Modeling of air pollution in residential and industrial sites by integrating statistical and Daubechies wavelet (level 5) analysis

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“…The decomposition of f ( t ) into approximation and detail components is also classified in Fig. 1 [52] .…”

Section: Wavelet Analysismentioning

confidence: 99%

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Singh

Parmar

Kumar

et al. 2020

Chaos, Solitons & Fractals

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136

103

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“…Nourani & Parhizkar (2013) stated that the guideline for choosing the appropriate mother wavelet should be based on similarity in shape between it and the time-series. The Daubechies-5 (Db5) wavelet has been used extensively, since its wavelet coefficients can capture the maximum amount of signal energy (He et al 2008;Parmar & Bhardwaj 2012;Soni et al 2017). In addition, the shape of the Db5 wavelet is similar to the water quality parameter time-series, so it was used in this study.…”

Section: Wavelet Analysismentioning

confidence: 99%

“…Decomposition level is often chosen based on the data length, and to yield a desired low-pass cutoff frequency (Soni et al 2017). Given a set of daily data, decomposition leads to 2 n -day mode resolutions (e.g., 2 1 -day mode, 2 2 -day mode, 2 3 -day mode, which is roughly weekly, 2 4 -day mode, 2 5 -day mode, which is roughly monthly, and…, 2 8 -day mode, which is roughly annually, etc.…”

Section: Wavelet Analysismentioning

confidence: 99%

Wavelet and statistical analyses of river water quality parameters: a case study in the Lower Minnesota River

Gao

Zhu

et al. 2018

Water Practice and Technology

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Statistical and wavelet analyses are useful tools for analyzing river water quality parameters. In this study, they were employed to study parameters including biochemical oxygen demand (BOD), dissolved oxygen (DO), nitrate (NO3), ammonium (NH4), phosphate (PO4), total phosphorus (TP), total Kjeldahl nitrogen (TKN), chlorophyll a (CHLA), total suspended solids (TSS) and water temperature (TEMP) monitored at five hydrologic stations on the Lower Minnesota River, USA. Strong positive correlations were observed between CHLA-BOD, TP-TKN, TP-TSS and TKN-TSS, with strong negative correlation between DO-TEMP. Daubechies wavelet at level 5 has been calculated for some key water quality parameters as it gives the finer scale approximation and decomposition of each water parameter. The results show that TEMP and DO have relative quasi-periodicity of about one year, while the quasi-periodicity of NH4 and PO4 are weaker than for TEMP and DO. Correlations between some parameters based on wavelet decomposition results are consistent. The fluctuation range characteristics of some parameters were also analyzed through wavelet decomposition.

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“…Autoregressive integrated moving average (ARIMA) model is based on Box-Jenkins ( 1976 ) method which is a commonly used time series analysis model. ARIMA model is applied to a wide variety of time series data including stationary, non-stationary, and seasonal (periodic) time series (Melard and Pasteels 2000 ; Valenzuela et al 2008 ; Parmar and Bhardwaj 2013 , 2014 , 2015 ; Soni et al 2014 , 2015 , 2016 , 2017 ; Kumar et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 pervasiveness in six major affected states of India and two-stage variation with temperature

Singh

Parmar

Kaur

et al. 2021

Air Qual Atmos Health

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Coronavirus disease knocked in Wuhan city of China in December 2019 which spread quickly across the world and infected millions of people within a short span of time. COVID-19 is a fast-spreading contagious disease which is caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2). Accurate time series forecasting modeling is the need of the hour to monitor and control the universality of COVID-19 effectively, which will help to take preventive measures to break the ongoing chain of infection. India is the second highly populated country in the world and in summer the temperature rises up to 50°, nowadays in many states have more than 40° temperatures. The present study deals with the development of the autoregressive integrated moving average (ARIMA) model to predict the trend of the number of COVID-19 infected people in most affected states of India and the effect of a rise in temperature on COVID-19 cases. Cumulative data of COVID-19 confirmed cases are taken for study which consists of 77 sample points ranging from 1st March 2020 to 16th May 2020 from six states of India namely Delhi (Capital of India), Madya Pradesh, Maharashtra, Punjab, Rajasthan, and Uttar Pradesh. The developed ARIMA model is further used to make 1-month ahead out of sample predictions for COVID-19. The performance of ARIMA models is estimated by comparing measures of errors for these six states which will help in understanding future trends of COVID-19 outbreak. Temperature rise shows slightly negatively correlated with the rise in daily cases. This study is noble to analyse the variation of COVID-19 cases with respect to temperature and make aware of the state governments and take precautionary measures to flatten the growth curve of confirmed cases of COVID-19 infections in other states of India, nearby countries as well.

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Modeling of air pollution in residential and industrial sites by integrating statistical and Daubechies wavelet (level 5) analysis

Cited by 18 publications

References 25 publications

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Wavelet and statistical analyses of river water quality parameters: a case study in the Lower Minnesota River

Prediction of COVID-19 pervasiveness in six major affected states of India and two-stage variation with temperature

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