Interpretation of airborne geophysical data by using artificial neural networks: approach and case studies

2015 ◽  
Author(s):  
A. Barth ◽  
A. Knobloch ◽  
S. Noack
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Case Studies ◽  
Geophysical Data ◽  
Artificial Neural ◽  
Airborne Geophysical Data

scholarly journals Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology?

2003 ◽  
Vol 7 (5) ◽  
pp. 693-706 ◽  
Author(s):  
E. Gaume ◽  
R. Gosset
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conceptual Model ◽  
Linear Model ◽  
Case Studies ◽  
Stream Flow ◽  
River Flow ◽  
Prediction Models ◽  
Rainfall Runoff ◽  
Artificial Neural

Abstract. Recently Feed-Forward Artificial Neural Networks (FNN) have been gaining popularity for stream flow forecasting. However, despite the promising results presented in recent papers, their use is questionable. In theory, their “universal approximator‿ property guarantees that, if a sufficient number of neurons is selected, good performance of the models for interpolation purposes can be achieved. But the choice of a more complex model does not ensure a better prediction. Models with many parameters have a high capacity to fit the noise and the particularities of the calibration dataset, at the cost of diminishing their generalisation capacity. In support of the principle of model parsimony, a model selection method based on the validation performance of the models, "traditionally" used in the context of conceptual rainfall-runoff modelling, was adapted to the choice of a FFN structure. This method was applied to two different case studies: river flow prediction based on knowledge of upstream flows, and rainfall-runoff modelling. The predictive powers of the neural networks selected are compared to the results obtained with a linear model and a conceptual model (GR4j). In both case studies, the method leads to the selection of neural network structures with a limited number of neurons in the hidden layer (two or three). Moreover, the validation results of the selected FNN and of the linear model are very close. The conceptual model, specifically dedicated to rainfall-runoff modelling, appears to outperform the other two approaches. These conclusions, drawn on specific case studies using a particular evaluation method, add to the debate on the usefulness of Artificial Neural Networks in hydrology. Keywords: forecasting; stream-flow; rainfall-runoff; Artificial Neural Networks

Case studies in process modelling and condition monitoring using artificial neural networks

1994 ◽  
Vol 19 ◽  
pp. 141-146
Author(s):  
P Rutherford ◽  
B Lennox ◽  
G.A Montague
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Case Studies ◽  
Condition Monitoring ◽  
Process Modelling ◽  
Artificial Neural

Neural Networks in Cognitive Science

2012 ◽  
pp. 58-83 ◽  
Author(s):  
Nooraini Yusoff ◽  
Ioana Sporea ◽  
André Grüning
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cognitive Science ◽  
Case Studies ◽  
Cognitive Modelling ◽  
Artificial Neural ◽  
Instructive Case

In this chapter we give a brief overview of the biological and technical background of artificial neural networks as are used in cognitive modelling and in technical applications. This will be complemented by three instructive case studies which demonstrate the use of different neural networks in cognitive modelling.

scholarly journals L-system models for image-based phenomics: case studies of maize and canola

2021 ◽  
Author(s):  
Mikolaj Cieslak ◽  
Nazifa Khan ◽  
Pascal Ferraro ◽  
Raju Soolanayakanahally ◽  
Stephen J Robinson ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Case Studies ◽  
Crop Plants ◽  
Great Promise ◽  
Attractive Alternative ◽  
Manual Annotation ◽  
L System ◽  
Artificial Neural ◽  
Real Plant

Abstract Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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