Rapid Extreme Tropical Precipitation and Flood Inundation Mapping Framework (RETRACE): Initial Testing for the 2021–2022 Malaysia Flood

Tew, Yi Lin; Tan, Mou Leong; Juneng, Liew; Chun, Kwok Pan; Hassan, M. M. A.; Osman, Sazali; Samat, Narimah; Chang, Chun Kiat; Kabir, Muhammad Humayun

doi:10.3390/ijgi11070378

Cited by 11 publications

(4 citation statements)

References 54 publications

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“…However, many climate and remote sensing datasets have a low temporal resolution, typically recorded at daily or monthly intervals [4,5]. The limited temporal resolution of these data sets presents significant challenges in extracting detailed insights and limits their utility for analyzing rapid, transient events [6,7]. This constraint hinders a comprehensive understanding of environmental changes, especially in the context of short-term variations and extreme weather phenomena.…”

Section: Introductionmentioning

confidence: 99%

Interpolation of environmental data using deep learning and model inference

Ibebuchi,

Abu

2024

Mach. Learn.: Sci. Technol.

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The temporal resolution of environmental data sets plays a major role in the granularity of the information that can be derived from the data. In most cases, it is required that different data sets have a common temporal resolution to enable their consistent evaluations and applications in making informed decisions. This study leverages deep learning with long short-term memory (LSTM) neural networks and model inference to enhance the temporal resolution of climate datasets, specifically temperature, and precipitation, from daily to sub-daily scales. We trained our model to learn the relationship between daily and sub-daily data, subsequently applying this knowledge to increase the resolution of a separate dataset with a coarser (daily) temporal resolution. Our findings reveal a high degree of accuracy for temperature predictions, evidenced by a correlation of 0.99 and a mean absolute error of 0.21 °C, between the actual and predicted sub-daily values. In contrast, the approach was less effective for precipitation, achieving an explained variance of only 37%, compared to 98% for temperature. Further, besides the sub-daily interpolation of the climate data sets, we adapted our approach to increase the resolution of the Normalized difference vegetation index of Landsat (from 16-day to 5-day interval) using the LSTM model pre-trained from the Sentinel 2 Normalized difference vegetation index - that exists at a relatively higher temporal resolution. The explained variance between the predicted Landsat and Sentinel 1 data is 70% with a mean absolute error of 0.03. These results suggest that our method is particularly suitable for environmental datasets with less pronounced short-term variability, offering a promising tool for improving the resolution and utility of the data.

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Section: Introductionmentioning

confidence: 99%

Interpolation of environmental data using deep learning and model inference

Ibebuchi,

Abu

2024

Mach. Learn.: Sci. Technol.

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“…It is crucial to have a precise understanding of the temporal and spatial characteristics of rainfall and its impact on floods, which is an essential aspect of disaster mechanism research [19]. Various researchers have used rainfall indices, such as process total precipitation [20], daily cumulative precipitation [21,22], and precipitation 1-3 days before a flood [23][24][25], to analyse flood disaster risks. The risk of heavy rainfall is higher during typhoons and the flooding formation time is shorter, resulting in more severe disasters.…”

Section: Introductionmentioning

confidence: 99%

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Qin

Lin

et al. 2023

Remote Sensing

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Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters.

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“…The flood affected almost the entire area of the Malaysian Peninsula and caused severe damage, so it was considered as the greatest flood in human memory [2]. In addition to the severe flood in Kelantan in 1926, monsoon floods are actually an annual natural disaster in Malaysia, with severe floods occurring in 1996,2000,2006,2010,2011,2014 and 2021 [3][4]. Due to the flood, the people and the government faced huge losses and the economy of the people was affected [5].…”

Section: Introductionmentioning

confidence: 99%

Development of a Wireless Monitoring System to Monitor River Water Levels in Real Time

Mohd Zain,

Muhammad Razif,

Misman

et al. 2023

IJIE

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This wireless monitoring system is developed to alert and warn the resident regarding the upcoming flood. Due to the floods that occur every year, especially during the monsoon season, the loss and damage done by this disaster are uncontrollable. When the water level rise-up above the safe ground, it may hit the communication substation, as well as the power tower. In most cases, many residents are not alert with their surroundings because there is no alert system that can remind them regarding to the issues that will arise. By not preparing themselves for the upcoming nature disaster, it could endanger themselves and destroy their property. Thus, this project focus on monitoring a real-time river water level system for flood prediction purposes which utilize three devices connected together in a centralized system. By having this system, people will be notified about the current situation and the water level of the river near to them via Internet of Things (IoT) such as Blynk application. ESP32 and ESP32-Cam are used as the main microcontroller to control the system. The sensor used to detect the water level at the river is TF Mini Lidar and ultrasonic sensor for the water level detection of the rain gauge. The ESP32-cam are used to monitor the surrounding area at the river. The combination of these three systems is used to monitor and predict the flood occurrence.

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Rapid Extreme Tropical Precipitation and Flood Inundation Mapping Framework (RETRACE): Initial Testing for the 2021–2022 Malaysia Flood

Cited by 11 publications

References 54 publications

Interpolation of environmental data using deep learning and model inference

Interpolation of environmental data using deep learning and model inference

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Development of a Wireless Monitoring System to Monitor River Water Levels in Real Time

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