Pub Date : 2024-07-26DOI: 10.54963/preventionandtreatmentofnaturaldisasters.v3i2.291
Pınar Usta Evci, Ali Ekber Sever, Elifnur Şakalak
Historic arch bridges, a common feature of Turkish infrastructure, represent a significant aspect of the country’s cultural heritage. To ensure their continued existence and preservation, it is essential to conduct a detailed examination of their structural features and behaviours. This study aimed to investigate the performance of the historic Yeşiltepe Bridge under earthquake conditions. To achieve this, the bridge was modelled using the SAP2000 finite element software, enabling a deeper understanding of its structure and the prediction of its behaviour during an earthquake. In order to ascertain the dynamic behaviour of the historical bridge, modal analysis and nonlinear time history analysis were conducted. The results of the modal analysis yielded period values, mass participation rates and mode shapes for the bridge. The time history analysis yielded displacement, base shear force and stress values for the historical structure, which were subsequently presented in graphical form. The data obtained from the study enabled the identification of the critical regions of the structure exhibiting the highest stress concentration values.
{"title":"Investigation of Seismic Behavior of the Historical Yeşiltepe Bridge","authors":"Pınar Usta Evci, Ali Ekber Sever, Elifnur Şakalak","doi":"10.54963/preventionandtreatmentofnaturaldisasters.v3i2.291","DOIUrl":"https://doi.org/10.54963/preventionandtreatmentofnaturaldisasters.v3i2.291","url":null,"abstract":"Historic arch bridges, a common feature of Turkish infrastructure, represent a significant aspect of the country’s cultural heritage. To ensure their continued existence and preservation, it is essential to conduct a detailed examination of their structural features and behaviours. This study aimed to investigate the performance of the historic Yeşiltepe Bridge under earthquake conditions. To achieve this, the bridge was modelled using the SAP2000 finite element software, enabling a deeper understanding of its structure and the prediction of its behaviour during an earthquake. In order to ascertain the dynamic behaviour of the historical bridge, modal analysis and nonlinear time history analysis were conducted. The results of the modal analysis yielded period values, mass participation rates and mode shapes for the bridge. The time history analysis yielded displacement, base shear force and stress values for the historical structure, which were subsequently presented in graphical form. The data obtained from the study enabled the identification of the critical regions of the structure exhibiting the highest stress concentration values.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"44 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Glacier retreat, a major impact of climate change that continues to occur in many parts of the world, continues to increase the risk of glacial lake outburst floods (GLOFs) in northern Pakistan. The rapid melting of glaciers in the mountains of Northern Pakistan, including the Hindu Kush, the Himalayas and the Karakoram, the rapid melting of glacier has led to the formation of 3044 glacial lakes, with 33 identified as particularly vulnerable to GLOFs. This study uses remote sensing and geographic information systems (GIS) methods for mapping and representing GLOFs. Based on the observational data of lake area, volume, and depth, empirical equations are developed through statistical methods. Only two lakes, Chitral-GL2 and Swat-G31, are classified as lakes with high potential for GLOF. Through modeling techniques using HEC-RAS and HEC-GeoRAS spatial hydrological models integrated with GIS remote sensing, the spatial extent and depth of inundations under different lake volumes are assessed. The analysis reveals that a total area of 20.56 km2 is susceptible to submersion by GLOFs, with Chitral-GL2 flooding area of 14.80 km2 and Swat-GL31 5.79 km2. Different land types are impacted by critical water depths, with built-up and agricultural lands 2.7 km2 totally, and barren lands 8.93 km2 under different flood depths ranging from less than 5 m to over 15 m.
