Statistical analysis of crowd behaviour in catastrophic situation
Machine learning (ML) is one of the emerging domains in classification and prediction. It is important to understand the responses of individuals in crowd during an earthquake emergency for making appropriate earthquake emergency management plan. Our research is focused on predicting the behaviour o...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Mehran University of Engineering and Technology
2022-07-01
|
| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/2514 |
| _version_ | 1811339143439974400 |
|---|---|
| author | Muhammad Ehtesham Tahir Nadir Abbas Muhammad Faisal Hayat Muhammad Nasir |
| author_facet | Muhammad Ehtesham Tahir Nadir Abbas Muhammad Faisal Hayat Muhammad Nasir |
| author_sort | Muhammad Ehtesham Tahir |
| collection | DOAJ |
| description | Machine learning (ML) is one of the emerging domains in classification and prediction. It is important to understand the responses of individuals in crowd during an earthquake emergency for making appropriate earthquake emergency management plan. Our research is focused on predicting the behaviour of individuals in a crowd during Catastrophic Situation. For this purpose, intended and actual behavioural response of crowd is collected by conducting a series of surveys. The attributes that are selected for result prediction are gender, age, affiliation, health status, training level, nearby exit, earthquake intensity, earthquake location, environmental status, and individual’s response. The dataset thus collected is divided into two crowds, Crowd 1 shows the intended behaviour whereas Crowd 2 shows actual. The decision tree, k-nearest neighbour, Naïve Bayes and neural network machine learning algorithms are used for predicting results. The results are analysed by using Rapid Miner as data mining tool. The dataset is split into two partitions. By applying randomization techniques like simple random sampling, shuffle random sampling, etc. we have trained and tested the machine learning algorithms. The results of this research will be a source of help in understanding critical details about crowd behaviour in earthquake emergency. |
| first_indexed | 2024-04-13T18:20:57Z |
| format | Article |
| id | doaj.art-9d2f8f6b9d564026b24c5a303624cc20 |
| institution | Directory Open Access Journal |
| issn | 0254-7821 2413-7219 |
| language | English |
| last_indexed | 2024-04-13T18:20:57Z |
| publishDate | 2022-07-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj.art-9d2f8f6b9d564026b24c5a303624cc202022-12-22T02:35:27ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192022-07-0141310411210.22581/muet1982.2203.102514Statistical analysis of crowd behaviour in catastrophic situationMuhammad Ehtesham Tahir0Nadir Abbas1Muhammad Faisal Hayat2Muhammad Nasir3Department of Computer Engineering, University of Engineering and Technology, Lahore PakistanDepartment of Computer Engineering, University of Engineering and Technology, Lahore PakistanDepartment of Computer Engineering, University of Engineering and Technology, Lahore PakistanDepartment of Computer Engineering, The University of Lahore, Lahore PakistanMachine learning (ML) is one of the emerging domains in classification and prediction. It is important to understand the responses of individuals in crowd during an earthquake emergency for making appropriate earthquake emergency management plan. Our research is focused on predicting the behaviour of individuals in a crowd during Catastrophic Situation. For this purpose, intended and actual behavioural response of crowd is collected by conducting a series of surveys. The attributes that are selected for result prediction are gender, age, affiliation, health status, training level, nearby exit, earthquake intensity, earthquake location, environmental status, and individual’s response. The dataset thus collected is divided into two crowds, Crowd 1 shows the intended behaviour whereas Crowd 2 shows actual. The decision tree, k-nearest neighbour, Naïve Bayes and neural network machine learning algorithms are used for predicting results. The results are analysed by using Rapid Miner as data mining tool. The dataset is split into two partitions. By applying randomization techniques like simple random sampling, shuffle random sampling, etc. we have trained and tested the machine learning algorithms. The results of this research will be a source of help in understanding critical details about crowd behaviour in earthquake emergency.https://publications.muet.edu.pk/index.php/muetrj/article/view/2514 |
| spellingShingle | Muhammad Ehtesham Tahir Nadir Abbas Muhammad Faisal Hayat Muhammad Nasir Statistical analysis of crowd behaviour in catastrophic situation Mehran University Research Journal of Engineering and Technology |
| title | Statistical analysis of crowd behaviour in catastrophic situation |
| title_full | Statistical analysis of crowd behaviour in catastrophic situation |
| title_fullStr | Statistical analysis of crowd behaviour in catastrophic situation |
| title_full_unstemmed | Statistical analysis of crowd behaviour in catastrophic situation |
| title_short | Statistical analysis of crowd behaviour in catastrophic situation |
| title_sort | statistical analysis of crowd behaviour in catastrophic situation |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/2514 |
| work_keys_str_mv | AT muhammadehteshamtahir statisticalanalysisofcrowdbehaviourincatastrophicsituation AT nadirabbas statisticalanalysisofcrowdbehaviourincatastrophicsituation AT muhammadfaisalhayat statisticalanalysisofcrowdbehaviourincatastrophicsituation AT muhammadnasir statisticalanalysisofcrowdbehaviourincatastrophicsituation |