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Subject: search.filters.subject.SHAP

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-15) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, Y
    Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies.
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    PublicationOpen Access
    Interpretable SHAP-bounded Bayesian optimization for underwater acoustic metamaterial coating design
    (Springer Science and Business Media Deutschland GmbH, 2025-09-08) Weeratunge, H; Robe, D. M; Hajizadeh, E
    We present an interpretability-informed Bayesian optimization framework for the inverse design of underwater acoustic coatings composed of polyurethane elastomers with embedded metamaterial features. A data-driven model was used to capture the relationship between acoustic performance, specifically, sound absorption and the corresponding geometrical design variables. To interpret these relationships, we applied SHapley Additive exPlanations (SHAP), enabling the identification of key parameters influencing the objective function and providing both global and local insights into their effects. The insights from the SHAP analysis were used to automatically refine the bounds of the design space, guiding the optimization process toward more promising regions. This approach was tested on two polyurethane materials with different hardness levels and yielded improved optimal designs compared to standard Bayesian optimization without increasing the number of simulations. This work underscores the effectiveness of combining interpretability techniques with optimization for the efficient and cost-effective design of underwater acoustic metamaterials under strict computational constraints and can be generalized towards other materials and engineering optimization problems
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    PublicationOpen Access
    Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings
    (2022-07) Meddage, D. P. P; Ekanayake, I; Weerasuriya, A; Lewangamage, C. S; Ramanayaka, C. D. E; Miyanawala, T
    This study used explainable machine learning (XML), a new branch of Machine Learning (ML), to elucidate how ML models make predictions. Three tree-based regression models, Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boost (XGB), were used to predict the normalized mean (Cp,mean), fluctuating (Cp,rms), minimum (Cp,min), and maximum (Cp,max) external wind pressure coefficients of a low-rise building with fixed dimensions in urban-like settings for several wind incidence angles. Two types of XML were used — first, an intrinsic explainable method, which relies on the DT structure to explain the inner workings of the model, and second, SHAP (SHapley Additive exPlanations), a post-hoc explanation technique used particularly for the structurally complex XGB. The intrinsic explainable method proved incapable of explaining the deep tree structure of the DT, but SHAP provided valuable insights by revealing various degrees of positive and negative contributions of certain geometric parameters, the wind incidence angle, and the density of buildings that surround a low-rise building. SHAP also illustrated the relationships between the above factors and wind pressure, and its explanations were in line with what is generally accepted in wind engineering, thus confirming the causality of the ML model’s predictions.