2010
DOI: 10.5194/bg-7-3311-2010
|View full text |Cite
|
Sign up to set email alerts
|

The use of machine learning algorithms to design a generalized simplified denitrification model

Abstract: Abstract. We propose to use machine learning (ML) algorithms to design a simplified denitrification model. Boosted regression trees (BRT) and artificial neural networks (ANN) were used to analyse the relationships and the relative influences of different input variables towards total denitrification, and an ANN was designed as a simplified model to simulate total nitrogen emissions from the denitrification process. To calibrate the BRT and ANN models and test this method, we used a database obtained collating … Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2011
2011
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 10 publications
(4 citation statements)
references
References 91 publications
0
4
0
Order By: Relevance
“…using an event based approach like in DNDC (Li et al, 1992)) or use more generalized simplified approaches (e.g. as in Oehler et al (2010) for the denitrification model). Spatially distributed measurements of mineralization dynamics in soil as well as denitrification would help to evaluate the realism of the different modeling approaches.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…using an event based approach like in DNDC (Li et al, 1992)) or use more generalized simplified approaches (e.g. as in Oehler et al (2010) for the denitrification model). Spatially distributed measurements of mineralization dynamics in soil as well as denitrification would help to evaluate the realism of the different modeling approaches.…”
Section: Discussionmentioning
confidence: 99%
“…However, further work is needed to assess denitrification rates in such context. In the meantime, more generalized denitrification models could be used, notably based on soil organic matter content (Oehler et al, 2010), as it may be a strong limiting factor on this site.…”
Section: Nitrogen Budget At the Catchment Scalementioning
confidence: 99%
“…However, the HB model strongly depends on not only the dataset but also the model itself. If the aim of the study is to comprehend the responses and their functions, other statistical methods such as neural networks [ Oehler et al , 2010] are more effective than the HB method or the other inverse calibration methods. Because the HB method is based on deduction, “model uncertainty” is the most significant issue.…”
Section: Discussionmentioning
confidence: 99%
“…Although this research effort reduced the number of parameters by a half, the study used 8000 training samples based on annual values, which were obtained from different sites, to achieve the optimal results. Similarly, another work further simplified the input parameters and training size requirement and used neural networks to simulate total N emissions from the de-nitrification process (N 2 O) [44]. The training set consisted of only 536 records based on input variables such as water filled pore space, nitrate concentration, soil denitrifying potential, organic matter, soil pH, bulk density and soil depth.…”
Section: B Related Work-data Modelsmentioning
confidence: 99%