PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study

Nieto, P.J. Garcı́a; Lasheras, Fernando Sánchez; García–Gonzalo, Esperanza; Juez, Francisco Javier de Cos

doi:10.1016/j.scitotenv.2017.11.291

Cited by 145 publications

(33 citation statements)

References 23 publications

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“…Conversely, if it cannot reject the hypothesis, the dataset is not stationary, and therefore ARIMA should be adopted. The difference should be conducted multiple times on training data in ARIMA to ensure a stationary series for the next step (Li and Chau 2016;Mollison 1977;Riley 2007;Valipour et al 2013;Nieto et al 2018). The flowchart is shown in Figure 7.…”

Section: Infections and Deathsmentioning

confidence: 99%

Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy

Yue

Shao

et al. 2020

JRFM

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This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 . It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter's Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China's economy will continue to maintain steady development.

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Section: Infections and Deathsmentioning

confidence: 99%

Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy

Yue

Shao

et al. 2020

JRFM

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“…According to the experiments, the multilayer perceptron algorithm has the ability to represent complex simulations and mapping and process high level non-linear problems. Also, the MLP algorithm has the ability to process the nonlinear features, thus allowing the representation of a continuous function of non-linear activation functions such as sigmoid functions [44], which has a clear analogy with the conventional representation of a periodic function such as a Fourier series (i.e., as the sum of simple sine waves). Therefore, the MLP can be considered as a universal functional approximation.…”

Section: Multilayer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

“…The MLP is considered as a feed-forward system with single or multiple layer of segments among network output and input layers [38][39][40][41][42][43][44][45]. With the assumption of L-layer MPL, the system can signified by N L n 0 ,n 1 ,..., n L , where n l , l = 0, 1, .…”

Section: Multilayer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

et al. 2019

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Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.

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“…In terms of ecology, Kumar et al [58] applied ARIMA to the prediction of atmospheric pollutants (Ozone, carbon monoxide, nitric oxide, nitrogen dioxide) and could effectively predict short-term atmospheric pollutants. Nieto et al [59] used four models to predict PM10 concentration, including ARIMA. Aasim et al [60] proposed combined repeated wavelet transform and ARIMA to forecast short-term wind speed.…”

Section: Review Of Arima Modelmentioning

confidence: 99%

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Han

2019

Energies

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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

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PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study

Cited by 145 publications

References 23 publications

Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy

Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

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