Abstract
Abstract. Machine-learning algorithms are good at computing non-linear problems and fitting complex composite functions, which makes them an adequate tool for addressing multiple environmental research questions. One important application is the development of pedotransfer functions (PTFs). This study aims to develop water retention PTFs for two remote tropical mountain regions with rather different soil landscapes: (1) those dominated by peat soils and soils under volcanic influence with high organic matter contents and (2) those dominated by tropical mineral soils. Two tuning procedures were compared to fit boosted regression tree models: (1) tuning with grid search, which is the standard approach in pedometrics; and (2) tuning with differential evolution optimization. A nested cross-validation approach was applied to generate robust models. The area-specific PTFs developed outperform other more general PTFs. Furthermore, the first PTF for typical soils of Páramo landscapes (Ecuador), i.e., organic soils under volcanic influence, is presented. Overall, the results confirmed the differential evolution algorithm's high potential for tuning machine-learning models. While models based on tuning with grid search roughly predicted the response variables' mean for both areas, models applying the differential evolution algorithm for parameter tuning explained up to 25 times more of the response variables' variance.
Highlights
Machine-learning algorithms are good at fitting highly complex non-linear functions (Witten et al, 2011)
We successfully developed new pedotransfer functions (PTFs) for two tropical mountain regions
The comparison with readily available PTFs showed their high performance with respect to predicting soil water retention for the soils in these areas
Summary
Machine-learning algorithms are good at fitting highly complex non-linear functions (Witten et al, 2011). Major application fields in soil science investigate the soils’ spatial variability (Heung et al, 2016), relate data from soil sensing to soil properties (Viscarra Rossel et al, 2016), or develop pedotransfer functions (PTFs; Botula et al, 2014; Van Looy et al, 2017). McBratney et al (2019) give a time line on developments in pedometrics, which refers to machine learning in multiple applications. Machine-learning algorithms applied for PTF development include support vector machines (Lamorski et al, 2008) artificial neural networks (Haghverdi et al, 2012), and regression trees (Tóth et al, 2015)
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