{"title":"Assessing the Impact of Climate Change on Glacial Lake Outburst Flood (GLOF) in Eastern Hindu Kush Region Using Integrated Geo-Statistical and Spatial Hydrological Approach","authors":"Mariam Sarwar, Shakeel Mahmood","doi":"10.54963/ptnd.v3i2.262","DOIUrl":"https://doi.org/10.54963/ptnd.v3i2.262","url":null,"abstract":"Glacier retreat, a major impact of climate change that continues to occur in many parts of the world, continues to increase the risk of glacial lake outburst floods (GLOFs) in northern Pakistan. The rapid melting of glaciers in the mountains of Northern Pakistan, including the Hindu Kush, the Himalayas and the Karakoram, the rapid melting of glacier has led to the formation of 3044 glacial lakes, with 33 identified as particularly vulnerable to GLOFs. This study uses remote sensing and geographic information systems (GIS) methods for mapping and representing GLOFs. Based on the observational data of lake area, volume, and depth, empirical equations are developed through statistical methods. Only two lakes, Chitral-GL2 and Swat-G31, are classified as lakes with high potential for GLOF. Through modeling techniques using HEC-RAS and HEC-GeoRAS spatial hydrological models integrated with GIS remote sensing, the spatial extent and depth of inundations under different lake volumes are assessed. The analysis reveals that a total area of 20.56 km2 is susceptible to submersion by GLOFs, with Chitral-GL2 flooding area of 14.80 km2 and Swat-GL31 5.79 km2. Different land types are impacted by critical water depths, with built-up and agricultural lands 2.7 km2 totally, and barren lands 8.93 km2 under different flood depths ranging from less than 5 m to over 15 m.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"66 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.
{"title":"Advancing Forest-Fire Management: Exploring Sensor Networks, Data Mining Techniques, and SVM Algorithm for Prediction","authors":"Shuo Zhang, Mengya Pan","doi":"10.54963/ptnd.v3i2.271","DOIUrl":"https://doi.org/10.54963/ptnd.v3i2.271","url":null,"abstract":"Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"123 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The author presents convincing evidence of the cosmic (planetary and solar) energy gravitational origin (related to the maximal and minimal combined planetary and solar integral energy gravitational influences on the internal rigid core of the Earth) of the maximal temporal intensifications of the global magnetic processes of the Earth. In fairly good agreement with the calculated date 2007.416666666 AD (of the local minimal planetary and solar integral energy gravitational influence on the internal rigid core), it was observed the very rapid changes of the geomagnetic field near the date March 2007 AD (corresponding approximately to 19-20 April 2007 AD). This fairly good agreement gives the additional convincing argument that the date March 2007 AD can be considered as the possible beginning of the geomagnetic reversal during the evaluated range (2007÷2216) AD. The strong magnetic anomaly occurred on 6 January 2020 AD in perfect agreement with the calculated date 2020.016666667 AD (corresponding to 6 January 2020 AD) related to the local maximal planetary and solar integral energy gravitational influence on the internal rigid core. In fairly good agreement with the calculated date 2023.26666666 AD (corresponding approximately to 7 April 2023 AD) of the local maximal planetary and solar integral energy gravitational influence on the internal rigid core, it was observed the strongest (during the last 6 years) magnetic anomaly on 23 March 2023 AD. These convincing agreements demonstrate the physical validity of the established global prediction thermohydrogravidynamic principles, which can be considered as the proven physical basis for the development of the general unified geophysical theory (describing the possible geomagnetic reversal during the evaluated range (2007 ÷ 2216) AD) combining the Special Theory of Relativity, the relativistic electrodynamics and the relativistic theory of the non-stationary gravitation, which can be developed based on the established physical analogy between the established relation for the energy flux (of the gravitational energy) and the Lorentz’s calibration condition (for the vector potential related with the scalar potential of the non-stationary electromagnetic field).
