scholarly journals A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 373
Author(s):  
Zhenyu Song ◽  
Cheng Tang ◽  
Junkai Ji ◽  
Yuki Todo ◽  
Zheng Tang
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Delay ◽  
Real World ◽  
Search Algorithm ◽  
Dimensional Space ◽  
Neural Model ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Pm2.5 Concentration

Air pollution in cities has a massive impact on human health, and an increase in fine particulate matter (PM2.5) concentrations is the main reason for air pollution. Due to the chaotic and intrinsic complexities of PM2.5 concentration time series, it is difficult to utilize traditional approaches to extract useful information from these data. Therefore, a neural model with a dendritic mechanism trained via the states of matter search algorithm (SDNN) is employed to conduct daily PM2.5 concentration forecasting. Primarily, the time delay and embedding dimensions are calculated via the mutual information-based method and false nearest neighbours approach to train the data, respectively. Then, the phase space reconstruction is performed to map the PM2.5 concentration time series into a high-dimensional space based on the obtained time delay and embedding dimensions. Finally, the SDNN is employed to forecast the PM2.5 concentration. The effectiveness of this approach is verified through extensive experimental evaluations, which collect six real-world datasets from recent years. To the best of our knowledge, this study is the first attempt to utilize a dendritic neural model to perform real-world air quality forecasting. The extensive experimental results demonstrate that the SDNN offers very competitive performance relative to the latest prediction techniques.

scholarly journals Time-Delay Identification Using Multiscale Ordinal Quantifiers

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 969
Author(s):  
Miguel C. Soriano ◽  
Luciano Zunino
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Real World ◽  
Propagation Speed ◽  
Real World Data ◽  
External Observer ◽  
The North ◽  
Weather Phenomenon ◽  
The North Atlantic Oscillation ◽  
Finite Propagation Speed

Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon.

scholarly journals Visualization of Correlation for Air Pollution Time Series using TAQMN Data

2019 ◽  
Vol 8 (3) ◽  
pp. 7922-7927
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Particulate Matter ◽  
Pearson Correlation ◽  
Fine Particulate Matter ◽  
Correlation Method ◽  
Well Being ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Pm2.5 Concentration

In Taiwan country Annan, Chiayi, Giran, and Puzi cities are facing a serious fine particulate matter (PM2.5) issue. To date the impressive advance has been made toward understanding the PM2.5 issue, counting special temporal characterization, driving variables and well-being impacted. However, notable research as has been done on the interaction of the content between the selected cities of Taiwan country for particulate matter (PM2.5) concentration. In this paper, we purposed a visualization technique based on this principle of the visualization, cross-correlation method and also the time-series concentration with particulate matter (PM2.5) for different cities in Taiwan. The visualization also shows that the correlation between the different meteorological factors as well as the different air pollution pollutants for particular cities in Taiwan. This visualization approach helps to determine the concentration of the air pollution levels in different cities and also determine the Pearson correlation, r values of selected cities are Annan, Puzi, Giran, and Wugu.

Analysis and generation of groundwater concentration time series

2018 ◽  
Vol 111 ◽  
pp. 20-30 ◽  
Author(s):  
Maria Crăciun ◽  
Călin Vamoş ◽  
Nicolae Suciu
Keyword(s):  
Time Series ◽  
Concentration Time ◽  
Concentration Time Series

scholarly journals Cluster analysis of WIBS single-particle bioaerosol data

2013 ◽  
Vol 6 (2) ◽  
pp. 337-347 ◽  
Author(s):  
N. H. Robinson ◽  
J. D. Allan ◽  
J. A. Huffman ◽  
P. H. Kaye ◽  
V. E. Foot ◽  
...  
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Single Particle ◽  
Fungal Spores ◽  
Polystyrene Latex ◽  
Data Sets ◽  
Forest Site ◽  
Fluorescence Measurements ◽  
Concentration Time ◽  
Concentration Time Series

Abstract. Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

scholarly journals Mapping the Wall-Region Dynamics of High-Flux Gas-Solid Riser Using Scaling Regions from the Solid Concentration Time Series

Engineering ◽  
2019 ◽  
Vol 11 (01) ◽  
pp. 74-92
Author(s):  
Justin M. Jeremiah ◽  
Samwel V. Manyele ◽  
Abraham K. Temu ◽  
Jesse-X. Zhu
Keyword(s):  
Time Series ◽  
Wall Region ◽  
High Flux ◽  
Solid Concentration ◽  
Concentration Time ◽  
Concentration Time Series

scholarly journals Discontinuities in the Ozone Concentration Time Series from MERRA 2 Reanalysis

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 812 ◽  
Author(s):  
Peter Krizan ◽  
Michal Kozubek ◽  
Jan Lastovicka
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Ozone Concentration ◽  
Homogeneity Test ◽  
Concentration Data ◽  
Trend Analyses ◽  
Earth Observing System ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Modern Era

Artificial discontinuities in time series are a great problem for trend analysis because they influence the values of the trend and its significance. The aim of this paper is to investigate their occurrence in the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA 2) ozone concentration data. It is the first step toward the utilization of the MERRA 2 ozone data for trend analysis. We use the Pettitt homogeneity test to search for discontinuities in the ozone time series. We showed the data above 4 hPa are not suitable for trend analyses due to the unrealistic patterns in an average ozone concentration and due to the frequent occurrence of significant discontinuities. Below this layer in the stratosphere, their number is much smaller, and mostly, they are insignificant, and the patterns of the average ozone concentration are explainable. In the troposphere, the number of discontinuities increases, but they are insignificant. The transition from Solar Backscatter Ultraviolet Radiometer (SBUV) to Earth Observing System (EOS) Aura data in 2004 is visible only above 1 hPa, where the data are not suitable for trend analyses due to other reasons. We can conclude the MERRA 2 ozone concentration data can be used in trend analysis with caution only below 4 hPa.

Application of nonparametric trend analysis to concentration time series

2020 ◽  
Author(s):  
Artur Kohler
Keyword(s):  
Time Series ◽  
Hazardous Substances ◽  
Anthropogenic Activity ◽  
Actual State ◽  
Temporal Behavior ◽  
Concentration Data ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Repeated Sampling ◽  
Non Parametric

<p>Groundwater contamination resulted from chemical releases related to anthropogenic activity often proves to be a persistent feature of the affected groundwater regime.  The affected volume (i.e. where the concentration of hazardous substances exceeds a certain threshold) is a complex and dynamic entity commonly called “contaminant plume”.  The plume can be described as a spatially dependent concentration pattern with temporal behavior.  Persistent plumes are regularly monitored, concentration data gained by repeated sampling of monitoring points and laboratory analyses of the samples are used to assess the actual state of the plume.  The change of the concentrations at certain points of the plume facilitates the assessment of the temporal behavior of the plume.  Repeated sampling of the monitoring points provides concentration time series.</p><p>Concentration time series are evaluated for trends.  Methods include parametric (regression using least squares) and non-parametric methods.  Mann-Kendall statistic is a commonly used, well known non parametric method.</p><p>When using Mann-Kendall statistics consecutive concentration data are compared to each other, their cumulative relation defines Mann-Kendall statistic ‘S’.  However, when comparing concentration data laboratory uncertainties are usually neglected.  Allowing for laboratory uncertainties, rises the question of what concentrations are considered equal, less or more than other concentrations.  In addition aggravating concentration data will change the previous equal – more - less status of two concentrations, thus changing the Mann-Kendall statistics value, which sometimes results in differences in trend significance.</p>

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