multi-Factor Prediction Model Using LSTM Neural Network Based on K-Similarity Noise Reduction.COMPUTER SYSTEMS APPLICATIONS,2019,28(2):226-232

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