Ridge regression as efficient model selection and forecasting of fish drying using v-groove hybrid solar drier

Lim Hui Yin, . and Fam Pei Shan, . and Javaid Anam, . and Majahar Ali Majid Khan, . Ridge regression as efficient model selection and forecasting of fish drying using v-groove hybrid solar drier. pp. 1179-1202. ISSN 2231-8526

Application of the Internet of things (IoT) for data collection in solar drying can be very efficient in collecting big data of drying parameters. There are many variables involved so it is hard to find a model to predict the moisture content of the food product during drying. In model building interaction terms should be incorporated because they also contribute to the model. Eight selection criteria (8SC) is a very useful method in model building. This study applied ordinary least squares (OLS) regression and ridge regression with 8SC in model building to predict the moisture content of drying fish. A total of eighty models were considered in this study. One best model was chosen each from OLS regression and ridge regression. M78.7.3 with a total of eleven independent variables was the best OLS model after conducting multicollinearity and coefficient test. Next the best ridge model M56.0.0 was obtained after the coefficient test. The mean absolute percentage error (MAPE) was used to measure the accuracy of the prediction model. For OLS model M78.7.3 the MAPE value was 15.7342. The MAPE value for ridge model M56.0.0 was 17.4054. From the MAPE value OLS model M78.7.3 provided a better estimation than the ridge model M56.0.0. However OLS model M78.7.3 violated the normality assumptions of residuals. This is highly caused by the outlier problem. So due to non- normality of the residuals and presence of outliers in the dataset ridge regression is preferred for the best forecast model.

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Abstract

Application of the Internet of things (IoT) for data collection in solar drying can be very efficient in collecting big data of drying parameters. There are many variables involved so it is hard to find a model to predict the moisture content of the food product during drying. In model building interaction terms should be incorporated because they also contribute to the model. Eight selection criteria (8SC) is a very useful method in model building. This study applied ordinary least squares (OLS) regression and ridge regression with 8SC in model building to predict the moisture content of drying fish. A total of eighty models were considered in this study. One best model was chosen each from OLS regression and ridge regression. M78.7.3 with a total of eleven independent variables was the best OLS model after conducting multicollinearity and coefficient test. Next the best ridge model M56.0.0 was obtained after the coefficient test. The mean absolute percentage error (MAPE) was used to measure the accuracy of the prediction model. For OLS model M78.7.3 the MAPE value was 15.7342. The MAPE value for ridge model M56.0.0 was 17.4054. From the MAPE value OLS model M78.7.3 provided a better estimation than the ridge model M56.0.0. However OLS model M78.7.3 violated the normality assumptions of residuals. This is highly caused by the outlier problem. So due to non- normality of the residuals and presence of outliers in the dataset ridge regression is preferred for the best forecast model.

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Item Type:	Article
AGROVOC Term:	Fish
AGROVOC Term:	Drying
AGROVOC Term:	Solar drying
AGROVOC Term:	Forecasting
AGROVOC Term:	Prediction
AGROVOC Term:	Regression analysis
AGROVOC Term:	Mathematical models
AGROVOC Term:	Application of technology
AGROVOC Term:	Moisture content
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:54
URI:	http://webagris.upm.edu.my/id/eprint/9355

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