Título
Attribute selection impact on linear and nonlinear regression models for crop yield prediction
Autor
JUAN FRAUSTO SOLIS
WALDO OJEDA BUSTAMANTE
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5' regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63).
Editor
Hindawi Publishing Corporation
Fecha de publicación
2014
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Fuente
The Scientific World Journal (1537-744X), ID 509429
Idioma
Inglés
Repositorio Orígen
Repositorio institucional del IMTA
Descargas
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