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Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things

作     者:Weiwen Kong BaoweiWang 

作者机构:School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital ForensicsMinistry of EducationNanjing210044China 

出 版 物:《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模(英文))

年 卷 期:2020年第125卷第11期

页      面:849-863页

核心收录:

学科分类:0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学] 

基  金:This work is supported by the National Natural Science Foundation of China under Grant 61972207,U1836208,U1836110,61672290 the Major Program of the National Social Science Fund of China under Grant No.17ZDA092,by the National Key R&D Program of China under Grant 2018YFB1003205 by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund 

主  题:Air quality forecasting Internet of Things recurrent neural network predicted trend loss function 

摘      要:Internet of Things(IoT)is a network that connects things in a special *** embeds a physical entity through an intelligent perception system to obtain information about the component at any *** connects various *** has the ability of information transmission,information perception,and information *** air quality forecasting has always been an urgent problem,which affects people’s quality of life *** far,many air quality prediction algorithms have been proposed,which can be mainly classified into two *** is regression-based prediction,the other is deep learning-based ***-based prediction is aimed to make use of the classical regression algorithm and the various supervised meteorological characteristics to regress themeteorological *** learning methods usually use convolutional neural networks(CNN)or recurrent neural networks(RNN)to predict the meteorological *** an excellent feature extractor,CNN has achieved good performance in many *** the same way,as an efficient network for orderly data processing,RNN has also achieved good ***,few or none of the above methods can meet the current accuracy requirements on ***,there is no way to pay attention to the trend monitoring of air quality *** the sake of accurate results,this paper proposes a novel predicted-trend-based loss function(PTB),which is used to replace the loss function in *** the same time,the trend of change and the predicted value are constrained to obtain more accurate prediction results of PM_(2.5).In addition,this paper extends the model scenario to the prediction of the whole existing training data *** the data on the next day of the model is mixed labels,which effectively realizes the prediction of all *** experiments show that the loss function proposed in this paper is effective.

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