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Multilayer Perceptron Models for
Surface Ozone Study in Hong Kong
under the Trans-boundary
Air Pollution Impact
WANG DONG
DOCTOR OF PHILOSOPHY
CITY UNIVERSITY OF HONG KONG
SEPTEMBER 2007
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CITY UNIVERSITY OF HONG KONG
香港城市大學
Multilayer Perceptron Models
for Surface Ozone Study in Hong Kong
under the Trans-boundary
Air Pollution Impact
在跨界空氣汚染影響下用感知器
模型對香港臭氧之研究
Submitted to
Department of Building and Construction
建築系 In Partial Fulfillment of the Requirements
For the Degree of Doctor of Philosophy
哲學博士學位
by
Wang Dong
王 東
September 2007
二零零七年九月
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Abstract
i
AAbbssttrraacctt
Multilayer perceptron (MLP) models have been experiencing a popularity resurgence
for predicting surface ozone level, based on the data of its influential variables colleted
locally from air quality monitoring and meteorology stations around the interested area.
In this dissertation, MLP, not only used traditionally as a predictive model but also an
assessment tool, will be employed to study ozone variation in three typical air
monitoring stations in Hong Kong, the ozone variation of where are believed to be
under different scale of trans-boundary air pollution impact. The optimal topology of
each MLP model used for assessment or prediction was identified by 3-fold cross
validation for two prediction horizons respectively. For assessment work, result from
both prediction horizons shows no remarkable difference. While for prediction work,
performance of all MLP models for 1-day ahead prediction was generally worse than
that for the current-day prediction due to the prolonged prediction horizon.
The preliminary statistical analysis showed the trans-boundary air pollution did exert
different scale of influence on local ozone level in each target air monitoring station,
according to the data for all local and regional ozone influential variables collected
from the whole study area that are defined as Hong Kong territory, Guangdong
Province and part of South China Sea.
MLP trained by automatic relevance determination (named by MLP-ARD), a Bayesian
MLP, was embedded into a two-staged variables selection scheme to assess what were
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Abstract
ii
the ozone key influential variables for each air monitoring station respectively. The
variables selection/assessment result from MLP-ARD was comparable with that from
the best method in the literature. The ozone key influential variables from such
selection scheme will further be used as input variables for MLP models developed
later for ozone prediction.
By comparing the predictive performance of MLP-ARD between with and without
regional ozone influential variables as inputs, it was found that trans-boundary air
pollution exerted the largest impact on Tap Mun (TM), the modest on Tung Chung
(TC), and the least on Tsuen Wan (TW) air monitoring station in Hong Kong. The
result also showed the advantage of MLP-ARD, which provided an interval estimation
for the possible ozone variation, in the prediction for ozone episode days, over the
MLP trained by Levenberg-Marquardt (LM) algorithm (named by MLP-LM), which
only provided a point estimation.
MLP-ARD, MLP-LM as well as most MLP models in the literature were trained by
the gradient-based algorithm, which potentially suffered from local minimum problem.
Two hybrid MLP models, based on the standard particle swarm optimization (PSO)
algorithm, will be developed for avoiding this problem in ozone prediction. The reason
for using hybrid model instead of using MLP trained by standard PSO (named by
MLP-PSO) directly is that standard PSO for MLP training will probably not obtain
good convergence reliability in the high dimension weight space, which could
influence the models‘ performance on the ozone-polluted days.
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Abstract
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Therefore, the aim of two hybrid models was to improve such convergence reliability
by using additional techniques before and after the standard PSO training for MLP
respectively. The first hybrid model was HMC–MLP–PSO. It employed hybrid Monte
Carlo (HMC) method to sample the weight matrix from the posterior probability
distribution of the estimated optimal weight matrix first, and then these sampled
weight matrices were used to initialize ―weight matrix swarm‖ of PSO, before MLP
trained by standard PSO starts. Aiming at exploiting the advantage of both PSO and
LM for MLP training, the other hybrid model was MLP–PSO–LM, which timely
inserted LM to help MLP-PSO avoid stagnation problem.
The performance of two hybrid models was better than MLP-ARD, MLP-LM and
MLP-PSO in terms of several statistics and exceedance indicators. The predictive
performance of all MLP models in this dissertation was finally evaluated. From the
operational point of view, MLP–PSO–LM was recommended for government
authority usage due to its smallest false negative rate for ozone-polluted day
prediction.
