Neural network approach for modelling ammonia emission after manure application on the field

This paper presents a neural network approach, which enables one to simulate ammonia emission after manure application on the field. Based on the data from 227 experiments out of previously published research, it can be illustrated that the time course of accumulated ammonia emission follows a non-l...

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Bibliographic Details
Published in:Atmospheric environment (1994) 2001-11, Vol.35 (33), p.5833-5841
Main Author: Plochl, Matthias
Format: Article
Language:English
Subjects:
Applied sciences
Atmospheric pollution
Dispersed sources and other
Empirical model
Exact sciences and technology
Mechanistic model
Neural network
neural networks
Non-linear regression
Pollution
Pollution sources. Measurement results
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Summary:This paper presents a neural network approach, which enables one to simulate ammonia emission after manure application on the field. Based on the data from 227 experiments out of previously published research, it can be illustrated that the time course of accumulated ammonia emission follows a non-linear Michaelis–Menten-like function. This function is determined by the two parameters E max and K M, which are dependent on manure-specific driving forces, application parameters and climate. 102 data sets of the 227 experiments showed sufficient data for training and validating neural networks for estimating E max and K M. The neural networks could be trained to R 2 values of 0.926 and 0.832 for the training set and the validation set of E max, and to R 2 values of 0.988 for the training set and 0.527 for the validation set of the K M-value, respectively.
ISSN:1352-2310
1873-2844
DOI:10.1016/S1352-2310(01)00281-3