{"title":"The Convincing Cosmic Energy Gravitational Genesis of the Strongest Geomagnatic Anomalies of the Magnetic Field of the Earth","authors":"Sergey Simonenko","doi":"10.54963/ptnd.v3i1.221","DOIUrl":"https://doi.org/10.54963/ptnd.v3i1.221","url":null,"abstract":"The author presents convincing evidence of the cosmic (planetary and solar) energy gravitational origin (related to the maximal and minimal combined planetary and solar integral energy gravitational influences on the internal rigid core of the Earth) of the maximal temporal intensifications of the global magnetic processes of the Earth. In fairly good agreement with the calculated date 2007.416666666 AD (of the local minimal planetary and solar integral energy gravitational influence on the internal rigid core), it was observed the very rapid changes of the geomagnetic field near the date March 2007 AD (corresponding approximately to 19-20 April 2007 AD). This fairly good agreement gives the additional convincing argument that the date March 2007 AD can be considered as the possible beginning of the geomagnetic reversal during the evaluated range (2007÷2216) AD. The strong magnetic anomaly occurred on 6 January 2020 AD in perfect agreement with the calculated date 2020.016666667 AD (corresponding to 6 January 2020 AD) related to the local maximal planetary and solar integral energy gravitational influence on the internal rigid core. In fairly good agreement with the calculated date 2023.26666666 AD (corresponding approximately to 7 April 2023 AD) of the local maximal planetary and solar integral energy gravitational influence on the internal rigid core, it was observed the strongest (during the last 6 years) magnetic anomaly on 23 March 2023 AD. These convincing agreements demonstrate the physical validity of the established global prediction thermohydrogravidynamic principles, which can be considered as the proven physical basis for the development of the general unified geophysical theory (describing the possible geomagnetic reversal during the evaluated range (2007 ÷ 2216) AD) combining the Special Theory of Relativity, the relativistic electrodynamics and the relativistic theory of the non-stationary gravitation, which can be developed based on the established physical analogy between the established relation for the energy flux (of the gravitational energy) and the Lorentz’s calibration condition (for the vector potential related with the scalar potential of the non-stationary electromagnetic field).","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Part of a requisite for natural hazard awareness and damage prevention in Egypt is the detailed monitoring of surface fault zones. The inventory of active faults and their risk assessment is an essential contribution to the safety of settlements, land use and infrastructure (railroads, highways, pipelines) and to damage prevention. Thus, this study aims to contribute to the detection, inventory, and documentation of fault zones. Surface faulting hazard assessment considers any surface consequences caused by surface near faults such as abrupt horizontal and vertical displacements and rotations, or mass movements after stronger earthquakes. Long-term, aseismic, slow-creeping movements along fault zones have to be considered as well. Southern Egypt offers unique and optimal conditions for the research of fault zones and their different structural expressions and conditions because of the dry desert climate conditions and relatively low human influence on the landscape in extended areas, especially for the investigation of different types of faults, their interactions with each other and the outcropping rocks, and fault-related deformation structures. Volcanic activity has been influenced in the geologic past often by larger fault zones as dike intrusions, volcanic cones and plugs or craters occur concentrated along these zones of weakness facilitating the uprise of magma. Larger fault zones are crossing reservoir areas. Surface water intrusions into deep-seated faults have played a role in triggering earthquake swarms in the reservoir areas during the last decades. Mapping of active faults is not only an important but also a cost-intense task when carried out in the field or when using geodetic and geophysical data. In the scope of this study, fault detection was carried out based on different open-source satellite data (Landsat 8 and 9, Sentinel 2 optical data, Sentinel 1 and ALOSPASAR radar data, and Google Earth and Bing Map high-resolution satellite images) from the southern part of Egypt. Faults were digitized using ArcGIS and QGIS software. An inventory of fault-related structural features (depressions, ridges, rotation structures, etc.) and rift zones was elaborated based on remote sensing data. An overview of different types of faults and their related structures as well as their interactions with their host rock conditions could be achieved. By merging the inventory results with infrastructural and land use data, critical areas with potential damage risk were pointed out.
