Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

被引:1
|
作者
Lu, Ying [1 ,2 ]
Fan, Xiaopeng [1 ]
Zhang, Yi [1 ]
Wang, Yong [1 ]
Jiang, Xuepeng [1 ]
机构
[1] Wuhan Univ Sci & Technol, Sch Resource & Environm Engn, Wuhan 430081, Peoples R China
[2] Hubei Ind Safety Engn Technol Res Ctr, Wuhan 430081, Peoples R China
基金
中国国家自然科学基金;
关键词
risk assessment model; stadium fire risk; equipment management; SHapley Additive exPlanations (SHAP); random forest algorithm; SYSTEM;
D O I
10.3390/s23042151
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Machine learning methods can establish complex nonlinear relationships between input and response variables for stadium fire risk assessment. However, the output of machine learning models is considered very difficult due to their complex "black box" structure, which hinders their application in stadium fire risk assessment. The SHapley Additive exPlanations (SHAP) method makes a local approximation to the predictions of any regression or classification model so as to be faithful and interpretable, and assigns significant values (SHAP value) to each input variable for a given prediction. In this study, we designed an indicator attribute threshold interval to classify and quantify different fire risk category data, and then used a random forest model combined with SHAP strategy in order to establish a stadium fire risk assessment model. The main objective is to analyze the impact analysis of each risk characteristic on four different risk assessment models, so as to find the complex nonlinear relationship between risk characteristics and stadium fire risk. This helps managers to be able to make appropriate fire safety management and smart decisions before an incident occurs and in a targeted manner to reduce the incidence of fires. The experimental results show that the established interpretable random forest model provides 83% accuracy, 86% precision, and 85% recall for the stadium fire risk test dataset. The study also shows that the low level of data makes it difficult to identify the range of decision boundaries for Critical mode and Hazardous mode.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Explanation of Machine Learning Models Using Improved Shapley Additive Explanation
    Nohara, Yasunobu
    Matsumoto, Koutarou
    Soejima, Hidehisa
    Nakashima, Naoki
    [J]. ACM-BCB'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, 2019, : 546 - 546
  • [2] Explanation of machine learning models using shapley additive explanation and application for real data in hospital
    Nohara, Yasunobu
    Matsumoto, Koutarou
    Soejima, Hidehisa
    Nakashima, Naoki
    [J]. Computer Methods and Programs in Biomedicine, 2022, 214
  • [3] Explanation of machine learning models using shapley additive explanation and application for real data in hospital
    Nohara, Yasunobu
    Matsumoto, Koutarou
    Soejima, Hidehisa
    Nakashima, Naoki
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 214
  • [4] Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive exPlanation (SHAP)
    Gebreyesus, Yibrah
    Dalton, Damian
    Nixon, Sebastian
    De Chiara, Davide
    Chinnici, Marta
    [J]. FUTURE INTERNET, 2023, 15 (03)
  • [5] Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation
    Liu, Lianhua
    Bi, Bo
    Cao, Li
    Gui, Mei
    Ju, Feng
    [J]. FRONTIERS IN ENDOCRINOLOGY, 2024, 15
  • [6] Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing
    Choi, Myoung-Young
    Jun, Sunghae
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (12):
  • [7] The Prediction of the Tibetan Plateau Thermal Condition with Machine Learning and Shapley Additive Explanation
    Tang, Yuheng
    Duan, Anmin
    Xiao, Chunyan
    Xin, Yue
    [J]. REMOTE SENSING, 2022, 14 (17)
  • [8] Axial Compression Prediction and GUI Design for CCFST Column Using Machine Learning and Shapley Additive Explanation
    Liu, Xuerui
    Wu, Yanqi
    Zhou, Yisong
    [J]. BUILDINGS, 2022, 12 (05)
  • [9] Risk assessment in machine learning enhanced failure mode and effects analysis
    Wang, Zeping
    Du, Hengte
    Tao, Liangyan
    Javed, Saad Ahmed
    [J]. DATA TECHNOLOGIES AND APPLICATIONS, 2023, : 95 - 112
  • [10] Deep Learning Model for Crash Injury Severity Analysis Using Shapley Additive Explanation Values
    Kang, Yashu
    Khattak, Aemal J.
    [J]. TRANSPORTATION RESEARCH RECORD, 2022, 2676 (12) : 242 - 254