Journal article
Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world
Environmental modelling & software : with environment data news, Vol.59, pp.202-221
09/2014
DOI: 10.1016/j.envsoft.2014.05.022
Abstract
This paper compares one global parametric land use change model, the artificial neural network – based Land Transformation Model, with two local non-parametric models: a classification and regression tree and multivariate adaptive regression spline model. We parameterized these three models with identical data from different regions of the world; one region undergoing extensive agricultural expansion (East Africa), another region where forests are re-growing (Muskegon River Watershed in the United States), and a third region where urbanization is prominent (South-Eastern Wisconsin in the United States). Independent training data and testing data were used to train and calibrate each model, respectively. Comparisons of simulated maps from the three kinds of land use change patterns were made using conventional goodness-of-fit metrics in land use change science. The results of temporal and spatial comparison of the data mining models show that the artificial neural network outperformed all other models in a short-time interval (East Africa; 5 years) and for coarse resolution data (East Africa; 1 km); however, the three data mining models obtained similar accuracies in a long-time interval (Muskegon River Watershed; 20 years) and for fine resolution data with large numbers of cells (Muskegon River Watershed; 30 m). Furthermore, the results showed that the probability of agriculture gain was more likely in locations closer to towns and large cities in East Africa, urbanization was more likely in locations closer to roads and urban areas in South-Eastern Wisconsin and the probability of forest gain was more likely in locations closer to the forest and shrub land cover and farther away from roads in Muskegon River Watershed.
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•Developed a framework to classify data mining tools.•Local non-parametric models (LNPM) were more interpretable than a global parametric model (GPM).•A GPM outperformed two LNPMs for a short time interval and for coarse resolution data.•Three models obtained similar accuracies in a longer time interval and for fine resolution data.
Details
- Title: Subtitle
- Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world
- Creators
- Amin Tayyebi - University of Wisconsin–MadisonBryan C Pijanowski - Purdue University West LafayetteMarc Linderman - University of IowaClaudio Gratton - University of Wisconsin–Madison
- Resource Type
- Journal article
- Publication Details
- Environmental modelling & software : with environment data news, Vol.59, pp.202-221
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.envsoft.2014.05.022
- ISSN
- 1364-8152
- eISSN
- 1873-6726
- Grant note
- name: USGS Climate Change Research Program; DOI: 10.13039/100001145, name: Great Lakes Fishery Trust; DOI: 10.13039/100007869, name: Department of Forestry and Natural Resources, Purdue University
- Language
- English
- Date published
- 09/2014
- Academic Unit
- Geographical and Sustainability Sciences
- Record Identifier
- 9984259637402771
Metrics
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