{"title":"Overview of Fault Zones Based on Remote Sensing Data as Contribution to the Safety of Infrastructure and Land Use in Southern Egypt","authors":"B. Theilen-Willige","doi":"10.54963/ptnd.v3i1.227","DOIUrl":"https://doi.org/10.54963/ptnd.v3i1.227","url":null,"abstract":"Part of a requisite for natural hazard awareness and damage prevention in Egypt is the detailed monitoring of surface fault zones. The inventory of active faults and their risk assessment is an essential contribution to the safety of settlements, land use and infrastructure (railroads, highways, pipelines) and to damage prevention. Thus, this study aims to contribute to the detection, inventory, and documentation of fault zones. Surface faulting hazard assessment considers any surface consequences caused by surface near faults such as abrupt horizontal and vertical displacements and rotations, or mass movements after stronger earthquakes. Long-term, aseismic, slow-creeping movements along fault zones have to be considered as well. Southern Egypt offers unique and optimal conditions for the research of fault zones and their different structural expressions and conditions because of the dry desert climate conditions and relatively low human influence on the landscape in extended areas, especially for the investigation of different types of faults, their interactions with each other and the outcropping rocks, and fault-related deformation structures. Volcanic activity has been influenced in the geologic past often by larger fault zones as dike intrusions, volcanic cones and plugs or craters occur concentrated along these zones of weakness facilitating the uprise of magma. Larger fault zones are crossing reservoir areas. Surface water intrusions into deep-seated faults have played a role in triggering earthquake swarms in the reservoir areas during the last decades. Mapping of active faults is not only an important but also a cost-intense task when carried out in the field or when using geodetic and geophysical data. In the scope of this study, fault detection was carried out based on different open-source satellite data (Landsat 8 and 9, Sentinel 2 optical data, Sentinel 1 and ALOSPASAR radar data, and Google Earth and Bing Map high-resolution satellite images) from the southern part of Egypt. Faults were digitized using ArcGIS and QGIS software. An inventory of fault-related structural features (depressions, ridges, rotation structures, etc.) and rift zones was elaborated based on remote sensing data. An overview of different types of faults and their related structures as well as their interactions with their host rock conditions could be achieved. By merging the inventory results with infrastructural and land use data, critical areas with potential damage risk were pointed out.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"120 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Kachakhidze, N. Kachakhidze-Murphy, Vaso Kukhianidze, G. Ramishvili, B. Khvitia
The consolidated paper presents work carried out in the sphere of earthquake problems. On the base of theoretical and experimental studies, it is shown earthquake prediction possibility. There are discussed earthquake indicators and triggering exogenous factors in the example of the Caucasus region. Because the earthquake preparation process causes anomalous changes with complex characteristics in various geophysical fields, it is given scientifically proven suggestions for the classification of these fields as earthquake precursors, indicators, and triggering factors. It offers a short-term plan for future work in the earthquake prediction direction.
{"title":"For the Classification of Anomalous Geophysical Fields that Existed Prior to an Earthquake","authors":"M. Kachakhidze, N. Kachakhidze-Murphy, Vaso Kukhianidze, G. Ramishvili, B. Khvitia","doi":"10.54963/ptnd.v3i1.197","DOIUrl":"https://doi.org/10.54963/ptnd.v3i1.197","url":null,"abstract":"The consolidated paper presents work carried out in the sphere of earthquake problems. On the base of theoretical and experimental studies, it is shown earthquake prediction possibility. There are discussed earthquake indicators and triggering exogenous factors in the example of the Caucasus region. Because the earthquake preparation process causes anomalous changes with complex characteristics in various geophysical fields, it is given scientifically proven suggestions for the classification of these fields as earthquake precursors, indicators, and triggering factors. It offers a short-term plan for future work in the earthquake prediction direction.\u0000 ","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this study is debris flow risk zonation using geological and hydrometeorological indicators in district Chitral, Hindu Kush Region Northwest Pakistan. The research is based on secondary data. Multi criteria Analysis (MCA) in Geographical Information System (GIS) environment was used to achieve the objective of the study. The geological and hydrometeorological parameters were analyzed by making five classes of each parameter. The classes are ranked as most favorable and least favorable with numerical weights. The weights were assigned in accordance to their importance in debris flow occurrence. Then weighted overlay analysis techniques were applied to develop composite map representing the importance of each factor. Debris flow risk zonation map was resulted into four classes very high risk zones, high risk zone, moderate zone, low risk zone. The geology of the study area is diverse with frequents earthquakes. Similarly the forest cover is decreasing due to anthropogenic activities. The area is also characterized by long cold winters with frost action. These factors are destabilizing the slope. During summer season rain storm event results high surface runoff and peak discharge in the perennial and non-perennial channels which results flood and debris flow. These events result human life loss and disruption. The main villages located in very high risk zone are Mulkoh, Mastuj, Reshun, Shegram, Terich Gol, Rogar, Asurat, Boni, Brep and Rech Tockhow. They have been frequently affected by hazard in the past decade. Out of the total area, very high risk zone is expanded over 8%, high risk zone is expanded over16%, moderate risk zone is 29% and the rest is low risk zone. This study has highlighted the risk zones which will help disaster management authorities and policy makers to reduce the risk of debris flow in future.
