Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models

Amid, Sama; Gundoshmian, Tarahom Mesri

doi:10.1002/ep.12448

Cited by 73 publications

(44 citation statements)

References 31 publications

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“…These evaluation criteria included coefficient of determination ( R 2 ), the root mean squared error (RMSE), mean absolute percentage error (MAPE), and model efficiency (EF). These are defined as Equations :

R^{2} = \frac{{((), \sum_{i = 1}^{n} ((), E_{ai} - \overset{E_{italicai}}{true}) \times ((), E_{pi} - \overset{E_{italicpi}}{true}))}^{2}}{\sum_{i = 1}^{n} {(E_{italicai} - \overset{false¯}{E_{ai}})}^{2} \times \sum_{i = 1}^{n} {(E_{italicpi} - \overset{false¯}{E_{pi}})}^{2}}

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {((), E_{ai} - E_{pi})}^{2}}{n}}

MAPE = \frac{1}{n} \sum_{i = 1}^{n} (||, \frac{E_{italicai} - E_{italicpi}}{E_{ai}}) \times 100

EF = 1 - \frac{\sum_{i = 1}^{n} {(E_{italicai} - E_{italicpi})}^{2}}{\sum_{i = 1}^{n} {(E_{italicai} - \overset{false¯}{E_{pi}})}^{2}}

where, E a and E p are the actual and the predicted energy output, respectively, and i (1,…, n ) is the number of patterns. The model with the smallest RMSE and MAPE, but the largest EF and R 2 is considered to be an optimal model .…”

Section: Methodsmentioning

confidence: 99%

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Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

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There are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13130, 2019

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R^{2} = \frac{{((), \sum_{i = 1}^{n} ((), E_{ai} - \overset{E_{italicai}}{true}) \times ((), E_{pi} - \overset{E_{italicpi}}{true}))}^{2}}{\sum_{i = 1}^{n} {(E_{italicai} - \overset{false¯}{E_{ai}})}^{2} \times \sum_{i = 1}^{n} {(E_{italicpi} - \overset{false¯}{E_{pi}})}^{2}}

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {((), E_{ai} - E_{pi})}^{2}}{n}}

MAPE = \frac{1}{n} \sum_{i = 1}^{n} (||, \frac{E_{italicai} - E_{italicpi}}{E_{ai}}) \times 100

EF = 1 - \frac{\sum_{i = 1}^{n} {(E_{italicai} - E_{italicpi})}^{2}}{\sum_{i = 1}^{n} {(E_{italicai} - \overset{false¯}{E_{pi}})}^{2}}

Section: Methodsmentioning

confidence: 99%

“…These evaluation criteria included coefficient of determination (R 2 ), the root mean squared error (RMSE), mean absolute percentage error (MAPE), and model efficiency (EF). These are defined as Equations 1-4 [93]:…”

Section: Performance Indicesmentioning

confidence: 99%

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

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“…The ANFIS was introduced by Jang (1991) and since then is has been used for controlling, parameter estimation and modeling in complex systems (Amid & Mesri Gundoshmian, 2017). The ANFIS is a combination of artificial neural network (ANN) and fuzzy inference system (FIS).…”

Section: Adaptive Neural-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Forecasting Attendance Demand of Sports Games

Şahin¹

2018

Journal of International Scientific Researches

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Demand information is a major tool for decision makers and sports administrators. It is also required by dynamic ticket pricing models of sports events, which have been an increasingly popular research topic. It can be inferred that the accuracy of the demand forecasting plays a crucial role on critical decisions. Considering this point, soft computing techniques, artificial neural networks and adaptive neuro-fuzzy inference system (ANFIS), are utilized to forecast attendance demand of sports games in this study. Classical regression models have been used in almost all of the studies conducted for the same purpose. It is aimed to propose competitive alternative forecasting approaches. To do so, multiple models are developed to forecast attendance demand rate of sports events. To generalize the use of the proposed approaches, as a general term, the demand rate is considered. By multiplying the demand rate by the total number of tickets offered for sale, the number of spectators (attendance demand) is obtained. The proposed approaches can be used for almost all sports disciplines by making some modifications. Demand factors of indoor and outdoor sports games may differ. Therefore, the determinants of demand are selected accordingly. The real data of the sports clubs are used to evaluate the forecasting results of the models. The mean absolute percentage error (MAPE) is used for that purpose. As a result, the MAPE values of the proposed models are below %10 that means the proposed models can be utilized for demand forecasting purposes. The proposed models may be utilized in other industries as well.

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“…Agricultural production is dependent upon the consumption of farm inputs. Due to increasing global population, and therefore a more limited supply of agricultural land and a desire for better standards of living, energy use in the agricultural sector has increased over the last few decades [2,3].…”

Section: Introductionmentioning

confidence: 99%

An integrated analysis of non‐renewable energy use, GHG emissions, carbon efficiency of groundnut sole cropping and groundnut‐bean intercropping agro‐ecosystems

Firouzi

Nikkhah

Rosentrater

2017

Env Prog and Sustain Energy

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The aim of this study was to investigate the non‐renewable energy use efficiency, greenhouse gas (GHG) emissions, and carbon efficiency of groundnut sole cropping and groundnut‐bean intercropping agro‐ecosystems. Data were collected from 136 farmers using face‐to‐face questionnaires in the Kiashahr region of northern Iran. The results analysis suggested that the non‐renewable energy ratio of groundnut‐bean intercrop agro‐ecosystem (6.03) was greater than that of groundnut sole cropping system (4.59). The highest share of non‐renewable energy belonged to diesel fuel, which was followed by the nitrogen fertilizer for both systems. This demonstrates the importance of fossil fuel and N fertilizer use management in both agro‐ecosystems. The total GHG emissions from groundnut and groundnut‐bean intercrop agro‐ecosystems were computed as 636.14 and 657.36 kgCO2eq ha−1, respectively. The highest impact of GHG emissions was from diesel fuel within both farming systems, confirming the prominent role of the fossil fuel input again. The carbon efficiency for groundnut‐bean intercrop agro‐ecosystem (14.78) was greater than that of the groundnut sole cropping system (11.14). Therefore, groundnut‐bean agro‐ecosystem can be observed to be more effective for carbon sequestration. The use of greater amounts of biological resources helps to improve the efficiency of energy use and mitigation of GHG emissions in both agro‐ecosystems. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1832–1839, 2017

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Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models

Cited by 73 publications

References 31 publications

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Forecasting Attendance Demand of Sports Games

An integrated analysis of non‐renewable energy use, GHG emissions, carbon efficiency of groundnut sole cropping and groundnut‐bean intercropping agro‐ecosystems

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