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Table of Contents
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TTaabbllee ooff CCoonntteennttss
ABSTRACT ......................................................................................................................................... I
ACKNOWLEDGEMENTS .................................................................................................................... IV
TABLE OF CONTENTS ........................................................................................................................ V
LIST OF FIGURES .............................................................................................................................. IX
LIST OF TABLES ................................................................................................................................ XI
NOMENCLATURE ........................................................................................................................... XIV
CHAPTER 1 GENERAL INTRODUCTION ........................................................................................... 1
1.1 A REGIONAL VIEW OF AIR POLLUTION IN HONG KONG .............................................................................. 1
1.2 BASICS ABOUT TROPOSPHERIC OZONE VARIATION .................................................................................... 5
1.2.1 Ozone Chemistry ................................................................................................................ 5
1.2.2 Meteorology–pollutant interactions .................................................................................. 7
1.3 AIR POLLUTION MODELING ................................................................................................................. 8
1.3.1 A brief review of two categories modeling approach ........................................................ 8
1.3.2 Model comparison works in the literature ....................................................................... 10
1.4 BASICS ABOUT MLP ........................................................................................................................ 12
1.5 TWO CONCEPTS FOR DEVELOPING MLP TRAINING ALGORITHMS ............................................................... 13
1.5.1 Training algorithms in maximum likelihood concept ....................................................... 14
1.5.2 Training algorithms in Bayesian concept ......................................................................... 16
1.6 REVIEW OF OZONE PREDICTION WITH ANN ......................................................................................... 18
1.7 RESEARCH OBJECTIVES ..................................................................................................................... 21
1.8 DISSERTATION OUTLINE .................................................................................................................... 23
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CHAPTER 2 STUDY AREA AND PRELIMINARY ANALYSIS OF FIELD DATA ....................................... 28
2.1 STUDY AREA................................................................................................................................... 29
2.1.1 Hong Kong territory .......................................................................................................... 29
2.1.2 Pearl River Delta region in Guangdong Province ............................................................. 33
2.1.3 North part of South China Sea .......................................................................................... 37
2.2 FIELD DATA ANALYSIS ....................................................................................................................... 38
2.2.1 Missing data ..................................................................................................................... 38
2.2.2 Statistical analysis for the imputed data set .................................................................... 40
2.3 CONCLUSION REMARKS .................................................................................................................... 54
CHAPTER 3 SELECTION AND ASSESSMENT FOR OZONE KEY INFLUENTIAL VARIABLES
SITE-SPECIFICALLY .......................................................................................................................... 57
3.1 SPATIAL INPUT VARIABLES SELECTION, STAGE ONE ................................................................................. 58
3.1.1 Connection weight analysis and sensitivity analysis ........................................................ 58
3.1.2 Performance indicators .................................................................................................... 62
3.1.3 MLP in ARD framework .................................................................................................... 65
3.1.4 Ranking RI for spatial input variables by MLP-ARD for current-day prediction ................ 68
3.1.5 Ranking RI for spatial input variables by NCW method for current-day prediction ......... 85
3.1.6 The key spatial input variables selection for current-day prediction ................................ 88
3.1.7 Ranking RI for spatial input variables by MLP-ARD for 1-day ahead prediction .............. 89
3.1.8 Ranking RI for spatial input variables by NCW method for 1-day ahead prediction ...... 100
3.1.9 The key spatial input variables selection for 1-day ahead prediction ............................ 102
3.2 TEMPORAL INPUT VARIABLES SELECTION, STAGE TWO ........................................................................... 103
3.3 CONCLUSION REMARKS .................................................................................................................. 108
CHAPTER 4 INTERVAL PREDICTION AND THE TRANS- BOUNDARY AIR POLLUTION IMPACT
ASSESSMENT, FOR OZONE VARIATION WITH MLP-ARD MODEL ..................................................... 110
4.1 INTRODUCTION ............................................................................................................................ 110
4.2 METHODOLOGY ........................................................................................................................... 112
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4.2.1 Prediction by MLP-ARD................................................................................................... 112
4.2.2 Experiment setting ......................................................................................................... 114
4.3 RESULTS AND DISCUSSION .............................................................................................................. 115
