Predicting artificially drained areas by means of a selective model ensemble

Predicting artificially drained areas by means of a selective model ensemble Farmers often install subsurface drainage systems to improve yields on wet soils, which has large impacts on the hydrological system. The present study uses an ensemble of machine learning models to map the extent of artificially drained areas in Denmark. The prediction is based on 745 field observations, of which one third is held out for evaluation, and 46 covariate layers. A library of 308 models is trained using 77 machine learning methods and four datasets containing either a combination of topographic variables, satellite imagery, soil properties and land use information or principal components based on these variables.A stepwise algorithm then selects models from the library, based on their predictions on a hillclimb dataset. The results show that models trained using principal components generally yielded a better performance than the models trained with the raw covariates. Furthermore, the best results were obtained when only a random fraction of the models was available for selection at each step. The covariates that were most important for the prediction of artificially drained areas mostly related to soil properties and topography. Overall, the ensemble predicted artificially drained areas with an accuracy of 76.5%. The study demonstrates machine learning as an accurate method for mapping artificially drained areas, which is likely to benefit both farmers and decision makers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geoderma Elsevier

Predicting artificially drained areas by means of a selective model ensemble

Loading next page...
 
/lp/elsevier/predicting-artificially-drained-areas-by-means-of-a-selective-model-0ReG1gAlp6
Publisher
Elsevier
Copyright
Copyright © 2018 The Authors
ISSN
0016-7061
eISSN
1872-6259
D.O.I.
10.1016/j.geoderma.2018.01.018
Publisher site
See Article on Publisher Site

Abstract

Farmers often install subsurface drainage systems to improve yields on wet soils, which has large impacts on the hydrological system. The present study uses an ensemble of machine learning models to map the extent of artificially drained areas in Denmark. The prediction is based on 745 field observations, of which one third is held out for evaluation, and 46 covariate layers. A library of 308 models is trained using 77 machine learning methods and four datasets containing either a combination of topographic variables, satellite imagery, soil properties and land use information or principal components based on these variables.A stepwise algorithm then selects models from the library, based on their predictions on a hillclimb dataset. The results show that models trained using principal components generally yielded a better performance than the models trained with the raw covariates. Furthermore, the best results were obtained when only a random fraction of the models was available for selection at each step. The covariates that were most important for the prediction of artificially drained areas mostly related to soil properties and topography. Overall, the ensemble predicted artificially drained areas with an accuracy of 76.5%. The study demonstrates machine learning as an accurate method for mapping artificially drained areas, which is likely to benefit both farmers and decision makers.

Journal

GeodermaElsevier

Published: Jun 15, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off