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| Titel: |
The use of machine learning algorithms to design a generalized simplified denitrification model |
| Auteur: |
F. Oehler J. C. Rutherford G. Coco |
| Verschenen in: |
Biogeosciences discussions |
| Paginering: | Jaargang 7 (2010) nr. 2 pagina's 2313-2360 |
| Jaar: |
2010 |
| Inhoud: |
We designed generalized simplified models using machine learning algorithms (ML) to assess denitrification at the catchment scale. In particular, we designed an artificial neural network (ANN) to simulate total nitrogen emissions from the denitrification process. Boosted regression trees (BRT, another ML) was also used to analyse the relationships and the relative influences of different input variables towards total denitrification. To calibrate the ANN and BRT models, we used a large database obtained by collating datasets from the literature. We developed a simple methodology to give confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS. NEMIS is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generality of the ANN model may also be improved if pH is used to differentiate between soil types. Further improvements in model performance can be achieved by lessening dataset effects. |
| Uitgever: |
Copernicus GmbH (provided by DOAJ) |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 44 van 45 gevonden artikelen
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