Gridded Flash Flood Risk Index Coupling Statistical Approaches and TOPLATS Land Surface Model for Mountainous Areas

Lee, Byong-Ju; Kim, Sangil

doi:10.3390/w11030504

Cited by 10 publications

(7 citation statements)

References 45 publications

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“…The uncertainty estimation for these methods should account for both the rainfall data aggregation step [31] and the uncertainties of the own input data used [32]. Recent efforts applied to enhance the representativeness of the uncertainty associated to FFG values include the consideration of the spatial rainfall features defined through statistical analysis [33] and the adoption of Bayesian probabilistic approaches to consider the fact that the same amount of accumulated rainfall may or may not trigger floods [34].…”

Section: Rainfall Comparison With Surface Conditions Considered (Rc-sc)mentioning

confidence: 99%

Recent Advances in Real-Time Pluvial Flash Flood Forecasting

Zanchetta

Coulibaly

2020

Water

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Recent years have witnessed considerable developments in multiple fields with the potential to enhance our capability of forecasting pluvial flash floods, one of the most costly environmental hazards in terms of both property damage and loss of life. This work provides a summary and description of recent advances related to insights on atmospheric conditions that precede extreme rainfall events, to the development of monitoring systems of relevant hydrometeorological parameters, and to the operational adoption of weather and hydrological models towards the prediction of flash floods. With the exponential increase of available data and computational power, most of the efforts are being directed towards the improvement of multi-source data blending and assimilation techniques, as well as assembling approaches for uncertainty estimation. For urban environments, in which the need for high-resolution simulations demands computationally expensive systems, query-based approaches have been explored for the timely retrieval of pre-simulated flood inundation forecasts. Within the concept of the Internet of Things, the extensive deployment of low-cost sensors opens opportunities from the perspective of denser monitoring capabilities. However, different environmental conditions and uneven distribution of data and resources usually leads to the adoption of site-specific solutions for flash flood forecasting in the context of early warning systems.

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Section: Rainfall Comparison With Surface Conditions Considered (Rc-sc)mentioning

confidence: 99%

Recent Advances in Real-Time Pluvial Flash Flood Forecasting

Zanchetta

Coulibaly

2020

Water

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“…Due to the destructive effects of flash floods on the environment and their social consequences, many studies so far have attempted flood risk modeling and zoning [17][18][19], because identifying areas vulnerable to flooding will be one of the most effective measures to reduce flood damage and flood management [20]. However, risk modeling and flood sensitivity mapping across large areas still remain challenging, because flash floods occur largely in each region under different climate conditions, which are unpredictable [21].…”

Section: Introductionmentioning

confidence: 99%

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

et al. 2020

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Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.

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“…Jumlah dan distribusi curah hujan merupakan hal yang sangat sulit untuk diprediksi, karena terdapat unsur ketidakpastian serta kompleksnya permasalahan suatu DAS menjadi pemicu utama terjadinya banjir bandang (Lee & Kim, 2019). Banjir bandang sering terjadi secara tiba-tiba dan sangat cepat dalam waktu yang singkat, menyapu semua yang dilaluinya dan menimbulkan kerugian baik materi maupun kerugian yang lainnya (Doswell, 2015).…”

Section: Pendahuluanunclassified

IDENTIFIKASI KERENTANAN BANJIR BANDANG MENGGUNAKAN METODE AHP (ANALYTICAL HIERARCHY PROCESS) BERBASIS SIG DI SUB DAS AIR MULAK, KABUPATEN LAHAT (Identification of Flash Flood Vulnerability Using GIS-Based AHP (Analytical Hierarchy Process) Method in Air Mulak Sub-watershed, Lahat Regency)

Dhiniati¹,

Dinata²

2022

JPPDAS

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The high rainfall for a long time resulted in flash floods that hit three villages in Lahat Regency, submerged dozens of residents' houses, damaged several public facilities, and cut off-road access. From this incident, spatial information is needed related to areas prone to flash floods so that the negative impacts can be minimized. This study aims to identify the vulnerability of flash floods in the Air Mulak Watershed. This research uses the AHP (Analytical Hierarchy Process) method for rational decision-making of all parameters, integrated with GIS (Geographical Information System) applications. The results of the consistency test for all parameters, the CR value of 0.97% is smaller than 10% and is considered consistent. The results of scoring all parameters are then overlaid by giving weight to each parameter, and it can be seen that the distribution of flash flood susceptibility in Air Mulak Sub-Watershed with a very high percentage of 0.20%, high class of 4.75%, moderate class of 15.78%, low at 43.50%, and very low at 35.78%. The results showed that the parameters that greatly affect flash flooding in the Air Mulak Sub-Watershed are altitude, river density, slope, and distance from the river because the area is where water accumulates from upstream of the river when it rains with high intensity.

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Gridded Flash Flood Risk Index Coupling Statistical Approaches and TOPLATS Land Surface Model for Mountainous Areas

Cited by 10 publications

References 45 publications

Recent Advances in Real-Time Pluvial Flash Flood Forecasting

Recent Advances in Real-Time Pluvial Flash Flood Forecasting

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

IDENTIFIKASI KERENTANAN BANJIR BANDANG MENGGUNAKAN METODE AHP (ANALYTICAL HIERARCHY PROCESS) BERBASIS SIG DI SUB DAS AIR MULAK, KABUPATEN LAHAT (Identification of Flash Flood Vulnerability Using GIS-Based AHP (Analytical Hierarchy Process) Method in Air Mulak Sub-watershed, Lahat Regency)

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