Urban flood hazard analysis in present and future climate after statistical downscaling: a case study in Ha Tinh city, Vietnam

2021 ◽  
Vol 18 (4) ◽  
pp. 257-274
Author(s):  
T. T. A. Le ◽  
N. T. Lan-Anh ◽  
V. Daskali ◽  
B. Verbist ◽  
K. C. Vu ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Flood Hazard ◽  
Hazard Analysis ◽  
Future Climate ◽  
Urban Flood

scholarly journals Spatial Modeling of Flood Sea Tides (Case Study: East Coast Semarang)

2004 ◽  
Vol 18 (1) ◽  
Author(s):  
Muhammad Aris Marfai
Keyword(s):  
Open Space ◽  
Flood Hazard ◽  
Hazard Analysis ◽  
Spatial Modelling ◽  
Flood Level ◽  
Digital Elevation ◽  
Elevation Model ◽  
Gis Software ◽  
The Impact

The aims of this research are 1) to construct a spatial model of tidal flood hazard, 2) to do hazard analysis of tidal flood. Spatial modelling has been generated using Geographic Information System (GIS) software and ILWIS software was seleccted to do the model operation. Neighborhood function and digital elevation model (DEM) have been applied on the modelling calculation process. DEM data was correted and menipulated using map calculation on the digital form. Tidal flood hazard analysis has been done by means of map calulation on the tidal flood hazard map and detail landuse map. Histogram and tabulation from the result of the map calculation have been analyzed to identify the impact of the tidal flood hazard on the landuse. The highest impact of the tidal flood hazard occurs on the 1 meter of tidal flood level, where in the inundation occurs mainly on the fishpond and yard/ open space area.

scholarly journals Predicting coastal urban floods using artificial neural network: The case study of Macau, China

2021 ◽  
Vol 11 (10) ◽  
Author(s):  
Weijun Dai ◽  
Zhiming Cai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Hazard ◽  
Scale Model ◽  
Coefficient Of Determination ◽  
Urban Flood ◽  
Flood Depth ◽  
Flood Disasters ◽  
Artificial Neural

AbstractUsing data-driven models to predict floods in advance is one of the current effective methods and hot researches to reduce urban flood disasters. In order to improve the prediction accuracy, it is necessary to select the appropriate flood hazard factors and the number of training samples to construct the prediction model. In our current research, an artificial neural network (i.e., the back-propagation neural network, BPNN) model was developed to predict the flood depth in the next hour. A case study of the urban flood during six typhoons in Macau of China was conducted to prove the performance of the proposed model. The flood depth was collected as output; after analyzing their correlation to the flood typhoon optimum track, urban weather, tides, geographic height and water depth increment of the submerged area were used as input. As a result, four models trained with different sample numbers were developed for training and testing. The model performances were examined using average absolute error, root mean square error and the coefficient of determination. The results show that in this case study, the 30-min scale model provides reliable predictions and can provide useful decision support for the prevention and mitigation of flood disasters in coastal urban.

Multi-Criteria Decision Making for Vulnerability Mapping of Flood Hazard: A Case Study of Pune City

2019 ◽  
Vol 2 (1) ◽  
pp. 41-52
Author(s):  
Nitin Mundhe
Keyword(s):  
Decision Making ◽  
Flood Hazard ◽  
Geographical Information ◽  
Multi Criteria Decision Making ◽  
Flood Hazards ◽  
Very High Frequency ◽  
Flood Vulnerability ◽  
Natural Risk ◽  
The Town

Floods are natural risk with a very high frequency, which causes to environmental, social, economic and human losses. The floods in the town happen mainly due to human made activities about the blockage of natural drainage, haphazard construction of roads, building, and high rainfall intensity. Detailed maps showing flood vulnerability areas are helpful in management of flood hazards. Therefore, present research focused on identifying flood vulnerability zones in the Pune City using multi-criteria decision-making approach in Geographical Information System (GIS) and inputs from remotely sensed imageries. Other input data considered for preparing base maps are census details, City maps, and fieldworks. The Pune City classified in to four flood vulnerability classes essential for flood risk management. About 5 per cent area shows high vulnerability for floods in localities namely Wakdewadi, some part of the Shivajinagar, Sangamwadi, Aundh, and Baner with high risk.

A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

2015 ◽  
Vol 36 (3) ◽  
pp. 308-323 ◽  
Author(s):  
Panchagnula Manjusree ◽  
Chandra Mohan Bhatt ◽  
Asiya Begum ◽  
Goru Srinivasa Rao ◽  
Veerubhotla Bhanumurthy
Keyword(s):  
Data Analysis ◽  
Satellite Data ◽  
Flood Hazard ◽  
North India ◽  
Hazard Evaluation

scholarly journals Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 873
Author(s):  
Yakob Umer ◽  
Janneke Ettema ◽  
Victor Jetten ◽  
Gert-Jan Steeneveld ◽  
Reinder Ronda
Keyword(s):  
High Intensity ◽  
Flood Hazard ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Event Triggering ◽  
Rain Events

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

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