Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks

Balaguer‐Ballester, Emili; Valls, Gustavo Camps; Carrasco-Rodriguez, J. L.; Soria-Olivas, Emilio; Valle-Tascón, Secundino del

doi:10.1016/s0304-3800(02)00127-8

Cited by 58 publications

(8 citation statements)

References 29 publications

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“…8 Cross validation of community succession modeling using multivariate linear regression differential equation. This conclusion supports the argumentation that neural network models were more effective in time series prediction than previous procedures based on dynamical system theory (Balaguer Ballester et al 2002;Zhang et al 2007). …”

Section: Ordinary Differential Equation Modelingsupporting

confidence: 87%

Spatial succession modeling of biological communities: a multi-model approach

Zhang

Wei

2008

Environ Monit Assess

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Strong spatial correlation may exist in the spatial succession of biological communities, and the spatial succession can be mathematically described. It was confirmed by our study on spatial succession of both plant and arthropod communities along a linear transect of natural grassland. Both auto-correlation and cross-correlation analyses revealed that the succession of plant and arthropod communities exhibited a significant spatial correlation, and the spatial correlation for plant community succession was stronger than arthropod community succession. Theoretically it should be reasonable to infer a site's community composition from the last site in the linear transect. An artificial neural network for state space modeling (ANNSSM) was developed in present study. An algorithm (i.e., Importance Detection Method (IDM)) for determining the relative importance of input variables was proposed. The relative importance for plant families Gramineae, Compositae and Leguminosae, and arthropod orders Homoptera, Diptera and Orthoptera, were detected and analyzed using IDM. ANNSSM performed better than multivariate linear regression and ordinary differential equation, while ordinary differential equation exhibited the worst performance in the simulation and prediction of spatial succession of biological communities. A state transition probability model (STPM) was proposed to simulate the state transition process of biological communities. STPM performed better than multinomial logistic regression in the state transition modeling. We suggested a novel multi-model framework, i.e., the joint use of ANNSSM and STPM, to predict the spatial succession of biological communities. In this framework, ANNSSM and STPM can be separately used to simulate the continuous and discrete dynamics.

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Section: Ordinary Differential Equation Modelingsupporting

confidence: 87%

Spatial succession modeling of biological communities: a multi-model approach

Zhang

Wei

2008

Environ Monit Assess

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“…It is shown that neural networks show better performances against MLR [19,20,21,22,23,24,25]. Artificial neural networks (ANN) have the advantages of incorporating complex nonlinear relationships between the concentration of air pollutants and the corresponding meteorological variables, and are widely used for the prediction of air pollutants concentration.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

Zhang

Ding

2017

IJERPH

108

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With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R2 increased and root mean square error values decreased respectively.

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“…The dataset consists of hourly concentrations of ozone, meteorological variables and other atmospheric pollutants ( Text S2 ). Ozone time series are well known-to exhibit daily periodicity which is modulated by a subtle seasonal trend [62] , [63] ; thus they will serve to benchmark further simulation results before the analysis of neural data in the next section.…”

Section: Resultsmentioning

confidence: 99%

Can We Identify Non-Stationary Dynamics of Trial-to-Trial Variability?

2014

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Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings.

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Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks

Cited by 58 publications

References 29 publications

Spatial succession modeling of biological communities: a multi-model approach

Spatial succession modeling of biological communities: a multi-model approach

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

Can We Identify Non-Stationary Dynamics of Trial-to-Trial Variability?

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