Time series analysis in acid rain modeling: Evaluation of filling missing values by linear interpolation

Terry, W. Robert; Lee, J.B.; Kumar, Ashok

doi:10.1016/0004-6981(86)90335-5

Cited by 24 publications

(16 citation statements)

References 7 publications

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“…The missing values (3%) were determined using the linear interpolation method as proposed by Terry et al . [3]. A database of 1,271 hourly data points was thus developed.…”

Section: Database Developmentmentioning

confidence: 99%

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Effects of an organic phosphorus nucleating agent on crystallization behaviors and mechanical properties of poly(propylene)

Gui

Xin

Zhu

et al. 2003

J of Applied Polymer Sci

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ABSTRACT:The effects of an organic phosphorus nucleating agent on the crystallization behavior and mechanical properties of poly(propylene) were investigated. As the concentration of nucleating agent increased from 0 to 0.8%, the tensile and flexural strengths of the nucleated poly(propylene) resin increased by 15%, the flexural modulus increased by 35%, the crystallization peak temperature increased by 10°C, and the nucleation density (calculated with the nonlinear Voltrra integral equation) increased by 10 6 times. Mechanical properties increased with nucleation density. Linear equations between the mechanical properties and the logarithmic nucleation density were revealed.

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“…The missing values (3%) were determined using the linear interpolation method as proposed by Terry et al . [3]. A database of 1,271 hourly data points was thus developed.…”

Section: Database Developmentmentioning

confidence: 99%

“…Time series analysis has been adopted by researchers worldwide in forecasting the atmospheric pollutants on a short‐term basis with acceptable results [3–10]. These studies mainly used the ARMA/ARIMA and kolmogorov–zurbenko (KZ) filtering techniques to model the atmospheric pollutant concentrations.…”

Section: Introductionmentioning

confidence: 99%

Effects of an organic phosphorus nucleating agent on crystallization behaviors and mechanical properties of poly(propylene)

Gui

Xin

Zhu

et al. 2003

J of Applied Polymer Sci

View full text Add to dashboard Cite

show abstract

“…The database used in the development of hybrid BPNN MTS models is the same as the database (May 8, 2007-June 29, 2007) outlined by Kadiyala and Kumar [15]. The missing values (3%) were determined using the linear interpolation method as proposed by Terry et al [1]. The missing values (3%) were determined using the linear interpolation method as proposed by Terry et al [1].…”

Section: Database Developmentmentioning

confidence: 99%

Multivariate time series based back propagation neural network modeling of air quality inside a public transportation bus using available software

Kadiyala

Kumar

2015

Env Prog and Sustain Energy

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Time series and artificial neural networks (ANNs) are two distinct methodologies that present environmental researchers and managers with the resources to develop reliable and accurate indoor air quality (IAQ) models. The development of valid IAQ models is a fundamental component in the development of risk assessment and mitigation plans that ensure an occupant's exposure to indoor air contaminants are within the permissible IAQ guidelines. This software review paper provides a detailed step‐by‐step description of the methodology on how one may integrate the use of the multivariate time series and the back propagation neural network (widely used ANN) methods simultaneously in the development of valid in‐bus carbon dioxide and carbon monoxide models using available software. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1259–1266, 2015

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“…The missing values (3%) were determined using the linear interpolation method as proposed by Terry et al . . A complete database of 1,271 hourly data points with no missing values was thus developed.…”

Section: Database Deveopmentmentioning

confidence: 99%

“…Table 3 presents the list of the 12 independent variables considered to have an impact on in-bus CO 2 and CO concentrations for VTS modeling. The missing values (3%) were determined using the linear interpolation method as proposed by Terry et al [5]. A complete database of 1,271 hourly data points with no missing values was thus developed.…”

Section: Database Deveopmentmentioning

confidence: 99%

Vector‐time‐series‐based back propagation neural network modeling of air quality inside a public transportation bus using available software

Kadiyala

Kumar

2015

Env Prog and Sustain Energy

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This software review article describes the development of hybrid indoor air quality (IAQ) models by integrating the use of vector time series (VTS) and back propagation neural network (BPNN) modeling approaches. BPNNs are the most widely adopted artificial neural networks that serve as universal approximators and provide a flexible computational platform to integrate conventional modeling approaches like time series in developing hybrid environmental prediction (or forecasting) models. The hybrid VTS‐based BPNN IAQ prediction models developed and validated in this study using available software are based on the monitoried in‐bus contaminants of carbon dioxide and carbon monoxide. © 2015 American Institute of Chemical Engineers Environ Prog, 35: 7–13, 2016

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Time series analysis in acid rain modeling: Evaluation of filling missing values by linear interpolation

Cited by 24 publications

References 7 publications

Effects of an organic phosphorus nucleating agent on crystallization behaviors and mechanical properties of poly(propylene)

Effects of an organic phosphorus nucleating agent on crystallization behaviors and mechanical properties of poly(propylene)

Multivariate time series based back propagation neural network modeling of air quality inside a public transportation bus using available software

Vector‐time‐series‐based back propagation neural network modeling of air quality inside a public transportation bus using available software

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