Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models
BACKGROUND AND OBJECTIVES: Flooding is one of the biggest challenges affecting the economy and people's well-being. Previous studies have used several methods to analyze spatial data and deliver a more efficient flood response, including machine learning techniques to support decision-making in...
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2024-10-01
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| Series: | Global Journal of Environmental Science and Management |
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| Online Access: | https://www.gjesm.net/article_713741_07c6898ee3a8ab2de65eaf23739cea88.pdf |
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| author | M. Srivanit S. Pattanasri N. Phichetkunbodee S. Manokeaw S. Sitthikankun D. Rinchumphu |
| author_facet | M. Srivanit S. Pattanasri N. Phichetkunbodee S. Manokeaw S. Sitthikankun D. Rinchumphu |
| author_sort | M. Srivanit |
| collection | DOAJ |
| description | BACKGROUND AND OBJECTIVES: Flooding is one of the biggest challenges affecting the economy and people's well-being. Previous studies have used several methods to analyze spatial data and deliver a more efficient flood response, including machine learning techniques to support decision-making in the urban planning process. However, different machine learning models serve different purposes depending on their learning processes and computation techniques. This study aims to develop a machine learning model for assessing flood risk zones to provide helpful information for city administration and planning and to support the well-being and resilience of the city's residents.METHODS: To develop a method for assessing flood risk zones and provide helpful information for city administration and planning. Eight urban factors were input into eleven multiclass classification algorithms to assess flood risk, and the results were displayed on a geographic information systems map.FINDINGS: The study discovered that the bagging decision tree algorithm model produced the best flood risk assessment model, with an accuracy of 88.58 percent compared to the government's flood simulation model results. Furthermore, rainfall, building coverage ratio, and floor area ratio were the three most important variables determining flood risk.CONCLUSION: The Bagging Decision Tree Algorithm model effectively assesses flood risk, offering valuable insights for city administration and planning. Integrating key variables such as rainfall, building coverage ratio, and floor area ratio into flood risk management strategies is crucial for mitigating the impact of floods in economically significant urban areas. |
| format | Article |
| id | doaj-art-fa6a9b68e77b49678e1db32b495068fc |
| institution | Kabale University |
| issn | 2383-3572 2383-3866 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | GJESM Publisher |
| record_format | Article |
| series | Global Journal of Environmental Science and Management |
| spelling | doaj-art-fa6a9b68e77b49678e1db32b495068fc2025-02-02T08:11:21ZengGJESM PublisherGlobal Journal of Environmental Science and Management2383-35722383-38662024-10-011041501151810.22034/gjesm.2024.04.02713741Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning modelsM. Srivanit0S. Pattanasri1N. Phichetkunbodee2S. Manokeaw3S. Sitthikankun4D. Rinchumphu5Urban Planning Programs, Thammasat University, ThailandUrban Planning Programs, Thammasat University, ThailandDepartment of Civil Engineering, National Taiwan University, TaiwanOffice of Research Administration, Chiang Mai University, ThailandDepartment of Industrial Technology, Chiang Mai Rajabhat University, ThailandDepartment of Civil Engineering, Chiang Mai University, ThailandBACKGROUND AND OBJECTIVES: Flooding is one of the biggest challenges affecting the economy and people's well-being. Previous studies have used several methods to analyze spatial data and deliver a more efficient flood response, including machine learning techniques to support decision-making in the urban planning process. However, different machine learning models serve different purposes depending on their learning processes and computation techniques. This study aims to develop a machine learning model for assessing flood risk zones to provide helpful information for city administration and planning and to support the well-being and resilience of the city's residents.METHODS: To develop a method for assessing flood risk zones and provide helpful information for city administration and planning. Eight urban factors were input into eleven multiclass classification algorithms to assess flood risk, and the results were displayed on a geographic information systems map.FINDINGS: The study discovered that the bagging decision tree algorithm model produced the best flood risk assessment model, with an accuracy of 88.58 percent compared to the government's flood simulation model results. Furthermore, rainfall, building coverage ratio, and floor area ratio were the three most important variables determining flood risk.CONCLUSION: The Bagging Decision Tree Algorithm model effectively assesses flood risk, offering valuable insights for city administration and planning. Integrating key variables such as rainfall, building coverage ratio, and floor area ratio into flood risk management strategies is crucial for mitigating the impact of floods in economically significant urban areas.https://www.gjesm.net/article_713741_07c6898ee3a8ab2de65eaf23739cea88.pdfflood susceptiblemachine learningresilient cityurban form |
| spellingShingle | M. Srivanit S. Pattanasri N. Phichetkunbodee S. Manokeaw S. Sitthikankun D. Rinchumphu Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models Global Journal of Environmental Science and Management flood susceptible machine learning resilient city urban form |
| title | Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models |
| title_full | Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models |
| title_fullStr | Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models |
| title_full_unstemmed | Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models |
| title_short | Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models |
| title_sort | analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models |
| topic | flood susceptible machine learning resilient city urban form |
| url | https://www.gjesm.net/article_713741_07c6898ee3a8ab2de65eaf23739cea88.pdf |
| work_keys_str_mv | AT msrivanit analysisofsensitiveurbanformindicatorsoffloodsusceptiblepredictionbasedonmachinelearningmodels AT spattanasri analysisofsensitiveurbanformindicatorsoffloodsusceptiblepredictionbasedonmachinelearningmodels AT nphichetkunbodee analysisofsensitiveurbanformindicatorsoffloodsusceptiblepredictionbasedonmachinelearningmodels AT smanokeaw analysisofsensitiveurbanformindicatorsoffloodsusceptiblepredictionbasedonmachinelearningmodels AT ssitthikankun analysisofsensitiveurbanformindicatorsoffloodsusceptiblepredictionbasedonmachinelearningmodels AT drinchumphu analysisofsensitiveurbanformindicatorsoffloodsusceptiblepredictionbasedonmachinelearningmodels |