Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

Torres, Rita Cabello; Estela, Manuel Angel Ponce; Sánchez-Ccoyllo, Odón R.; Romero-Cabello, Edison Alessandro; Ávila, Fausto Fernando García; Castañeda-Olivera, Carlos Alberto; Valdiviezo-Gonzáles, Lorgio; Eulogio, Carlos Enrique Quispe; Cruz, Alex Rubén Huamán De La; López-Gonzales, Javier Linkolk

doi:10.1038/s41598-022-20904-2

Cited by 12 publications

(10 citation statements)

References 45 publications

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“…Further, for the testing results, R 2 and R for ANN were 94% and 97%, while the results for MLR were 97% and 98% accuracy. We can present and organize the findings from our predictive comparison in the following way: Regarding the prediction of COVID-19, MLR was superior to ANN, and this result is similar to the findings of [ 6 , 7 , 23 , 24 , 33 , 34 ]. Additionally, ref.…”

Section: Application Of Results and Discussionsupporting

confidence: 72%

“…It is crucial to compare and contrast the rates of positive cases, the number of recoveries, the comparison of mortality cases, evaluate the effects of the vaccines, and examine other factors affecting the spread of this virus due to a lack of test kits, ventilators, oxygen tanks, hospital beds, and proper treatment or vaccine. In a similar vein, adequate preparations can be made to reduce casualties and improve situational awareness [ 7 ]. For instance, the government can prepare for the expected number of cases up until a certain day by analyzing the data in this study and deciding, in advance, what kind of medical supplies are needed or what kind of precautions can be taken to reduce the number of casualties.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

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The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

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Section: Application Of Results and Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

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“…However, further studies may be needed to improve the fit by incorporating more input variables that have not yet been investigated due to lack of data and information. Alternatively, in the context of COVID-19, it would also be interesting to evaluate and model the behavior of ozone, using statistical techniques such as multiple linear regression, use of three-dimensional logarithms and principal component analysis, under the influence of meteorological variables in the warm and cold season, similar to what was developed for PM 52 , obtaining important reports for decision-making in environmental management.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru

Carbo-Bustinza

Belmonte

Jimenez

et al. 2022

Sci Rep

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The main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in Metropolitan Lima during the years 2017, 2018 and 2019. Exploratory, correlational and predictive approaches are presented. The exploratory results showed that ATE is the station with the highest prevalence of ozone pollution. Likewise, in an hourly scale analysis, the pollution peaks were reported at 00:00 and 14:00. Finally, the machine learning models that showed the best predictive capacity for adjusting the ozone concentration were the linear regression and support vector machine.

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“…Machine learning models such as deep learning and artificial neural networks can also be considered part of the current forecasting decompositioncombination technique. It can also be extended and applied to other approaches and datasets (for example, energy [1,3], air pollution [67][68][69][70], solid waste [71] and academic performance [72]).…”

Section: Conclusion and Future Work Directionsmentioning

confidence: 99%

Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique

Iftikhar,

Turpo-Chaparro,

Canas Rodrigues

et al. 2023

Energies

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Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges.

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Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

Cited by 12 publications

References 45 publications

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru

Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique

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