Applicability of statistical and machine learning–based regression algorithms in modeling of carbon dioxide emission in experimental pig barns

Basak, Jayanta Kumar; Kim, Na Eun; Shahriar, Shihab Ahmad; Paudel, Bhola; Moon, Byeong Eun; Kim, Hyeon Tae

doi:10.1007/s11869-022-01225-9

Cited by 9 publications

(5 citation statements)

References 61 publications

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“…From Fig 3, there was a positive correlation between growthrelated factors and body weights, specifically the height of pig (HP), age (AG), length (LP) and girth length (GL). Other researchers have reported similar findings, in where FI (Pierozan et al, 2016), DW (Arulmozhi et al, 2020), HP (Yang et al, 2019), AG (Birteeb et al, 2015), LP (Banik et al, 2021), GL (Banik et al, 2021) and RCO 2 (Basak et al, 2022) are highly associated with pig's body weight. In addition, a negative correlation existed between PBW and the pig's body temperature (PBT).…”

Section: Input Variables Selectionsupporting

confidence: 60%

“…non-linear models such as ANNs, Bayesian classification and genetic expression are acceptable when the variables under consideration have complicated and non-linear correlation (Basak et al, 2020;Basak et al, 2022). It has been noted that non-linear methods accurately interpret variable relationship and are better able to predict output variables than linear-based approaches (Basak et al, 2022).…”

Section: Applications Of Artificial Neural Network and Multiple Linea...mentioning

confidence: 99%

“…MLR model aims to represent the linear association between two or more explanatory variables and one response variable (Basak et al, 2022). Equation ( 2) was employed to develop MLR model: W here, y i = Body weight.…”

Section: Development Of Ann and Mlr Model And Data Analysismentioning

confidence: 99%

“…The experimental result indicated that the best performance was achieved when hidden layers had three nodes (Wang et al, 2008). It was also noted that a higher level of uncertainty in a model is directly connected to the individual parameter coefficient and the architecture of ANN models (Arulmozhi et al, 2021;Basak et al, 2022;Jaihuni et al, 2022).…”

Section: Ffbp and Mlr Model Performancementioning

confidence: 99%

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Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Basak¹,

Paudel²,

Deb³

et al. 2023

IJAR

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Background: Experiments were conducted to analyze environmental parameters and growth-related factors to identify the most influential factors in estimating pig’s body weight (PBW) using artificial neural networks (ANNs) and multiple linear regression (MLR)-based models. Methods: Four different ANNs, including Cascade Forward Back-propagation (CFBP), Layer Recurrent (LR), Feed Forward Back-propagation (FFBP) and Elman (EL), and MLR models were developed to estimate the body weight of pigs. The current research was conducted for 92 days during the two experimental periods (2021–2022) in a pig barn. Result: The Levenberg-Marquardt training function, gradient descent weight and bias learning function, tan-sigmoid transfer function, and two hidden layers with 16 neurons in each layer were shown to be the most effective architecture of the FFBP model in predicting PBW. According to the sensitivity analysis, length of pig (LP) was the most influential factor in estimating the PBW for MLR/ANN models. However, the environmental parameters along with growth-related factors could not always be the same association with PBW. Therefore, further research on viable alternative breeds with different management conditions may be considered to evaluate MLR and ANN model’s performance.

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Section: Input Variables Selectionsupporting

confidence: 60%

Section: Applications Of Artificial Neural Network and Multiple Linea...mentioning

confidence: 99%

Section: Development Of Ann and Mlr Model And Data Analysismentioning

confidence: 99%

Section: Ffbp and Mlr Model Performancementioning

confidence: 99%

See 2 more Smart Citations

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Basak¹,

Paudel²,

Deb³

et al. 2023

IJAR

View full text Add to dashboard Cite

show abstract

“…Gautam et al [92] developed an offline prediction model to estimate future thermal conditions from building data collected in operation. Basak et al [93,94] used statistical and machine-learning methods to model CH 4 and CO 2 emissions from pig manure. Rodriguez et al [95] developed a CO 2 emission prediction model using neural networks.…”

Section: Applications Of Machine Learningmentioning

confidence: 99%

Environmental Factor Detection and Analysis Technologies in Livestock and Poultry Houses: A Review

et al. 2023

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The environment in livestock and poultry houses plays an important role in the growth and reproduction of livestock and poultry. In order to obtain the environmental conditions of livestock and poultry houses in a timely and reliable manner, and eliminate adverse environmental factors, scholars have been exploring various methods to obtain and predict environmental factors. This paper reviewed the literature from the last 10 years, specifically focusing on technologies for detecting environmental factors in livestock and poultry houses, which can be mainly divided into three categories: research on the environmental monitoring and control of livestock and poultry houses based on detection equipment and wireless sensor technology; research on the distribution and regularity of environmental factors in livestock and poultry houses based on a mathematical model; research on the environmental simulation and detection of livestock and poultry houses based on computer technology. The current testing methods have their advantages and disadvantages. When studying environmental factors, researchers should choose the most appropriate method for data acquisition according to the actual situation. The proposed recommendations for achieving this goal are as follows: (1) The control of environmental factors should be combined with the physiological response of livestock and poultry. The needs of animals should be considered; (2) Novel approaches need to be developed to integrate energy requirements into the environmental regulation of livestock and poultry houses; (3) It is necessary to research and develop control models and strategies that can predict the environment in the houses, and the transient simulation method should be further explored; (4) Improve environmental detection and control accuracy through the coupling of different technologies.

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Don’t look earth: environmental taxes effect on Co2 emissions, evidence from moments quantile regression for EU countries

Özmen,

Mutascu

2023

Environ Dev Sustain

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Applicability of statistical and machine learning–based regression algorithms in modeling of carbon dioxide emission in experimental pig barns

Cited by 9 publications

References 61 publications

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Environmental Factor Detection and Analysis Technologies in Livestock and Poultry Houses: A Review

Don’t look earth: environmental taxes effect on Co2 emissions, evidence from moments quantile regression for EU countries

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