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      Spatial-Temporal Analysis of Environmental Data of North Beijing District Using Hilbert-Huang Transform

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          Abstract

          Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

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          A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

          Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems.
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            Author and article information

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            9 December 2016
            2016
            : 11
            : 12
            Affiliations
            [1 ]School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
            [2 ]School of Electrical & Computer Engineering, RMIT University, Melbourne, Australia
            West Virginia University, UNITED STATES
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            • Conceptualization: YX.

            • Data curation: LH YX.

            • Formal analysis: YX XW LH.

            • Funding acquisition: WW YX.

            • Investigation: XW WM.

            • Methodology: YX.

            • Project administration: YX WW.

            • Resources: YX.

            • Software: YX LH.

            • Supervision: WM XW.

            • Validation: YX XW WW WM.

            • Visualization: LH YX.

            • Writing – original draft: YX.

            • Writing – review & editing: XW WM WW.

            [¤]

            Current address: School of Computer Science and Engineering, Guilin University Of Aerospace Technology, Guilin, Guangxi, China.

            ‡ These authors also contributed equally to this work.

            PONE-D-16-34742
            10.1371/journal.pone.0167662
            5147931
            27936056
            © 2016 Xiang et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Figures: 11, Tables: 5, Pages: 18
            Product
            Funding
            The authors received no specific funding for this work.
            Categories
            Research Article
            Computer and Information Sciences
            Network Analysis
            Signaling Networks
            Wireless Sensor Networks
            Physical Sciences
            Physics
            Electromagnetic Radiation
            Solar Radiation
            Physical Sciences
            Materials Science
            Material Properties
            Surface Properties
            Surface Temperature
            Biology and Life Sciences
            Agriculture
            Agricultural Soil Science
            Ecology and Environmental Sciences
            Soil Science
            Agricultural Soil Science
            Engineering and Technology
            Signal Processing
            White Noise
            Biology and Life Sciences
            Computational Biology
            Ecosystem Modeling
            Biology and Life Sciences
            Ecology
            Ecosystems
            Ecosystem Modeling
            Ecology and Environmental Sciences
            Ecology
            Ecosystems
            Ecosystem Modeling
            Biology and Life Sciences
            Ecology
            Ecosystems
            Ecology and Environmental Sciences
            Ecology
            Ecosystems
            Biology and Life Sciences
            Agriculture
            Custom metadata
            All relevant data are within the paper and its Supporting Information files.

            Uncategorized

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