4.3.1 Topic 1: prediction performance comparison between MLP-ARD and MLP-LM ............ 115
4.3.2 Topic 2: impact assessment for the trans-boundary air pollution on ozone variation
site-specifically.............................................................................................................................. 123
4.4 CONCLUSION REMARKS .................................................................................................................. 137
CHAPTER 5 IMPROVE POINT PREDICTION FOR OZONE POLLUTED DAYS BY USING HMC-MLP-PSO
MODEL 140
5.1 INTRODUCTION ............................................................................................................................ 140
5.2 METHODOLOGY ........................................................................................................................... 144
5.2.1 Hybrid Monte Carlo and MLP weight matrix sampling .................................................. 145
5.2.2 Particle swarm optimization .......................................................................................... 147
5.2.3 HMC-MLP-PSO ................................................................................................................ 150
5.2.4 Experiment setup ............................................................................................................ 150
5.3 RESULTS AND DISCUSSION .............................................................................................................. 152
5.3.1 Initializing strategy comparison ..................................................................................... 152
5.3.2 Models performance comparison with different initializing strategy ............................ 156
5.3.3 Performance comparison between MLP trained by PSO-based algorithms and MLP
trained by the individual-based algorithms .................................................................................. 163
5.3.4 Performance comparison between air monitoring stations during ozone polluted days for
the PSO-trained MLP .................................................................................................................... 164
5.4 CONCLUSION REMARKS .................................................................................................................. 170
CHAPTER 6 IMPROVE POINT PREDICTION FOR OZONE POLLUTED DAYS BY USING MLP-PSO-LM
MODEL 175
6.1 INTRODUCTION ............................................................................................................................ 175
6.2 METHODOLOGY ........................................................................................................................... 177
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6.2.1 MLP model trained with LM algorithm .......................................................................... 177
6.2.2 MLP trained with standard particle swarm optimization (MLP-PSO) ............................ 178
6.2.3 MLP-PSO-LM, MLP alternatively trained by PSO and LM ............................................... 178
6.2.4 Experiment preparation and models setup .................................................................... 179
6.3 RESULTS AND DISCUSSION .............................................................................................................. 180
6.3.1 Comparison of convergence history ............................................................................... 180
6.3.2 Prediction comparison between MLP-PSO-LM, HMC-MLP-PSO and MLP-PSO .............. 185
6.4 CONCLUSION REMARKS .................................................................................................................. 200
CHAPTER 7 CONCLUSIONS AND DISCUSSIONS ........................................................................... 203
7.1 CONCLUSIONS FOR THE SPECIFIC CHAPTERS ........................................................................................ 203
7.2 GENERAL CONCLUSIONS ................................................................................................................. 207
7.3 DISCUSSIONS ............................................................................................................................... 209
7.3.1 Comparative review for prediction result in the literature study ................................... 209
7.3.2 Original Contributions .................................................................................................... 212
7.3.3 Limitations and future study recommendations ............................................................ 213
LIST OF PUBLICATIONS .................................................................................................................. 216
REFERENCE ................................................................................................................................... 217
APPENDIX ..................................................................................................................................... 228
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List of Figures
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LLiisstt ooff FFiigguurreess
Figure 1-1 Annual average of major air pollutants in Hong Kong in four types of monitoring station
................................................................................................................................................ 3
Figure 1-2 A simplified tropospheric ozone dynamic equilibrium without anthropogenic emissions
................................................................................................................................................ 6
Figure 1-3 Ozone accumulation with VOCs or hydrocarbons presented ......................................... 6
Figure 2-1 Study area ................................................................................................................. 29
Figure 2-2 Hong Kong environment and the studied monitoring sites ......................................... 30
Figure 2-3 Distribution of average concentrations of nitrogen dioxide in PRDR-AQMN ................ 35
Figure 2-4 Annual averaged diurnal variation of major pollutants at TW, TM and TC ................... 45
Figure 2-5 Annual averaged diurnal trend of temperature in Hong Kong ..................................... 46
Figure 2-6 Annual averaged weekly variation of major pollutants at TW, TM and TC ................... 48
Figure 3-1 Average performance of MLP-ARD with different topology for ozone prediction ........ 74
Figure 3-2 Convergence process for three hyperparameters in three air monitoring stations ...... 75
Figure 3-3 Contour plot of the ranking matrices for the current day prediction ........................... 78
