BLANCO Anibal Manuel
congresos y reuniones científicas
Modelling Avena fatua seedling emergence: a comparative study between traditional non-linear regression and a neural network approach
CHANTRE GUILERMO; LODOVICHI, MARIELA V.; BLANCO ANíBAL M.; BANDONI J. ALBERTO; SABBATINI, M. RICARDO; LOPEZ RICARDO; VIGNA MARIO; GIGÓN RAMÓN
Vina del Mar
Congreso; XX Congreso ALAM; 2011
Asociacion Latinoamericana de Malezas
Avena fatua is an invasive weed of the semiarid temperate region of Argentina. Seedling emergence patterns show great variability between years mainly due to a highly unpredictable precipitation regime and a fluctuating thermal environment. Traditional non-linear regression techniques are unable to give a precise prediction of emergence patterns under semiarid conditions. Artificial neural networks (ANN) models are known for their capacity to describe highly non-linear relationships among variables thus showing a high potential applicability in ecological systems. The objectives of the present work were to: (i) develop different ANN model architectures and obtain an optimal model for seedling emergence prediction; (ii) compare the predictive accuracy of traditional non-linear regression models with the ANN approach. Input variables for model development were: hydrothermal-time (èHT), thermal-time (èT) and hydro-time (èH) indexes. The accumulated proportion of seedling emergence was the output variable. A total of 528 input/output data pairs corresponding to 11 years of data collection were divided into training (82%) and test (18%) subsets. Evaluated ANN model scenarios resulted of varying the number of effective parameters using hyperbolic tangent sigmoid transfer functions and a Bayesian Regularization algorithm. Results showed that an ANN model with èT and èH as independent variables and 30 effective parameters gave the most accurate prediction on independent emergence data (RMSE=0.078). Traditional non-linear models showed a lower predictive capability (RMSE=0.1320.204) irrespective of the index used. These results indicate a higher accuracy and generalization performance of the optimal ANN model in comparison to traditional non-linear regression further suggesting the potential applicability of the former in weed management decision support systems.