Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy
The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in...
| Main Authors: | , , , , , |
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| Format: | UTAR Academic Staff Publication |
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Geospatial Health
2023
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| Subjects: | |
| Online Access: | https://geospatialhealth.net/index.php/gh/article/view/1236 http://eprints.utar.edu.my/6162/1/gh%2D18%2D2%2D1236.pdf |
| _version_ | 1848886605733953536 |
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| author | Nur Amalina, Mat Jan Muhammad Fadhil, Marsani Loshini, Thiruchelvam Nur Balqishanis, Zainal Abidin Ani, Shabri Sarah A'fifah, Abdullah Sani |
| author_facet | Nur Amalina, Mat Jan Muhammad Fadhil, Marsani Loshini, Thiruchelvam Nur Balqishanis, Zainal Abidin Ani, Shabri Sarah A'fifah, Abdullah Sani |
| author_sort | Nur Amalina, Mat Jan |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease. |
| first_indexed | 2025-11-15T19:41:09Z |
| format | UTAR Academic Staff Publication |
| id | utar-6162 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:41:09Z |
| publishDate | 2023 |
| publisher | Geospatial Health |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-61622023-12-15T08:17:54Z Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy Nur Amalina, Mat Jan Muhammad Fadhil, Marsani Loshini, Thiruchelvam Nur Balqishanis, Zainal Abidin Ani, Shabri Sarah A'fifah, Abdullah Sani GB Physical geography RA0421 Public health. Hygiene. Preventive Medicine The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease. Geospatial Health 2023 UTAR Academic Staff Publication NonPeerReviewed application/pdf http://eprints.utar.edu.my/6162/1/gh%2D18%2D2%2D1236.pdf https://geospatialhealth.net/index.php/gh/article/view/1236 Nur Amalina, Mat Jan and Muhammad Fadhil, Marsani and Loshini, Thiruchelvam and Nur Balqishanis, Zainal Abidin and Ani, Shabri and Sarah A'fifah, Abdullah Sani (2023) Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy. Geospatial Health. http://eprints.utar.edu.my/6162/ |
| spellingShingle | GB Physical geography RA0421 Public health. Hygiene. Preventive Medicine Nur Amalina, Mat Jan Muhammad Fadhil, Marsani Loshini, Thiruchelvam Nur Balqishanis, Zainal Abidin Ani, Shabri Sarah A'fifah, Abdullah Sani Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy |
| title | Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy |
| title_full | Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy |
| title_fullStr | Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy |
| title_full_unstemmed | Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy |
| title_short | Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy |
| title_sort | mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in malaysia based on natural disaster reduction strategy |
| topic | GB Physical geography RA0421 Public health. Hygiene. Preventive Medicine |
| url | https://geospatialhealth.net/index.php/gh/article/view/1236 https://geospatialhealth.net/index.php/gh/article/view/1236 http://eprints.utar.edu.my/6162/1/gh%2D18%2D2%2D1236.pdf |