Figure 3-4 Average performance of MLP-ARD with different topology for ozone prediction ........ 92
Figure 3-5 Convergence for three hyperparameters .................................................................... 93
Figure 3-6 Contour plot of the ranking matrices for the 1 day ahead prediciton .......................... 95
Figure 4-1 Performance comparison between MLP-ARD and MLP-LM in current-day prediction 121
Figure 4-2 Performance comparison between MLP-ARD and MLP-LM in 1-day ahead prediction
............................................................................................................................................ 123
Figure 4-3 Performance comparison between MLP-ARD-OKIV and MLP-ARD-LOIV in current-day
prediction ............................................................................................................................ 133
Figure 4-4 Performance comparison between MLP-ARD-OKIV and MLP-ARD-LOIV in 1-day ahead
prediction ............................................................................................................................ 136
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List of Figures
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Figure 5-1 Random versus HMC initialization (on validation data set, current day prediction) ... 154
Figure 5-2 Random versus HMC initialization (on validation data set, 1 day ahead prediction) .. 155
Figure 6-1 General flowchart of MLP-PSO-LM ........................................................................... 179
Figure 6-2 Comparison of convergence history ......................................................................... 184
Figure 6-3 Models comparison for current and 1 day ahead prediction on test dataset ............. 194
Figure 6-4 Models comparison for current and 1 day ahead prediction on test dataset ............. 197
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List of Tables
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LLiisstt ooff TTaabblleess
Table 1-1 MLP models in this dissertation .................................................................................. 27
Table 2-1 Major sources and pollutants generated in Hong Kong in 2005 .................................... 30
Table 2-2 Contribution of major sectors to regional year pollutant emission inventory ............... 34
Table 2-3 Information of SYNOP stations .................................................................................... 38
Table 2-4 Percentage of missing in original data from year 2001 to 2004 .................................... 40
Table 2-5 Statistics for the collected data (imputed) ................................................................... 41
Table 2-6 Bivariate granger causality test results including the sample 2 , P- value and degree of
freedom ................................................................................................................................ 51
Table 2-7 Number and direction of MSLPG when daily maximum ozone level over 240 3/ mg
.............................................................................................................................................. 53
Table 3-1 List of performance indicators ..................................................................................... 63
Table 3-2 Aggregation for inputs and output for all air monitoring stations for current day
prediction .............................................................................................................................. 69
Table 3-3 Distribution of number of data points in dataset ......................................................... 71
Table 3-4 Ranking result from MLP-ARD with topology scoring the highest 2d in ozone
prediction .............................................................................................................................. 83
Table 3-5 Ranking result from MLP-LM with topology scoring the highest 2d by using NCW
method in ozone prediction ................................................................................................... 87
Table 3-6 The key spatial input variables for each air-monitoring station .................................... 89
Table 3-7 Aggregation for inputs and output for all air monitoring stations for 1 day ahead
prediction .............................................................................................................................. 90
Table 3-8 Distribution of number of data points in dataset ......................................................... 91
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List of Figures
xii
Table 3-9 Ranking result from MLP-ARD with topology scoring the highest 2d in ozone
prediction .............................................................................................................................. 98
Table 3-10 Ranking result from MLP-LM with topology scoring the highest 2d by using NCW
method in ozone prediction ................................................................................................. 101
Table 3-11 The key spatial input variables for each air-monitoring station ................................ 103
Table 3-12 The ozone key influential variables (including key spatial and temporal input variables)
for current-day prediction .................................................................................................... 106
Table 3-13 The ozone key influential variables (key spatial and temporal input variables) for 1-day
ahead prediction ................................................................................................................. 107
Table 4-1 Statistics for models comparison for current and 1 day ahead prediction on test dataset
(MLP-ARD v.s. MLP-LM) ....................................................................................................... 118
Table 4-2 Local key ozone influential variables for current-day prediction ................................ 127
Table 4-3 Local key ozone influential variables for 1-day ahead prediction ................................ 128
Table 4-4 Statistics for models comparison for current-day prediction on test dataset
(MLP-ARD-OKIV v.s. MLP-ARD-LOIV) .................................................................................... 129
Table 4-5 Statistics for models comparison for 1-day ahead prediction on test dataset
(MLP-ARD-OKIV v.s. MLP-ARD-LOIV) .................................................................................... 130
Table 4-6 Difference of 2R between MLP-ARD-OKIV and MLP-ARD-LOIV ............................... 130
Table 5-1 Parameters in HMC and PSO for both HMC-MLP-PSO and MLP-PSO .......................... 152
Table 5-2 Statistics for models comparison for current day prediction (HMC-MLP-PSO v.s.
