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Combining Trend-Based Loss with Neural Network for air quality forecasting in Internet of Things

Computer Modeling in Engineering & Sciences 2020年第11期125卷 849-863页

作者： Weiwen Kong BaoweiWang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China

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.

关键词： air quality forecasting Internet of Things recurrent neural network predicted trend loss function

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Research on Decision Support System (DSS) of Atmospheric Environment Management in Anhui Province Based on air quality forecasting

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Meteorological and Environmental Research 2018年第4期9卷 61-65页

作者： Geng Tianzhao Ji Mian Zhu Yu Wang Huan Dong Hao Zhao Xuhui Cheng Long Anhui Environmental Monitoring Central Station Hefei 230071 China

With the atmospheric stereoscopic monitoring, air quality forecasting and decision of environment management as the main line, and comprehensive management system as the guidance, five platforms including infrastructure, technological support, monitoring and early monitoring, decision support and information services were established. These platforms have 15 subsystems, including stereoscopic monitoring network, visual business consultation, high-performance computing environment, comprehensive management of atmospheric data, emission inventories of pollu-tion sources, evaluation tools of atmospheric models, monitoring and management of air pollution, forecasting and early warning of air quality, diag-nostic analysis of atmospheric environment, tracking of air pollution sources, emergency management of air pollution, conformity management of air quality, comprehensive display of information, releasing of information to external networks, and releasing of information by mobile networks. The decision support system （DSS） of atmospheric environment management could realize an integration business system of 11 air quality forecast - heavy pollution weather warning - diagnosis of pollution causes （dynamic analysis of pollution sources） -air quality conformity planning （air pollu-tion emergency management） -evaluation of forecasting and warning results （evaluation pf management measures） -air quality forecasting＂ and provide the technical support for the prevention and control of atmosphere pollution in Anhui province.

关键词： Atmospheric stereoscopic monitoring air quality forecasting Decision of environmental management

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An Online Method Based on Random Forest for air Pollutant Concentration forecasting

An Online Method Based on Random Forest for Air Pollutant Co...

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第37届中国控制会议

作者： Jiangeng Li Xingyang Shao Huihong Zhao College of Automation Faculty of Information TechnologyBeijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Electromechanical Engineering Dezhou University Clean Energy Research and Technology Promotion Center Dezhou University

With rapid modernization, air quality is becoming gradually deteriorate. To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. The factors relevant to air pollutant concentration forecasting contain simultaneously numeric type（temperature etc.） and non-numeric type（wind direction etc.）. Random Forest has many advantages, which includes it can deal with numeric features and non-numeric features. In this study, an online forecasting method based on Random Forest is proposed to predict the concentrations of three kinds of air pollutants（PM2.5, NO2,SO2）, 24 hours in advance. The sliding window is used to take the recent data to retrain Random Forest prediction model and the well-trained models is used to predict the dependent variable at target moment. Before prediction model is trained, a variable selection method based on Random Forests（VSURF） is used to select the factors that are relevant to the forecast of air pollutant concentrations. We evaluate our method with dataset from Microsoft Research. Comparison with baseline methods shows that our method achieve state-of-art performance on air pollutant concentration forecasting. Experimental results also indicate that the features we selected using VSURF method are most important predictors for the prediction of three kinds of air pollutant concentrations.

关键词： Random Forest(RF) air quality forecasting online forecasting feature selection

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