Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin

Such catastrophes may be brought on by floods to the impacted populace due to property damage, crop loss and death. Flooding caused significant damage to property and crops due to the high economic value of the property and the extent of the flood. Flood forecasts and warnings are one of the informa...

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Main Authors: Alkareem F.A., Sidek L.M., Salih G.H.A., Basri H., Sammen S.S.
Other Authors: 58905982500
Format: Book chapter
Published: Springer Science and Business Media Deutschland GmbH 2024
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spelling my.uniten.dspace-344022024-10-14T11:19:32Z Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin Alkareem F.A. Sidek L.M. Salih G.H.A. Basri H. Sammen S.S. 58905982500 35070506500 56239664100 57065823300 57192093108 Climate change Dungun River Basin Flood hazard Risk mitigation RTIBFF Warning system Such catastrophes may be brought on by floods to the impacted populace due to property damage, crop loss and death. Flooding caused significant damage to property and crops due to the high economic value of the property and the extent of the flood. Flood forecasts and warnings are one of the informal measures to provide warnings to affected populations. People living in flood-affected areas will be warned to evacuate their belongings before the flood arrives. This will greatly reduce the loss and damage caused by flooding, especially the loss of human life. This paper presents a comprehensive study of flood assessment and forecasting using Real-Time Flood Forecasting (RTIBFF) to assess the performance of IBF in identifying areas of potential flood risk by increasing the gap between the users and producers of timely information. Synergies are among several elements of an early warning system. Furthermore, in this paper, automated warning messages using color codes are used to initiate risk reduction measures at the local level for vulnerable groups in the Long Strait of Malaysia. RTIBFF collects information on the potential severity and likelihood of climate impacts. RTIBFF is still underutilized in Malaysia despite its extreme weather and potential catastrophe risk reduction benefits. The forecast, user understanding, and confidence are still questionable due to the forecast environment's uncertainty. To improve government-user collaboration, users should incorporate RTIBFF into popular weather forecast methodologies. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. Final 2024-10-14T03:19:32Z 2024-10-14T03:19:32Z 2023 Book chapter 10.1007/978-981-99-3708-0_62 2-s2.0-85185939426 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185939426&doi=10.1007%2f978-981-99-3708-0_62&partnerID=40&md5=64bcf801d3cc49053f8c49d94486f8c9 https://irepository.uniten.edu.my/handle/123456789/34402 Part F2265 881 897 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Climate change
Dungun River Basin
Flood hazard
Risk mitigation
RTIBFF
Warning system
spellingShingle Climate change
Dungun River Basin
Flood hazard
Risk mitigation
RTIBFF
Warning system
Alkareem F.A.
Sidek L.M.
Salih G.H.A.
Basri H.
Sammen S.S.
Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin
description Such catastrophes may be brought on by floods to the impacted populace due to property damage, crop loss and death. Flooding caused significant damage to property and crops due to the high economic value of the property and the extent of the flood. Flood forecasts and warnings are one of the informal measures to provide warnings to affected populations. People living in flood-affected areas will be warned to evacuate their belongings before the flood arrives. This will greatly reduce the loss and damage caused by flooding, especially the loss of human life. This paper presents a comprehensive study of flood assessment and forecasting using Real-Time Flood Forecasting (RTIBFF) to assess the performance of IBF in identifying areas of potential flood risk by increasing the gap between the users and producers of timely information. Synergies are among several elements of an early warning system. Furthermore, in this paper, automated warning messages using color codes are used to initiate risk reduction measures at the local level for vulnerable groups in the Long Strait of Malaysia. RTIBFF collects information on the potential severity and likelihood of climate impacts. RTIBFF is still underutilized in Malaysia despite its extreme weather and potential catastrophe risk reduction benefits. The forecast, user understanding, and confidence are still questionable due to the forecast environment's uncertainty. To improve government-user collaboration, users should incorporate RTIBFF into popular weather forecast methodologies. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
author2 58905982500
author_facet 58905982500
Alkareem F.A.
Sidek L.M.
Salih G.H.A.
Basri H.
Sammen S.S.
format Book chapter
author Alkareem F.A.
Sidek L.M.
Salih G.H.A.
Basri H.
Sammen S.S.
author_sort Alkareem F.A.
title Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin
title_short Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin
title_full Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin
title_fullStr Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin
title_full_unstemmed Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin
title_sort real time impact based flood forecasting (ibf) for tropical rivers: a case study in dungun river basin
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061054490050560
score 13.214268