Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

作者：Luca Giuliano Bernardini, Christoph Rosinger, Gernot Bodner, Katharina Keiblinger, Emma Izquierdo‐Verdiguier, Heide Spiegel, Carl Orge Retzlaff, Andreas Holzinger · 发表于：New Biotechnology · 年份：2024 · DOI：10.1016/j.nbt.2024.03.001 · 被引用次数：28 · 研究领域：Soil Carbon and Nitrogen Dynamics、Soil Geostatistics and Mapping、Soil and Water Nutrient Dynamics

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while pr...