Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Practical neural network recipes in C++
Practical neural network recipes in C++
Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks
Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks
Extended Kalman filter training of neural networks on a SIMD parallel machine
Journal of Parallel and Distributed Computing
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
Prediction of urban stormwater quality using artificial neural networks
Environmental Modelling & Software
Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks
Environmental Modelling & Software
Increasing the accuracy of neural network classification using refined training data
Environmental Modelling & Software
Environmental Modelling & Software
Artificial neural networks as an alternative tool in pine bark volume estimation
Computers and Electronics in Agriculture
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Environmental Modelling & Software
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Missing data are omnipresent in forestry research, and this poses problems in the analysis of primary data. Many statistical problems have been viewed as missing data problems. To cope with incomplete data, several methods are currently being used. They are all based on assumptions some of which might not be valid in a particular case. The choice mostly depends on the objective of the study. Considerable mensuration research is motivated by the need for yield projections that can support forest management decisions. This paper is focused on a new approach for filling gaps in diameter measurements on standing tree boles. Dealing with this problem, an attempt was made to examine the applicability of artificial neural network models for missing data estimation and to use the estimated values in the subsequent analysis. The procedure that should be followed in the development of such models is outlined. The results show good performance of the examined ANN models compared to regression treatments for missing data and ANN models demonstrate their adequacy and potential for filling gaps in diameter measurements on standing tree boles. The ANN models applied in this study are sufficiently general and have great potential to be applicable for estimating the missing values of many variables in environmental applications.