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Abstract. Machine-learning algorithms are good at computingnon-linear problems and fitting complex composite functions, which makesthem 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 remotetropical mountain regions with rather different soil landscapes:(1) those dominated by peat soils and soils under volcanic influence with high organic mattercontents and (2) those dominated by tropical mineral soils. Two tuning procedures werecompared to fit boosted regression tree models: (1) tuning with grid search,which is the standard approach in pedometrics; and (2) tuning withdifferential evolution optimization. A nested cross-validation approach wasapplied to generate robust models. The area-specific PTFs developed outperform other more general PTFs. Furthermore, the first PTF for typical soils ofParamo landscapes (Ecuador), i.e., organic soils under volcanicinfluence, is presented. Overall, the results confirmed the differentialevolution algorithm's high potential for tuning machine-learning models.While models based on tuning with grid search roughly predicted the responsevariables' mean for both areas, models applying the differential evolutionalgorithm for parameter tuning explained up to 25 times more of the responsevariables' variance.

Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning