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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A shap-Driven Framework to Accelerate Urban Carbon Neutrality

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ENERGIES 2025年第7期18卷 1624-1624页

作者： Zhao, Zhihui Wang, Minjuan Wei, Jin Cen, Xiao Du, Shengnan Wu, Ziwen Liu, Huanying Wang, Weiqiang Zhejiang Ocean Univ Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S Sch Petrochem Engn & Environm Zhejiang Key Lab Pollut Control Port Petrochem Ind Zhoushan 316022 Peoples R China Ningbo Univ Technol Coll Mech & Automot Engn Ningbo 315211 Peoples R China China Univ Petr Natl Engn Lab Pipeline Safety MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China Sinopec Sales Co Ltd Zhejiang Hangzhou Petr Branch Hangzhou 310013 Peoples R China

The transition towards carbon neutrality and sustainable urban development necessitates innovative strategies for managing multi-energy supply stations (MESS), which integrate oil, gas, electricity, and hydrogen to support diversified energy demands. Existing revenue prediction models for MESS lack interpretability and multi-energy adaptability, hindering actionable insights for sustainable operations. This study proposes a novel shapley additive explanations (shap)-driven machine learning framework for multi-energy supply station revenue forecasting. By leveraging real-world consumption data from Hangzhou West Lake Tanghe Station, we constructed a dataset with nine critical parameters, including energy types, transaction frequency, and temporal features. Four machine learning models-decision tree regression, random forest (RF), support vector regression, and multilayer perceptron-were evaluated using MAE, MSE, and R2 metrics. The RF model achieved an R2 of 0.98, demonstrating superior accuracy in predicting hourly gross transaction values. shap analysis further identified consumption volume and transaction frequency as the most influential factors, providing actionable insights for operational optimization. This research not only advances the scientific management of MESS but also contributes to carbon emission reduction by enabling data-driven resource allocation. The proposed framework offers policymakers and industry stakeholders a scalable tool to accelerate urban energy transitions under carbon neutrality goals, bridging the gap between predictive analytics and sustainable infrastructure planning.

关键词： multi-energy supply station revenue forecasting data-driven model shap algorithm interpretability

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The Use of Machine Learning Models and shap Interaction Values to Predict the Soil Swelling Index

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2025年第1期69卷 239-250页

作者： Letif, Myriam Bahar, Ramdane Mezouar, Nourredine Univ Sci & Technol Houari Boumediene USTHB Fac Civil Engn Dept Geotech & Hydraul LEEGO Lab POB 32 Algiers 16111 Algeria Ctr Natl Rech Appl Genie Parasism CGS Dept Microzonage Sism Rue Kaddour RahimPOB 252 Algiers 16040 Algeria

Predicting the soil swelling index (CS) is crucial for geotechnical engineer to ensure the stability of civil engineering conceptions. Recently, ML models has sparked great interest from researchers in predicting the soil swelling index. However, due to the black-box nature of ML models, their prediction capabilities are still uninterpretable. This study aims to predict the soil swelling index using ML algorithms and interpret predictions. First, it employs the prediction capability of the Gaussian process regression (GPR) algorithm and compares it to the artificial neural network (ANN) for prediction the soil swelling index. Second, the shap algorithm as one recent explainable artificial intelligence (XAI) models is applied to interpret the predictions of the complex GPR and ANN models. The compiled experimental database covers 362 clayey samples gathered from different sites located in Northern Algeria. The modeling involved six input features, including the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (omega n), dry density (gamma d), and void ratio (e) to predict the soil swelling index. The findings based on statistical metrics showed a good performance of GPR with R2 = 0.78 and of ANN with R2 = 0.79. Comparative study based on Wilcoxon signed- rank test and sign test indicated that the ANN outperform better than GPR. Based on the interpretations obtained by shap algorithm, it is observed that the liquid limit (LL) and plastic limit (PL) are the two main input features that influence the CS, indicating, the higher content of LL and PL increase the model's output.

关键词： soil swelling index machine learning Gaussian process regression artificial neural network shap algorithm

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An Energy-Saving Evaluation Method for Driving Behavior Based on the shap Scorecard Model

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第2期16卷 83-83页

作者： Zheng, Guowen Kong, Deyang Ding, Chenyang Hu, Jiaming Liu, Zhanping Nanchang Intelligent New Energy Vehicle Res Inst Nanchang 330000 Peoples R China Tongji Univ Coll Automobile Shanghai 200092 Peoples R China Jiangxi Jiangling Grp New Energy Vehicle Co Ltd Nanchang 330000 Peoples R China

In order to fully examine how driving behavior affects vehicle energy consumption and further cut down on excessive energy use while driving, an energy-saving evaluation method for driving behavior based on the shap (shapley Additive exPlanations) scorecard model is proposed. First, to categorize short trip segments, a LightGBM model is built, which performs better and has high interpretability. The shap method is then used to statistically assess the categorization findings in order to ascertain how various driving events and driving behavior characteristics affect energy consumption. After that, the data are converted into scores using a scorecard model, which is then used to examine the evaluation outcomes of each sample and individual driver. Lastly, by checking the distribution of scores and the pertinent factors for various scores, the scorecard model's correctness and reasonableness in evaluating energy-saving driving practices of short-trip segments are confirmed.

关键词： scorecard model energy saving driving behavior evaluation shap algorithm

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Shear performance prediction of RC beams shear-strengthened with FRP sheet: A machine learning driven design-oriented method

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ENGINEERING STRUCTURES 2025年 334卷

作者： Tang, Yixing Wang, Wenwei Huang, Qiudi Cheng, Yi Zhou, Chang Southeast Univ Sch Transportat Nanjing 211189 Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong 999077 Peoples R China

This study employs machine learning (ML) methods to predict and analyze the failure modes and shear capacity of RC beams shear-strengthened with FRP sheets. A dataset comprising 410 experimental parameters and results was compiled to train and test six supervised ML algorithms. The findings indicate that the eXtreme Gradient Boosting (XGBoost) model demonstrated exceptional performance in predicting failure modes, achieving 85 % accuracy on the test set. Additionally, the introduction of four performance indicators (RMSE, MAPE, MAE, and R2) highlights the superior accuracy of K-Nearest Neighbors (KNN) and XGBoost algorithms in predicting shear bearing capacity compared to three authoritative empirical formulas and ML models, with all empirical formulas exhibiting comparatively poorer performance. The shapley Additive Explanations (shap) method was then applied to quantify the influence of different features, with dataset samples analyzed to assess the correlation between design parameters and both failure modes and shear capacity. Notably, the wrap scheme, FRP tensile strength and thickness, as well as beam height and width, were significant in determining failure modes, whereas beam height and width, shear span- depth ratio, FRP strength, wrap scheme, and stirrup reinforcement ratio exerted the most substantial influence on shear capacity. These insights offer valuable guidance for enhancing the safety and economic efficiency of RC beams shear-strengthened with FRP in terms of design parameters.

关键词： RC beam FRP sheet Shear performance Machine learning Model interpretation shap algorithm

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Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Lin, Kunzhe Zhang, Jianping Zhao, Lin Wei, Liangfeng Wang, Shousen Fujian Med Univ Fuzong Clin Med Coll Fuzhou Peoples R China 900th Hosp Joint Logist Support Force Dept Neurosurg Fuzhou Peoples R China 910th Hosp Joint Logist Support Force Dept Urol Quanzhou Peoples R China Fujian Med Univ Affiliated Hosp 1 Dept Androl Fuzhou Peoples R China Fujian Med Univ Hosp 900 Dept Neurosurg Fuzong Clin Med Coll Xi Erhuanbei Rd Fuzhou 350025 Fujian Peoples R China

This study aimed to develop and validate machine learning (ML) models to predict the occurrence of delayed hyponatremia after transsphenoidal surgery for pituitary adenoma. We retrospectively collected clinical data on patients with pituitary adenomas treated with transsphenoidal surgery between January 2010 and December 2020. From January 2021 to December 2022, patients with pituitary adenomas were prospectively enrolled. We trained seven ML models to predict delayed hyponatremia using the clinical variables in the training set. The final model was internally validated using a test set and a prospective dataset. The shapley Additive exPlanations (shap) algorithm was used to determine the significance of each variable in the occurrence of delayed hyponatremia. In the training dataset, the best predictive performance was observed for XGBoost (area under the ROC curve;AUC = 0.821), followed by Random Forest (AUC = 0.8), Logistic Regression (AUC = 0.793), Support Vector Machine (AUC = 0.776), na & iuml;ve Bayes (AUC = 0.774), K-Nearest Neighbors (AUC = 0.742), and Decision Tree (AUC = 0.717). The AUC of the XGBoost model for the test and prospective datasets are 0.831 and 0.785, respectively. The differences in pituitary stalk deviation angle, the "measurable pituitary stalk" length before and after surgery, and blood sodium concentration between preoperative and postoperative day 2 were important variables for predicting delayed hyponatremia as determined by the shap algorithm. The XGBoost model was best able to predict delayed hyponatremia after transsphenoidal surgery for pituitary adenomas. The differences in pituitary stalk deviation angle, pre- versus postoperative "measurable pituitary stalk" length, and pre- versus postoperative day 2 blood sodium concentrations were important variables for predicting delayed hyponatremia.

关键词： Machine learning delayed hyponatremia transsphenoidal surgery pituitary adenomas shap algorithm

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Automated prediction and design of PBL connectors using physics-integrated explainable machine learning and user-friendly web API

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STRUCTURES 2025年 73卷

作者： Mou, Ben Lang, Xin Fu, Yuguang Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 100144 Hubei Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266033 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

In this paper, an automated prediction and design framework of perfobond rib (PBL) connectors is developed using physics-integrated explainable machine learning. The key objective is to obtain a design and calculation formula with high precision based on the experimental datasets collected from the literature papers. To do this, as the first step, six machine learning (ML) models, namely Adaptive Boosting (AdaBoost), Decision Tree (DT), KNearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost), have been developed to predict the shear capacity of PBL connectors. Then, the shapley Additive exPlanations (shap) algorithm is used to explain the input-output relationship of the best-performing model, XGBoost. Eventually, an effective formula for prediction of the shear capacity of PBL connectors was derived using physics-integrated explainable ML by combining the equation of surface fitting results and expanded lightweight Artificial Neural Network (ANN) model. Finally, a user-friendly Web API was developed to facilitate the prediction and design of PBL connectors for engineers.

关键词： PBL connectors Machine learning shap algorithm Web API Structural design

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Leveraging machine learning to explore nonlinear associations between urban heat vulnerability and morbidity risk

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URBAN CLIMATE 2025年 59卷

作者： Yang, Jiaming Tong, Zhaomin Xu, Jiwei An, Rui Liu, Yanfang Liu, Yaolin Wuhan Univ Sch Resource & Environm Sci 129 Luoyu Rd Wuhan 430079 Peoples R China Wuhan Univ Key Lab Geog Informat Syst Minist Educ 129 Luoyu Rd Wuhan 430079 Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol 129 Luoyu Rd Wuhan 430079 Peoples R China Duke Kunshan Univ 8 Duke Ave Kunshan 215316 Jiangsu Peoples R China