{"title":"Debris Flow Hazard Assessment in District Chitral, Eastern Hindu Kush, Pakistan","authors":"Shakeel Mahmood, Attia Atiq","doi":"10.54963/ptnd.v1i2.88","DOIUrl":"https://doi.org/10.54963/ptnd.v1i2.88","url":null,"abstract":"The aim of this study is debris flow risk zonation using geological and hydrometeorological indicators in district Chitral, Hindu Kush Region Northwest Pakistan. The research is based on secondary data. Multi criteria Analysis (MCA) in Geographical Information System (GIS) environment was used to achieve the objective of the study. The geological and hydrometeorological parameters were analyzed by making five classes of each parameter. The classes are ranked as most favorable and least favorable with numerical weights. The weights were assigned in accordance to their importance in debris flow occurrence. Then weighted overlay analysis techniques were applied to develop composite map representing the importance of each factor. Debris flow risk zonation map was resulted into four classes very high risk zones, high risk zone, moderate zone, low risk zone. The geology of the study area is diverse with frequents earthquakes. Similarly the forest cover is decreasing due to anthropogenic activities. The area is also characterized by long cold winters with frost action. These factors are destabilizing the slope. During summer season rain storm event results high surface runoff and peak discharge in the perennial and non-perennial channels which results flood and debris flow. These events result human life loss and disruption. The main villages located in very high risk zone are Mulkoh, Mastuj, Reshun, Shegram, Terich Gol, Rogar, Asurat, Boni, Brep and Rech Tockhow. They have been frequently affected by hazard in the past decade. Out of the total area, very high risk zone is expanded over 8%, high risk zone is expanded over16%, moderate risk zone is 29% and the rest is low risk zone. This study has highlighted the risk zones which will help disaster management authorities and policy makers to reduce the risk of debris flow in future.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114264162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents the analysis of seismic signatures generated during passage of hurricanes Newton(September 2016) and Willa (October 2018), recorded on the daily helicorders of a short-period seismic station at thedistances about 450 km from the tracks of events. This view from seismic station allowed to obtain the following results.Periods of passage of these tropical storms and hurricanes were identified. Analysis of the dynamics of sequences ofthese seismic signals allowed to separate the time intervals of increase and decrease in the development of atmosphericdisturbances. The spectral analysis of the signals of tropical storm Newton and hurricane Willa showed that the spectralamplitudes of signals, recorded during the maximum stage of activity of the tropical storm, were larger than the samefor the maximum stage of activity of the hurricane. This may be related to the presence of intensive hailstorms duringtropical storm.
{"title":"Dynamics of Hurricanes and Tropical Storms along the Pacific Coast of Mexico: A View from a Seismic Station","authors":"V. Zobin","doi":"10.54963/ptnd.v1i2.71","DOIUrl":"https://doi.org/10.54963/ptnd.v1i2.71","url":null,"abstract":"This study presents the analysis of seismic signatures generated during passage of hurricanes Newton(September 2016) and Willa (October 2018), recorded on the daily helicorders of a short-period seismic station at thedistances about 450 km from the tracks of events. This view from seismic station allowed to obtain the following results.Periods of passage of these tropical storms and hurricanes were identified. Analysis of the dynamics of sequences ofthese seismic signals allowed to separate the time intervals of increase and decrease in the development of atmosphericdisturbances. The spectral analysis of the signals of tropical storm Newton and hurricane Willa showed that the spectralamplitudes of signals, recorded during the maximum stage of activity of the tropical storm, were larger than the samefor the maximum stage of activity of the hurricane. This may be related to the presence of intensive hailstorms duringtropical storm.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Millions of motor vehicle crashes and tens of thousands of resulting deaths occur each year in the United States. While it is well known that wintry conditions make driving more difficult and dangerous, it is difficult to quantify and communicate the threat to motorists, especially in real time. This proof-of-concept research uses machine learning (ML) to approach this problem in a new way by creating a ML model that can identify snow on the road froma traf c camera image. This information is coupled with the number of coincident vehicular crashes to provide detailedconsideration of the impact of snow on the road to motorists and transportation agency decision-makers. It was foundthat, during meteorological winter, when the ML model determined there to be snow on the road in a traf c camera image, the chance of a vehicular crash pairing with that traf c camera increased by 61%. The systems developed as part of this research have potential to assist roadway of cials in assessing risk in real time and making informed decisionsabout snow removal and road closures. Moreover, the implementation of in-vehicle weather hazard information could promote driver safety and allow motorists to adjust their driving behavior and travel decision making as well.
