Browsing by Author "Lieb, Mareike"

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Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning
(2020) Gebauer, Anika; Ellinger, Monja; Brito Gomez, Victor Manuel; Lieb, Mareike
© 2020 Copernicus Gmb H. All rights reserved. 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.
Optimisation in machine learning: an application to topsoil organic stocks prediction in a dry forest ecosystem
(2019) Gebauer, Anika; Brito Gomez, Victor Manuel; Lieb, Mareike
Soil organic carbon (SOC) sequestration plays a key role in reducing the atmospheric greenhouse gas concentration. However, dry forest ecosystems in Ecuador are endangered to become a source of carbon emissions because of deforestation. Often spatial information, necessary to quantify potential carbon loss to the atmosphere, is missing. This particularly applies to remote areas of limited accessibility. This study aims to regionalise the SOC stocks of a small and poorly accessible dry forest ecosystem in southwestern Ecuador by using boosted regression tree (BRT) models. Resampling in a nested repeated k-fold cross validation approach was applied to develop robust models for a dataset of 118 samples with limited predictor information. To select an optimal set of model parameters, optimisation by differential evolution (DE) was applied for parameter tuning. Predictor selection was implemented using the same optimisation algorithm. This study demonstrates how the predictive performance of BRT models can be improved by applying an optimisation approach for parameter tuning and predictor selection. Model performance was improved by approximately 40% concerning the R2. Still, the results also demonstrated the difficulties of machine learning applications in small and highly heterogeneous natural areas. Very variable or even random factors were assumed to distort the relationship between predictor and response variables. We assume that the presented approach is particularly successful in the case of a real-valued multivariate space of tuning parameters. However, this requires testing in further machine learning applications and algorithms.
Spatial prediction of soil water retention in a Páramo landscape: methodological insight into machine learning using random forest
(2018) Guio Blanco, Carlos Manuel; Brito Gómez, Víctor Manuel; Crespo Sánchez, Patricio Javier; Lieb, Mareike
Soils of Páramo ecosystems regulate the water supply to many Andean populations. In spite of being a necessary input to distributed hydrological models, regionalized soil water retention data from these areas are currently not available. The investigated catchment of the Quinuas River has a size of about 90 km 2 and comprises parts of the Cajas National Park in southern Ecuador. It is dominated by soils with high organic carbon contents, which display characteristics of volcanic influence. Besides providing spatial predictions of soil water retention at the catchment scale, the study presents a detailed methodological insight to model setup and validation of the underlying machine learning approach with random forest. The developed models performed well predicting volumetric water contents between 0.55 and 0.9 cm 3 cm− 3. Among the predictors derived from a digital elevation model …