Urban heat vulnerability (UHV) caused by anthropogenic activities and climate changes has given rise to heat health issues in urban areas worldwide. Previous studies have extensively revealed a simple linear relationship between heat vulnerability indices (HVIs) and morbidity or mortality of heat-related illnesses, but the nonlinear relationship and interactions between main HVIs have not yet been fully explored. Based on vulnerability assessment framework, this paper selected fifteen indicators from built environment, sociodemographic and socioeconomic attributes, resource accessibility and residential thermal comfort, obtained from multisource data. Through the evaluation and analysis of composite HVI and its dimensions, we found that Qingshan district and East Lake scenic area contain more high to very high heat vulnerability communities. The performances of the ordinary least squares (OLS) and gradient boosting decision trees (GBDT) were compared, and results indicate GBDT outperforms the OLS model and captures the nonlinear relationship more efficiently in study areas with higher accuracy. When analyzing HVIs' contributions and interactions with the GBDT model and the shap algorithm, nighttime light (NTL), building year (BY), PM2.5, floor area ratio (FAR), number of elderly (>= 65 years) (NE) and urban surface roughness (USR) are six key indicators of morbidity of heat-related diseases (mean shap value>2.5), and they have an evident nonlinear relationship with the threshold effect and spatially heterogeneous contributions for the morbidity variation of heat-related diseases. Our study provides insights into machine learning (ML) model for the effect of heat vulnerability on city residential health and mitigation and adaptation strategies for governments and urban planners to develop heat resilience cities.

关键词： Urban heat vulnerability Morbidity GBDT model shap algorithm Interaction effect

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Analysis of landslide deformation mechanisms and coupling effects under rainfall and reservoir water level effects

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ENGINEERING GEOLOGY 2024年 343卷

作者： Li, Boyi Wang, Guilin Chen, LiChuan Sun, Fan Wang, Runqiu Liao, MingYong Xu, Hong Li, Siyu Kang, Yanfei Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Minist Nat Resources Technol Innovat Ctr Geohazards Automat Monitoring Chongqing Inst Geol & Mineral Resources Chongqing Peoples R China Natl Joint Engn Res Ctr Geohazards Prevent Reservoir Areas Chongqing 400045 Peoples R China Chongging Univ Key Lab New Technol Construct Cities Mt Area Minist Educ Chongqing 400045 Peoples R China Chongqing Huadi Zihuan Technol Co Ltd Chongqing 401120 Peoples R China Nanjiang Hydrogeol & Engn Geol Brigade Chongqing 401121 Peoples R China Chongqing Bur Geol Survey & Mineral Explorat Chongqing 401121 Peoples R China

Changes in rainfall, groundwater levels, and reservoir water levels exacerbate the deformation of water-involved landslides, accelerating the transition from landslide evolution to extinction. Extracting the destruction patterns of landslides from extensive monitoring data, and understanding their overall deformation mechanisms are crucial for geological hazard prevention and control. Herein, we took the Jiuxianping landslide in the Three Gorges Reservoir area as an example and proposed a deformation mechanism analysis model for water-related landslides based on monitoring data mining techniques. Using Granger causality testing, the study analyzes the spatiotemporal characteristics of GPS displacement data from three different profiles, which confirms that Jiuxianping exhibits a traction destruction mode. By comparing GPS displacement data and their Granger causality relationships across different profiles, we reveal that segmented sliding features of the landslide's front, middle, and trailing during its evolution. Furthermore, the impact intensity of triggering factors (rainfall and reservoir water level changes) on landslide displacement was identified. Based on GPS displacement data from profiles II-II ', an empirical mode decomposition-long short-term memory-regression model (EMD-LSTMregression) is developed for multisource prediction of landslide displacements. The shapley additive explanations algorithm is used to analyze the influence of rainfall and reservoir water level changes on periodic displacements at different positions of the landslide. Owing to the large area of the Jiuxianping landslide, the response to triggering factors varies across different locations. In the context of global warming and frequent extreme weather events, these findings offer important insights for preventing and mitigating water-related landslides in the Three Gorges Reservoir area, while also providing new perspectives for the analysis of global water-involved landslide defor

关键词： Water-involved landslides Jiuxianping landslide Granger causality testing EMD-LSTM-regression model shap algorithm Segmented sliding features