MLP-PSO, low v.s. high dimension weight space) ................................................................. 158
Table 5-3 Statistics for models comparison for 1 day ahead prediction (HMC-MLP-PSO v.s. MLP-PSO,
low v.s. high dimension weight space) ................................................................................. 160
Table 5-4 Statistics for models comparison for current and 1 day ahead prediction on test dataset
(HMC-MLP-PSO v.s. MLP-PSO) ............................................................................................. 165
Table 5-5 Exceedance prediction with respect to ozone level threshold 120 3/ mg .............. 169
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List of Tables
xiii
Table 6-1 Statistics for models comparison for current and 1 day ahead prediction on test dataset
(MLP-PSO v.s. MLP-PSO-LM) ................................................................................................ 188
Table 6-2 Statistics for models comparison for current and 1 day ahead prediction on test dataset
(HMC-MLP-PSO v.s. MLP-PSO-LM) ....................................................................................... 190
Table 6-3 Exceedance prediction with respect to ozone level threshold 120 3/ mg .............. 198
Table 7-1 Summary of some selected literature models ............................................................ 211
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Nomenclature
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NNoommeennccllaattuurree
Symbols
A Hessian matrix of wS evaluated at MPw
1b input-layer bias group in model MLP-ARD
2b hidden-layer bias group in model MLP-ARD
h
ib bias of neuron i in the hidden layer
o
kb bias of neuron k in the output layer
1C and 2C cognition and social components in PSO algorithm
D dataset
2d Index of agreement
we sum of squares error
DE error function generated by training data in model MLP-ARD
kWE error function generated by weight matrix in model MLP-ARD
f general function representing the MLP‘s mapping from input to output
h
if transfer function of neuron i in the hidden layer
o
kf transfer function of neuron k in the output layer
wxf n ; MLP output with respect to the thn input data
k
iFV fitness value of k
iPo
kGbest the particle with the best fitness value among all the particle population at the
thk generation in
PSO algorithm
h element in the hidden layer
I identity unit matrix
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Nomenclature
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ki Number of weight in the thk weight group in model MLP-ARD
kwJ Jocobian matrix evaluated at weight matrix w in thk iteration
l number of the total data points in D
L number of neurons in the output layer
0L number of leapfrog steps (trajectory length) algorithm in HMC
N number of neurons in the input layer
o element in the output layer
iO the thi measured data corresponding to the thi input data
1k
iPbest the
thi particle‘s position, which gives the best fitness value within 1k generations in PSO
algorithm
wDp ; likelihood function with respect to w
wxOp ;, joint pdf of x and O
k
iPo position of the thi particle at the thk generation
wxOp ;| conditional pdf of O given input x
DwP | posterior probability distribution
xp unconditional pdf of input x
zq normal distribution function
1Q number of the first inner iteration in model MLP-ARD
2Q number of the second inner iteration in model MLP-ARD
1rand and
2rand
two random numbers in PSO algorithm
jRI RI of thj input variable in percentage
2R coefficient of determination
S number of neurons in the hidden layer
0S number of outer iteration in model MLP-ARD
wS total error function in model MLP-ARD
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Nomenclature
xvi
nt the thn target data
v number of weight component
k
iV the velocity of the thi particle at the thk generation in PSO algorithm
maxV velocity limit in PSO algorithm
kwV cumulative error vector
w weight matrix
1w input-layer weight group in model MLP-ARD
2w hidden-layer weight group in model MLP-ARD
MPw most probable values of weight matrix in model MLP-ARD
h
jiw weight that connects the neuron j in the input layer with the neuron i in the hidden layer
o
ikw weight that connects the neuron i in the hidden layer with the neuron k in the output layer
optw optimal weight matrix
x one of MLP input data
ix the thi input data
o
ky output of neuron k in the output layer
sZ normalization factor in model MLP-ARD
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Nomenclature
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Greek Alphabet Symbols
hyperparameter in model MLP-ARD
1b hyperparameter with respect to 1b in model MLP-ARD
2b hyperparameter with respect to 2b in model MLP-ARD
1w hyperparameter with respect to 1w in model MLP-ARD
2w hyperparameter with respect to 2w in model MLP-ARD
jw1 hyperparameter associated with thj input neuron in model MLP-ARD
MP
k most probable values of in the in the thk weight group in model MLP-ARD
GYMSLPG hyperparameter associated with input variables GYMSLPG in model MLP-ARD
xNO hyperparameter associated with input variables NOx in model MLP-ARD
SZAPI hyperparameter associated with input variables SZAPI in model MLP-ARD
MP most probable values of in model MLP-ARD
hyperparameter in model MLP-ARD
k number of well-determined parameters for weight group k in model MLP-ARD
time lag between ozone and key spatial input variables
t standard deviation in the output of model MLP-ARD
time step-size in leapfrog algorithm in HMC
intrinsic data noise with a pdf zq
k useful feature in LM algorithm
0 parameter determining when LM should be inserted into PSO training in model
MLP-PSO-LM
MPw most probable values of weight matrix in model MLP-ARD
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Nomenclature
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Abbreviations
i]-Ave[D previous thi daily average data
10]-[04Ave 1-D average of hourly data from 04:00-10:00 in previous day
ACC auto-correlation coefficient
ACF auto-correlation function
AIC Akaike's information criterion
ANN artificial neural network
AOE number of all observed exceedances.
