http://scholars.ntou.edu.tw/handle/123456789/25305
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Ku, Cheng-Yu | en_US |
dc.contributor.author | Liu, Chih-Yu | en_US |
dc.date.accessioned | 2024-11-01T06:27:43Z | - |
dc.date.available | 2024-11-01T06:27:43Z | - |
dc.date.issued | 2024/4/1 | - |
dc.identifier.issn | 2571-6255 | - |
dc.identifier.uri | http://scholars.ntou.edu.tw/handle/123456789/25305 | - |
dc.description.abstract | To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash-Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 x 10-2, 89.21, 1.63, 4.90 x 10-2, 0.97, 2.92 x 10-1, 0.88, and 4.84, respectively. The model's predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning. | en_US |
dc.language.iso | English | en_US |
dc.publisher | MDPI | en_US |
dc.relation.ispartof | FIRE-SWITZERLAND | en_US |
dc.subject | fire incidence | en_US |
dc.subject | deep neural networks | en_US |
dc.subject | geographic information system | en_US |
dc.subject | urban renewal | en_US |
dc.subject | factor | en_US |
dc.title | Predictive Modeling of Fire Incidence Using Deep Neural Networks | en_US |
dc.type | journal article | en_US |
dc.identifier.doi | 10.3390/fire7040136 | - |
dc.identifier.isi | WOS:001210099100001 | - |
dc.relation.journalvolume | 7 | en_US |
dc.relation.journalissue | 4 | en_US |
item.cerifentitytype | Publications | - |
item.openairetype | journal article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.languageiso639-1 | English | - |
crisitem.author.dept | College of Engineering | - |
crisitem.author.dept | Department of Harbor and River Engineering | - |
crisitem.author.dept | National Taiwan Ocean University,NTOU | - |
crisitem.author.dept | Doctorate Degree Program in Ocean Engineering and Technology | - |
crisitem.author.dept | College of Ocean Science and Resource | - |
crisitem.author.dept | Institute of Earth Sciences | - |
crisitem.author.dept | Center of Excellence for Ocean Engineering | - |
crisitem.author.dept | Ocean Energy and Engineering Technology | - |
crisitem.author.orcid | 0000-0001-8533-0946 | - |
crisitem.author.parentorg | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | College of Engineering | - |
crisitem.author.parentorg | College of Engineering | - |
crisitem.author.parentorg | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | College of Ocean Science and Resource | - |
crisitem.author.parentorg | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | Center of Excellence for Ocean Engineering | - |
顯示於: | 河海工程學系 |
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