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Machine learning driven bond performance prediction between FRP bars and coral aggregate concrete

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CONSTRUCTION AND BUILDING MATERIALS 2024年 442卷

作者： Tang, Yixing Zhou, Chang Wang, Wenwei Zhang, Wutong Cheng, Yi Southeast Univ Sch Transportat Nanjing 211189 Peoples R China

This study aims to apply machine learning (ML) methods to predict the failure mode and bond stress between FRP bars and coral aggregate concrete (CAC). The central pull test dataset of 221 FRP bars and CAC was synthesized for training and testing six supervised ML models, including Artificial Neural Network (ANN), Decision Tree, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM) and eXtreme Gradient Boosting Trees (XGBoost). The performance of ML algorithms in predicting failure modes was analyzed using confusion matrices, with the random forest model exhibiting the highest classification ability. Regarding the prediction of bond stress, ML algorithms demonstrated superior precision compared to three existing empirical methods, with XGBoost and KNN being identified as the optimal algorithms based on five regression performance metrics. Additionally, the application of the shapley Additive Explanation (shap) method provided insights into the contribution of each input feature to the prediction process of ML models. Notably, variables including the rib height, diameter and bond length of FRP bars significantly influenced failure mode. In contrast, the compressive strength of CAC, the rib height, and the bond length of FRP bars, played crucial roles in predicting bond stress. These findings provide valuable guidance for enhancing the collaborative efficiency between FRP bars and CAC and optimizing structural design practices.

关键词： Bond performance Fiber reinforced polymer (FRP) Coral aggregate concrete (CAC) Machine learning Model interpretation shap algorithm

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Landslide susceptibility evaluation and determination of critical influencing factors in eastern Sichuan mountainous area, China

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ECOLOGICAL INDICATORS 2024年 169卷

作者： Zhang, Lin Guo, Zhengxi Qi, Shi Zhao, Tianheng Wu, Bingchen Li, Peng Beijing Forestry Univ Sch Soil & Water Conservat Beijing 100083 Peoples R China Guangxi Commun Design Grp Co Ltd Nanning 5300291 Peoples R China Jiangxi Acad Water Sci & Engn Nanchang 330029 Peoples R China

Landslide susceptibility evaluation and determination of critical influencing factors is a prerequisite for preventing hazardous risks, especially in landslide-prone mountainous areas. However, in densely vegetated Southwest mountainous areas, identifying assessment approach of shallow landslides susceptibility and their major inducing factors is still a huge challenge. To address this challenge, we applied five advanced machine learning models (Logistic Regression Model, Generalized Additive Model, Random Forest Model, Support Vector Machine Model, Artificial Neural Network Model) to assess the spatial distribution of shallow landslide susceptibility, considering several relevant factors that affect landslide occurrence. These factors include geological, topographic and vegetation factors, as well as four new vegetation factors: stock volume, stand density, average tree age, and stand types. Furthermore, we employed shap algorithm and Structural Equation Models to quantify the relative importance and explanatory power of these factors on shallow landslide susceptibility and to clarify the interaction mechanisms among various factors in Huaying Mountain. The results shown that Random Forest Model proves to be the most accurate (95.1 %) in assessing the spatial distribution of shallow landslides susceptibility, followed by the Artificial Neural Network model (78.6 %), the Support Vector Machine model (69.8 %), the Generalized additive model (68.1 %) and the Logistic Regression model (67.6 %).The area with high susceptible landslide possibility was 25.3 km2 occupying 14.8 % of the study region, it is mainly distributed in the west of Tianchi Lake, southeast of Huaying City and west of the study area, along with Xiangyu Railway. Geographical environment and vegetation features were found to significantly explain 67.4 % and 32.6 % of the total effects in shallow landslides susceptibility, respectively. Specifically, the spatial distribution of shallow landslides suscept

关键词： Landslides susceptibility evaluation Machine learning shap algorithm Vegetation features Influencing factors

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