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Given Spatial Variability Aware Neural Networks (SVANNs), the goal is toinvestigate mathematical (or computational) models for comparative physicalinterpretation towards their transparency (e.g., simulatibility,decomposability and algorithmic transparency). This problem is important due toimportant use-cases such as reusability, debugging, and explainability to ajury in a court of law. Challenges include a large number of model parameters,vacuous bounds on generalization performance of neural networks, risk ofoverfitting, sensitivity to noise, etc., which all detract from the ability tointerpret the models. Related work on either model-specific or model-agnosticpost-hoc interpretation is limited due to a lack of consideration of physicalconstraints (e.g., mass balance) and properties (e.g., second law ofgeography). This work investigates physical interpretation of SVANNs usingnovel comparative approaches based on geographically heterogeneous features.The proposed approach on feature-based physical interpretation is evaluatedusing a case-study on wetland mapping. The proposed physical interpretationimproves the transparency of SVANN models and the analytical results highlightthe trade-off between model transparency and model performance (e.g.,F1-score). We also describe an interpretation based on geographicallyheterogeneous processes modeled as partial differential equations (PDEs).

Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results