Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests
Autor: | B V Balaji Prabhu, M. Dakshayini |
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Rok vydání: | 2020 |
Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network Computer Networks and Communications business.industry Computer science Regression analysis 02 engineering and technology 01 natural sciences Agriculture 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing business 0105 earth and related environmental sciences |
Zdroj: | International Journal of Grid and High Performance Computing. 12:35-47 |
ISSN: | 1938-0267 1938-0259 |
DOI: | 10.4018/ijghpc.2020100103 |
Popis: | Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily. |
Databáze: | OpenAIRE |
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