{"title":"Correlating Machine Learning Classi cation of Traf c Camera Images with Snow-related Vehicular Crashes in New York State","authors":"Joshua Chang, C. Walker","doi":"10.54963/ptnd.v1i2.65","DOIUrl":"https://doi.org/10.54963/ptnd.v1i2.65","url":null,"abstract":"Millions of motor vehicle crashes and tens of thousands of resulting deaths occur each year in the United States. While it is well known that wintry conditions make driving more difficult and dangerous, it is difficult to quantify and communicate the threat to motorists, especially in real time. This proof-of-concept research uses machine learning (ML) to approach this problem in a new way by creating a ML model that can identify snow on the road froma traf c camera image. This information is coupled with the number of coincident vehicular crashes to provide detailedconsideration of the impact of snow on the road to motorists and transportation agency decision-makers. It was foundthat, during meteorological winter, when the ML model determined there to be snow on the road in a traf c camera image, the chance of a vehicular crash pairing with that traf c camera increased by 61%. The systems developed as part of this research have potential to assist roadway of cials in assessing risk in real time and making informed decisionsabout snow removal and road closures. Moreover, the implementation of in-vehicle weather hazard information could promote driver safety and allow motorists to adjust their driving behavior and travel decision making as well.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128891050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Well-coordinated efforts are being made in dishing out meteorological services by the NigerianMeteorological Agency to ensure the delivery of timely, accurate and quality weather information for the developmentof agricultural activities in the drylands of Nigeria for sustainable food security. This is through the densification ofthe observations networks, acquisition of the relevant and latest weather instruments and improvement in weatherequipment and training of technical data capturing system, data delivery system, calibration of equipment, installation,routine maintenance of the equipment and training and retraining of staff .This work is aimed at giving an insight tothe efforts being made by NIMET towards the issuance of accurate weather information for agricultural development.The information contained in Seasonal Rainfall Prediction (SRP), 2019 and 2020 is given consideration in this studyespecially as it relates to agriculture for food security in the drylands of Nigeria.
{"title":"Improvement of Meteorological Services for Food Security in the Drylands of Nigeria","authors":"A. Hussaini","doi":"10.54963/ptnd.v1i1.59","DOIUrl":"https://doi.org/10.54963/ptnd.v1i1.59","url":null,"abstract":"Well-coordinated efforts are being made in dishing out meteorological services by the NigerianMeteorological Agency to ensure the delivery of timely, accurate and quality weather information for the developmentof agricultural activities in the drylands of Nigeria for sustainable food security. This is through the densification ofthe observations networks, acquisition of the relevant and latest weather instruments and improvement in weatherequipment and training of technical data capturing system, data delivery system, calibration of equipment, installation,routine maintenance of the equipment and training and retraining of staff .This work is aimed at giving an insight tothe efforts being made by NIMET towards the issuance of accurate weather information for agricultural development.The information contained in Seasonal Rainfall Prediction (SRP), 2019 and 2020 is given consideration in this studyespecially as it relates to agriculture for food security in the drylands of Nigeria.","PeriodicalId":325067,"journal":{"name":"Prevention and Treatment of Natural Disasters","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132212615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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