The author affiliation and the funding information in the Acknowledgement section of the online version of the original article was revised.One affiliation(the 8th affiliation)of the first author is added.The Acknowle...The author affiliation and the funding information in the Acknowledgement section of the online version of the original article was revised.One affiliation(the 8th affiliation)of the first author is added.The Acknowledgement section of the original article has been revised to:Acknowledgments:This research was funded by the National University of Mongolia under grant agreement P2023(grant number P2023-4578)and supported by the Chey Institute for Advanced Studies“International Scholarship Exchange Fellowship for the academic year of 2024-2025”,Republic of Korea,and the National University of Mongolia.We would like to acknowledge the National University of Mongolia and Soumik Das from the Center for the Study of Regional Development,Jawaharlal Nehru University,New Delhi-110067,for his valuable assistance in preparing the geological maps.展开更多
Coal mining accidents are a major concern worldwide,necessitating effective safety measures and comprehensive analysis to prevent future accidents.Our proposed solution is the first attempt for Indian mines,inspired b...Coal mining accidents are a major concern worldwide,necessitating effective safety measures and comprehensive analysis to prevent future accidents.Our proposed solution is the first attempt for Indian mines,inspired by the potential of Natural Language Processing(NLP)that can read and analyze vast repositories of accident records in seconds.In combination with machine learning(ML),NLP algorithms can extract unstructured text by eliminating manual data entry errors,reading poorly scanned reports,and understanding multiple versions of the event and cluster documents based on types that would otherwise take months to collate.In the case of accident records,it can be an asset in capturing recurring issues,contributing factors,and high-risk areas,enabling proactive measures to be taken to prevent future accidents.The heart of the study lies in applying two ML algorithms called latent Dirichlet allocation(LDA)and RAKE(Rapid Automatic Keyword Extraction).LDA is a topic modeling technique for clustering accidents based on descriptions.RAKE generates root cause analysis through keywords from accident descriptions and remedies suggested by inspection officers.Both are unsupervised learning techniques that do not require any training on labeled datasets.AI and NLP can significantly enhance the process of creating Swiss Cheese Models and Logic Sequences of Contributory Factors Diagrams by automating the extraction,classification,and analysis of data from incident reports and other relevant documents.Data for analysis in this study came from the Directorate General of Mines Safety(DGMS),India records from 2010 to 2015.展开更多
This study investigates the glacial lake outburst flood(GLOF)hazards in the Tsambagarav mountain range in Western Mongolia,focusing on the Khukhnuruu Valley and its interconnected proglacial lakes.Over the last 30 yea...This study investigates the glacial lake outburst flood(GLOF)hazards in the Tsambagarav mountain range in Western Mongolia,focusing on the Khukhnuruu Valley and its interconnected proglacial lakes.Over the last 30 years,significant glacier retreats,driven by rising temperatures and changing precipitation patterns,have led to the formation and expansion of several proglacial lakes.Fieldwork combined with satellite data and meteorological analysis was used to assess the dynamics of glacier and lake area changes,with particular focus on the flood events of July 2021.The research reveals a substantial reduction in glacier area,particularly in the Khukhnuruu E complex,where glacier area decreased by 19.3%.The study highlights the influence of increasing temperatures and summer precipitation,which have accelerated ice melt,contributing to the expansion and eventual breaching of lakes.Additionally,lake area changes were influenced by the steepness of the terrain,with steeper slopes exacerbating peak discharge during floods.Of the studied seven lakes(Lake 1 to Lake 7),Lake 1 experienced the most dramatic reduction,with a decrease in area by 73.51%and volume by 84.84%,followed by Lake 7.This study underscores the region's vulnerability to climate-induced hazards and stresses the need for a comprehensive early warning system and disaster preparedness measures to mitigate future risks.展开更多
文摘The author affiliation and the funding information in the Acknowledgement section of the online version of the original article was revised.One affiliation(the 8th affiliation)of the first author is added.The Acknowledgement section of the original article has been revised to:Acknowledgments:This research was funded by the National University of Mongolia under grant agreement P2023(grant number P2023-4578)and supported by the Chey Institute for Advanced Studies“International Scholarship Exchange Fellowship for the academic year of 2024-2025”,Republic of Korea,and the National University of Mongolia.We would like to acknowledge the National University of Mongolia and Soumik Das from the Center for the Study of Regional Development,Jawaharlal Nehru University,New Delhi-110067,for his valuable assistance in preparing the geological maps.
基金support to provide accidents reports of mines from year 2010 to 2015.
文摘Coal mining accidents are a major concern worldwide,necessitating effective safety measures and comprehensive analysis to prevent future accidents.Our proposed solution is the first attempt for Indian mines,inspired by the potential of Natural Language Processing(NLP)that can read and analyze vast repositories of accident records in seconds.In combination with machine learning(ML),NLP algorithms can extract unstructured text by eliminating manual data entry errors,reading poorly scanned reports,and understanding multiple versions of the event and cluster documents based on types that would otherwise take months to collate.In the case of accident records,it can be an asset in capturing recurring issues,contributing factors,and high-risk areas,enabling proactive measures to be taken to prevent future accidents.The heart of the study lies in applying two ML algorithms called latent Dirichlet allocation(LDA)and RAKE(Rapid Automatic Keyword Extraction).LDA is a topic modeling technique for clustering accidents based on descriptions.RAKE generates root cause analysis through keywords from accident descriptions and remedies suggested by inspection officers.Both are unsupervised learning techniques that do not require any training on labeled datasets.AI and NLP can significantly enhance the process of creating Swiss Cheese Models and Logic Sequences of Contributory Factors Diagrams by automating the extraction,classification,and analysis of data from incident reports and other relevant documents.Data for analysis in this study came from the Directorate General of Mines Safety(DGMS),India records from 2010 to 2015.
基金funded by the National University of Mongolia under grant agreement P2023(grant number P2023-4578)。
文摘This study investigates the glacial lake outburst flood(GLOF)hazards in the Tsambagarav mountain range in Western Mongolia,focusing on the Khukhnuruu Valley and its interconnected proglacial lakes.Over the last 30 years,significant glacier retreats,driven by rising temperatures and changing precipitation patterns,have led to the formation and expansion of several proglacial lakes.Fieldwork combined with satellite data and meteorological analysis was used to assess the dynamics of glacier and lake area changes,with particular focus on the flood events of July 2021.The research reveals a substantial reduction in glacier area,particularly in the Khukhnuruu E complex,where glacier area decreased by 19.3%.The study highlights the influence of increasing temperatures and summer precipitation,which have accelerated ice melt,contributing to the expansion and eventual breaching of lakes.Additionally,lake area changes were influenced by the steepness of the terrain,with steeper slopes exacerbating peak discharge during floods.Of the studied seven lakes(Lake 1 to Lake 7),Lake 1 experienced the most dramatic reduction,with a decrease in area by 73.51%and volume by 84.84%,followed by Lake 7.This study underscores the region's vulnerability to climate-induced hazards and stresses the need for a comprehensive early warning system and disaster preparedness measures to mitigate future risks.
