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The Development and Application of Machine Learning in Atmospheric Environment Studies

Lianming Zheng, Rui Lin, Xuemei Wang, Weihua Chen
2021 Remote Sensing  
In this paper, we present a brief overview of the development of ML models as well as their application to atmospheric environment studies.  ...  Finally, the prospects of ML for atmospheric prediction are discussed.  ...  Classical application using remote sensing data.  ... 
doi:10.3390/rs13234839 fatcat:hw3zmhycnjg4lowjr654ymqsha

Aerosol retrieval from remote sensing image using artificial neural network

Houmao Wang, Jiaku
2010 2010 International Conference on Computer Application and System Modeling (ICCASM 2010)  
The usual method of aerosol retrieval using remote sensing is interpolation of look-up-table (LUT), but it is too time-consuming.  ...  By comparison, not only is the retrieval error of the neural network within acceptable range, but also it can reduce much processing time.  ...  For example, to a 400 x 400 remote sensing image, interpolation requires 12 � 14 hours, while training neural network only needs 1 � 2 hours.  ... 
doi:10.1109/iccasm.2010.5620271 fatcat:aqxfrweyybdyvm7hkvarjwno7q

Analysis of Influencing Factors of PM2.5 Concentration and Design of a Pollutant Diffusion Model Based on an Artificial Neural Network in the Environment of the Internet of Vehicles

Sumin Li, Xiuqin Pan, Qian Li, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
The application of artificial neural networks in haze prediction is studied.  ...  In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network.  ...  Remote sensing monitoring of haze mainly uses remote sensing data to obtain aerosol optical thickness and other data, analyzes the relationship between aerosol optical thickness and haze concentration,  ... 
doi:10.1155/2021/3092197 fatcat:pueyyhxjujhephj7lvca4ef2x4

Study on correlations between Lidar scattered light signal and air quality data in an industrial area

Juliana Steffens, Roberto Guardani, Eduardo Landulfo, Paulo F. Moreira, Renata F. da Costa
2011 Procedia Environmental Sciences  
By using neural networks as a non-linear association method, a clear correlation was obtained between the Lidar scattered light signal and hourly-averaged ground-level ozone concentration.  ...  Results of a campaign on the use of Lidar in an industrial area in the city of Cubatão, Brazil, are presented.  ...  Center, University of São Paulo, and by Petrobras.  ... 
doi:10.1016/j.proenv.2011.03.012 fatcat:5amzph5jmrbude3lwmmhp5nhje

A Review of Remote Sensing for Environmental Monitoring in China

Jun Li, Yanqiu Pei, Shaohua Zhao, Rulin Xiao, Xiao Sang, Chengye Zhang
2020 Remote Sensing  
Due to the characteristics of large-scale and dynamic observation, remote sensing technology has been an indispensable approach for environmental monitoring.  ...  The remote sensing models and methods for various types of environmental monitoring, and the specific applications in China are comprehensively summarized.  ...  All authors have read and agreed to the published version of the manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12071130 doaj:a3cca73bb5b948c28c301d8bc40cf9f5 fatcat:hrnme56p5fawjkukensc25nfme

Prediction of Aerosol Particle Size Distribution Based on Neural Network

Yali Ren, Jiandong Mao, Hu Zhao, Chunyan Zhou, Xin Gong, Zhimin Rao, Qiang Wang, Yi Zhang
2020 Advances in Meteorology  
The results show that BP neural network has a better prediction effect than that of the RBF neural network and is an effective method to obtain the aerosol particle size distribution of the whole atmosphere  ...  To avoid solving such an integral equation, the BP neural network prediction model was established.  ...  Active remote sensing refers to a remote sensing system that transmits a certain form of electromagnetic or light wave to the target from an artificial radiation source on a remote sensing platform and  ... 
doi:10.1155/2020/5074192 fatcat:gkleizke5fchpmnfb7nr3dhcpe

Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

Yanhu He, Zhenjie Gong, Yanhui Zheng, Yuanbo Zhang
2021 Water  
range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network.  ...  remote sensing images and field observations.  ...  Acknowledgments: The authors would like to express their gratitude to all of the reviewers for their valuable recommendations. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/w13202844 fatcat:yljhgtp2qrbpjio4rww3h627gy

Application of High-resolution Satellite Imagery in Water Quality Monitoring of Rivers and Lakes

Jian Song, Jiabin Wang
2019 IOP Conference Series: Materials Science and Engineering  
First, the original remote sensing data are preprocessed by radiometric calibration, geometric correction, atmospheric correction and water extraction, and then an inversion model is established to invert  ...  The traditional water quality monitoring needs a lot of manpower and financial resources, but with the advantage of high time efficiency and other advantages of high-score data for information extraction  ...  ratio of remote sensing, and use the radiation transmission model to simulate the propagation process of light in the atmosphere and water, and use the remote sensing data to retrieve the concentration  ... 
doi:10.1088/1757-899x/592/1/012160 fatcat:32r7qgifujfjncfoaloh2rahfi

Numerical Simulation of Donghu Lake Hydrodynamics and Water Quality Based on Remote Sensing and MIKE 21

