Consequences of data uncertainty and data precision in artificial neural network sugar cane yield prediction

Authors:
M. Héctor F. Satizábal;Daniel R. Jiménez;Andres Pérez-Uribe
Affiliations:
Université de Lausanne, Hautes Etudes Commerciales, Institut des Systèmes d'Information and University of Applied Sciences of Western Switzerland;Ghent University, Faculty of Agricultural and Applied Biological Sciences: Agricultural Science and University of Applied Sciences of Western Switzerland;University of Applied Sciences of Western Switzerland
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 5
Cited 0

A practical guide to neural nets

A practical guide to neural nets
Note on free lunches and cross-validation

Neural Computation
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Bootstrapping confidence intervals for clinical input variable effects in a network trained to identify the presence of acute myocardial infarction

Neural Computation
Impact of missing data in evaluating artificial neural networks trained on complete data

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data incompleteness and data scarcity are common problems in agroecological modelling. Moreover, agroecological processes depend on historical data that could be fed into a model in a vast number of ways. This work shows a case study of modelling in agroecology using artificial neural networks. The variable to be modelled is sugar cane yield and for this purpose we used climate, soil, and other environmental variables. Regarding the data precision issue, we trained different neural models using monthly and weekly data in order to compare their performance. Furthermore, we studied the influence of using incomplete observations in the training process in order to include them and thus use a larger quantity of input patterns. Our results show that the gain in observations due to the inclusion of incomplete data is preferable in this application.