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SOIL An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/soil-2020-17
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-2020-17
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: original research article 30 Mar 2020

Submitted as: original research article | 30 Mar 2020

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This preprint is currently under review for the journal SOIL.

Game theory interpretation of digital soil mapping convolutional neural networks

José Padarian, Alex B. McBratney, and Budiman Minasny José Padarian et al.
  • Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, New South Wales, Australia

Abstract. The use of complex models such as deep neural networks has yielded large improvements in predictive tasks in many fields including digital soil mapping. Once of the concerns about using these models is that they are perceived as black boxes with low interpretability. In this paper we introduce the use of game theory, specifically SHAP values, in order to interpret a digital soil mapping model. SHAP values represent the contribution of a covariate to the final model predictions. We applied this method to a multi-task convolutional neural network trained to predict soil organic carbon of Chile. The results show the contribution of each covariate to the model predictions in three different contexts: (a) at a local level, showing the contribution of the various covariates for a single prediction, (b) a global understanding of the covariate contribution, and (c) a spatial interpretation of their contributions. The latter constitutes a novel application of SHAP values and also the first detailed analysis of a model in a spatial context. The analysis of a SOC model in Chile corroborated that the model is capturing sensible relationships between SOC and rainfall, temperature, elevation, slope and topographic wetness index. The results agree with commonly reported relationships, highlighting environmental thresholds that coincide with significant areas within the study area. This contribution addresses the limitations of the current interpretation of models in digital soil mapping, especially in a spatial context. We believe that SHAP values are a valuable tool that should be included within the DSM framework since they address the important concerns regarding the interpretability of more complex models. The model interpretation is a crucial step that could lead to generating new knowledge to improve our understanding of soils.

José Padarian et al.

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José Padarian et al.

José Padarian et al.

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Latest update: 06 Jun 2020
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Short summary
In this paper we introduce the use of game theory to interpret a digital soil mapping (DSM) model to understand the contribution of environmental factors to the prediction of soil organic carbon (SOC) of Chile. The analysis corroborated that the SOC model is capturing sensible relationships between SOC and climatic and topographical factors. We were able to represent them spatially (map) addressing the limitations of the current interpretation of models in DSM.
In this paper we introduce the use of game theory to interpret a digital soil mapping (DSM)...
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