Xiaojuan Li, Mutao Huang, Ronghui Wang
2020 ISPRS International Journal of Geo-Information  
The results show that the water quality simulation of chlorophyll a and nitrate nitrogen mean square errors fell to 17% and 24%, from 19% and 31% respectively, after optimization using remote sensing spatial  ...  At the same time, lake water quality inversion technology using the characteristics of spatial optical continuity data from remote sensing satellites is constantly improving.  ...  Remote Sensing Inversion Model In the back propagation (BP) neural network model, it is easy to fall into local extreme values during training.  ... 
doi:10.3390/ijgi9020094 fatcat:7wng5ksxlvavxialp6jme5kwgi

Statistical and Machine Learning Models for Remote Sensing Data Mining—Recent Advancements

Monidipa Das, Soumya K. Ghosh, Vemuri M. Chowdary, Pabitra Mitra, Santosh Rijal
2022 Remote Sensing  
During the last few decades, the remarkable progress in the field of satellite remote sensing (RS) technology has enabled us to capture coarse, moderate to high-resolution earth imagery on weekly, daily  ...  Conflicts of Interest: The authors declare no conflict of interest.  ...  Acknowledgments: We would like to thank all authors who have contributed to this volume by sharing their domain knowledge, research experiences and experimental results.  ... 
doi:10.3390/rs14081906 fatcat:zvmfnk4djvcmlhismmnlrv4g3e

A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

J. Fan, Q. Li, J. Hou, X. Feng, H. Karimian, S. Lin
2017 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc.  ...  processing algorithms and deep recurrent neural network (DRNN).  ...  ACKNOWLEDGEMENTS (OPTIONAL) This work was supported by the National Key Technology R&D Program of the Ministry of Science and Technology of China (Grant No. 2012BAC20B06).  ... 
doi:10.5194/isprs-annals-iv-4-w2-15-2017 fatcat:spaofwi42jalfphbjbvfxl6trm

Agro-food farmland Environmental Monitoring Techniques and Equipment

Jia Wenshen, Pan Ligang, Qian yuange, Wang Jihua, Wu Wenfu
2011 Procedia Environmental Sciences  
Based on the comprehensive literatures, and on the basis of research achievements, combinative oneself from fast detection techniques of pesticide residues, heavy metal pollution fast detection techniques  ...  In this paper, new, fast and efficient farmland environment technology and instrument equipment already become this field of important scientific and technological requirements.  ...  Acknowledgements This work is supported by Beijing great technology projects-Polluted farmland ecological management and agricultural commodity quality improvement demonstrative project of science and  ... 
doi:10.1016/j.proenv.2011.09.352 fatcat:lh5s6phx25ctfonafq4zhb4vhq

Prediction of Fine Particulate Matter Concentration near the Ground in North China from Multivariable Remote Sensing Data Based on MIV-BP Neural Network

Hailing Wu, Ying Zhang, Zhengqiang Li, Yuanyuan Wei, Zongren Peng, Jie Luo, Yang Ou
2022 Atmosphere  
in North China using remote sensing products.  ...  To predict the spatial distribution of fine-mode particulate matter (PM2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM2.5 mass concentration  ...  Since ANN have broader and more far-reaching applications than random forest and GWR, evaluating the performance of the most basic neural network, BP neural network, can provide a basis and benchmark for  ... 
doi:10.3390/atmos13050825 fatcat:hi54zajkbrdjfmytbm22bv6tpa

Front Matter: Volume 11152

Adolfo Comerón, Evgueni I. Kassianov, Klaus Schäfer, Richard H. Picard, Konradin Weber, Upendra N. Singh
2019 Remote Sensing of Clouds and the Atmosphere XXIV  
using a neural network 11152 1I Preliminary validation of GF-1/GF-2 surface reflectance products over land using VNIR atmospheric correction method 11152 1K Monthly analysis of lightning discharges  ...  E) with DIAL, MLS, and IASI s), "Title of Paper," in Remote Sensing of Clouds and the Atmosphere XXIV, edited by Adolfo Comerón, Evgueni I. Kassianov, Klaus Schäfer, Richard H.  ... 
doi:10.1117/12.2557006 fatcat:6nl2l66v3zeuzpuzs3q7dn566u

PM2.5 Air Pollution Prediction through Deep Learning Using Multisource Meteorological, Wildfire, and Heat Data

Pratyush Muthukumar, Kabir Nagrecha, Dawn Comer, Chisato Fukuda Calvert, Navid Amini, Jeanne Holm, Mohammad Pourhomayoun
2022 Atmosphere  
Air pollution is a lethal global threat. To mitigate the effects of air pollution, we must first understand it, find its patterns and correlations, and predict it in advance.  ...  We use high-resolution remote-sensing satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the NASA Terra+Aqua satellites and remote-sensing data from the  ...  We can then construct a deep neural network with an initial layer embedding of h 0 v = x i to perform convolution neighborhoods of nodes, similar to a Convolutional Neural Network (CNN).  ... 
doi:10.3390/atmos13050822 fatcat:ewfbph3ep5egzm6yjxie6xnpha
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