Campus Units
Agronomy
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
12-20-2019
Journal or Book Title
Applied Mathematical Modelling
DOI
10.1016/j.apm.2019.12.016
Abstract
Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to identify relationships between soil properties and multiple covariates that can be detected across landscapes. Selecting the appropriate algorithm for model building is critical for optimizing results in the context of the available data. Over the past decade, many studies have tested different machine learning (ML) approaches on a variety of soil data sets. Here, we review the application of some of the most popular ML algorithms for digital soil mapping. Specifically, we compare the strengths and weaknesses of multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), Cubist, random forest (RF), and artificial neural networks (ANN) for DSM. These algorithms were compared on the basis of five factors: 1) quantity of hyperparameters, 2) sample size, 3) covariate selection, 4) learning time, and 5) interpretability of the resulting model. If training time is a limitation, then algorithms that have fewer model parameters and hyperparameters should be considered, e.g., MLR, KNN, SVR, and Cubist. If the data set is large (thousands of samples) and computation time is not an issue, ANN would likely produce the best results. If the data set is small (<100), then Cubist, KNN, RF, and SVR are likely to perform better than ANN and MLR. The uncertainty in predictions produced by Cubist, KNN, RF, and SVR may not decrease with large datasets. When interpretability of the resulting model is important to the user, Cubist, MLR, and RF are more appropriate algorithms as they do not function as “black boxes.” There is no one correct approach to produce models for predicting the spatial distribution of soil properties. Nonetheless, some algorithms are more appropriate than others considering the nature of the data and purpose of mapping activity.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Copyright Owner
Elsevier Inc.
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Khaledian, Yones and Miller, Bradley A., "Selecting appropriate machine learning methods for digital soil mapping" (2019). Agronomy Publications. 618.
https://lib.dr.iastate.edu/agron_pubs/618
Included in
Applied Mathematics Commons, Soil Science Commons, Spatial Science Commons, Theory and Algorithms Commons
Comments
This is a manuscript of an article published as Yones Khaledian , Bradley A. Miller , Selecting appropriate machine learning methods for digital soil mapping, Applied Mathematical Modelling (2019), doi: 10.1016/j.apm.2019.12.016. Posted with permission.