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Amélie Marie Beucher

Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification

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Convolutional neural networks (CNNs) have been originally used for computer vision tasks,
such as image classification. While several digital soil mapping studies have been
assessing these deep learning algorithms for the prediction of soil properties, their
potential for soil classification has not been explored yet. Moreover, the use of deep
learning and neural networks in general has often raised concerns because of their
presumed low interpretability (i.e., the black box pitfall). However, a recent and fast-developing
sub-field of Artificial Intelligence (AI) called explainable AI (XAI) aims to clarify
complex models such as CNNs in a systematic and interpretable manner. For example, it is
possible to apply model-agnostic interpretation methods to extract interpretations from
any machine learning model. In particular, SHAP (SHapley Additive exPlanations) is a
method to explain individual predictions: SHAP values represent the contribution of a
covariate to the final model predictions. The present study aimed at, first, evaluating the
use of CNNs for the classification of potential acid sulfate soils located in the wetland areas
of Jutland, Denmark (c. 6,500 km2), and second and most importantly, applying a model-agnostic
interpretation method on the resulting CNN model. About 5,900 soil observations
and 14 environmental covariates, including a digital elevation model and derived terrain
attributes, were utilized as input data. The selected CNN model yielded slightly higher
prediction accuracy than the random forest models which were using original or scaled
covariates. These results can be explained by the use of a common variable selection
method, namely recursive feature elimination, which was based on random forest and thus
optimized the selection for this method. Notably, the SHAP method results enabled to
clarify the CNN model predictions, in particular through the spatial interpretation of the
most important covariates, which constitutes a crucial development for digital soil
mapping.
Original languageEnglish
Article number809995
JournalFrontiers in Environmental Sciences
Volume9
Number of pages14
ISSN2296-665X
DOIs
Publication statusPublished - Jan 2022

    Research areas

  • SHAP (SHapley Additive exPlanations), XAI (eXplainable artificial intelligence), acid sulfate soils, classification, convolutional neural network, deep learning, interpretability

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