APE number of all predicted exceedances.
API air pollution index
AQMN Air Quality Monitoring Network
ARD automatic relevance determination
ARIMA regressive integrated moving average
Ave[04-10] average of hourly data from 04:00-10:00
Ave[D-1] previous daily average data
BP back-propagation algorithm
CA cloud amount
CCC cross correlation coefficient
CCF cross correlation function
CO carbon monoxide
CPE number of the correctly predicted exceedances.
CV cross-validation
DF degree of freedom.
DSMSLPG mean sea level pressure gradient at Dongsha with respect to HKIA
exce. Exceedance of defined ozone level
FNR False negative rate
FPR False positive rate
GA genetic algorithm
GDEMC Guangdong Provincial Environmental Protection Monitoring Centre
GYMSLPG mean sea level pressure gradient at Gaoyao with respect to HKIA
GZ Guangzhou
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Nomenclature
xix
H(w, MV) the total energy in HDS
HDS Hamiltonian dynamical system
HKEPD Hong Kong Environmental Protection Department
HKIA Hong Kong international airport
HKO Hong Kong Observatory
HMC hybrid monte carlo method
K(MV) kinetic energy in HMC
LM Levenberg-Marquardt algorithm
LXMSLPG mean sea level pressure gradient at Lianxian with respect to HKIA
MAE Mean absolute error
Max[0-24] daily maximum of hourly data
MBE Mean bias error
ML maximum likelihood
MLP Multilayer perceptron
MLP-ARD-LOIV MLP-ARD only with local ozone influential variables as input variables
MLP-ARD-OKIV MLP-ARD with ozone key influential variables as input variables
MSE Mean square error
MSLP mean sea level pressure
MSLPG mean sea level pressure gradient
MV momentum vector in HMC
NCW new connection weight method
NO nitric oxide
NO2 nitrogen dioxide
NOx nitrogen oxide
O3 ozone
obs. observations
NFr normalized Spearman‘s footrule
PCA principal component analysis
pdf probability density function
PRDR Pearl River Delta region
PSO particle swarm optimization
purelin linear transfer function
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Nomenclature
xx
QYMSLPG mean sea level pressure gradient at Qingyuan with respect to HKIA
iRank j rank number for the thi variable in the thj variables ranking realization
RH relative humidity
RI relative importance
RK
rank with respect to RI, from the most important variable or high rank to the least
one or low rank
RMSE Root mean square error
RSP respirable suspended particulates
SCG scaled conjugate gradient algorithm
SI success index
SO2 sulphur Dioxide
SR solar radiation
STMSLPG mean sea level pressure gradient at Shantou with respect to HKIA
SYNOP surface synoptic observation stations
SZ Shenzhen
SZMSLPG: mean sea level pressure gradient at Shenzhen with respect to HKIA.
T temperature
tansig hyperbolic tangent function
TC Tung Chung air-monitoring station
TM Tap Mun air-monitoring station
TNR True negative rate
TPR True positive rate
TS
number of times for corresponding variable ranked at some rank such position within
30 MLP-ARD runs.
TSP total suspended particulate
TW Tsuen Wan air-monitoring station
USEPA United States Environmental Protection Agency
VAR vector autoregressive
VC Vapnik-Chervonenkis
VOCs volatile organic compounds
WD wind direction
WS wind speed
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Nomenclature
xxi
ZH Zhuhai
ZJMSLPG: mean sea level pressure gradient at Zhanjiang with respect to HKIA