Explainable artificial intelligence-driven sustainability for electric vehicle charging station siting: A hybrid LIME-SHAP for MCDM
Abstract
Electric Vehicle Charging Station (EVCS) planning typically relies on subjective factor weighting, hence expert bias may be introduced in multi-criteria decision problems. We propose an Explainable Artificial Intelligence (XAI) powered enhancement to a GIS–MCDM siting by relying on LIME and SHAP hybrid-based approach for inducing data-driven Multi-Influencing Factor (MIF) weights of the weighted overlay. This method applied to four wards in Mumbai India, combines global (SHAP) and local (LIME) model-agnostic importances from a balanced surrogate classifier trained using spatial samples around observed EVCS locations. The hybrid weights substitute the MIF prior inter-relation and drive the classical overlay and TOPSIS stages, leaving intact interpretability and auditability. Validation shows the degree of enhancement in site discrimination (ROC–AUC = 0.846) relative to that of the baseline MIF–TOPSIS process (ROC–AUC = 0.826) with more separated high- and low-suitability classes with less affectedness responding to single-factor perturbations, which can be attributed to the benefit of XAI-based weighting on these weights. It is expected that this will give rise to a more reliable and replicable map of EVCS suitability which can enhance overall sustainability benefits and transparent, stakeholder facing decision-taking.
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References
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