scholarly journals Integrate Weather Radar and Monitoring Devices for Urban Flooding Surveillance

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 825 ◽  
Author(s):  
Shih-Yen Hsu ◽  
Tai-Been Chen ◽  
Wei-Chang Du ◽  
Jyh-Horng Wu ◽  
Shih-Chieh Chen
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Weather Radar ◽  
Water Levels ◽  
Mitigation Measures ◽  
Extreme Weather Events ◽  
Television System ◽  
Urban Flood ◽  
Flood Monitoring ◽  
Image Capturing

With the increase of extreme weather events, the frequency and severity of urban flood events in the world are increasing drastically. Therefore, this study develops ARMT (automatic combined ground weather radar and CCTV (Closed Circuit Television System) images for real-time flood monitoring), which integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity. Furthermore, ARMT combines CCTV image capturing, analysis, and Fourier processing, identification, water level estimation, and data transmission to provide real-time warning information. Furthermore, the hydrograph data can serve as references for relevant disaster prevention, and response personnel may take advantage of them and make judgements based on them. The ARMT was tested through historical data input, which showed its reliability to be between 83% to 92%. In addition, when applied to real-time monitoring and analysis (e.g., typhoon), it had a reliability of 79% to 93%. With the technology providing information about both images and quantified water levels in flood monitoring, decision makers can quickly better understand the on-site situation so as to make an evacuation decision before the flood disaster occurs as well as discuss appropriate mitigation measures after the disaster to reduce the adverse effects that flooding poses on urban areas.

Street Level Flood Monitoring and Warning System, a conceptual model using IoT: A Case of Surat City

2021 ◽  
Vol 14 (12) ◽  
pp. 55-65
Author(s):  
Anant Patel ◽  
Sanjay Yadav
Keyword(s):  
Developing Countries ◽  
Water Level ◽  
Warning System ◽  
Water Levels ◽  
Mitigation Measures ◽  
Developed Country ◽  
River Bank ◽  
Street Level ◽  
Flood Warning ◽  
Flood Monitoring

Most of the natural disasters are unpredictable, but the most frequent occurring catastrophic event over the globe is flood. Developing countries are severely affected by the floods because of the high frequencies of floods. The developing countries do not have good forecasting system compared to the developed country. The metro cities are also settled near the coast or river bank which are the most vulnerable places to floods. This study proposes plan for street level flood monitoring and warning system for the Surat city, India. Waterlogging happens in the low lying area of the Surat city due to heavy storm and heavy releases from the Ukai dam. The high releases from upstream Ukai dam and heavy rainfall resulted into flooding in the low lying area of the Surat city. This research proposed a wireless water level sensor network system for the street water level flood monitoring. The system is proposed to monitor the water levels of different areas of city through the wireless water level sensors as well as to capture live photos using CCTV camera. This will help authority not only to issue flood warning but also to plan flood mitigation measures and evacuation of people.

scholarly journals Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

Development of a Decision Support System for Flood Warning in Jinan City

2011 ◽  
Vol 219-220 ◽  
pp. 1267-1270 ◽  
Author(s):  
Chuan Qi Li ◽  
Chao Jia ◽  
Bang Shu Xu
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Urban Areas ◽  
Flood Simulation ◽  
Urban Flood ◽  
Web Based ◽  
Flood Warning ◽  
Jinan City

A decision support system for flood warning has been developed for Jinan city. It is a web based distributed system that integrates GIS, databases and models. Urban Flood Simulation model is used as a real-time flood forecasting model. Mike Flood model is used to simulate pre-formulated flood scenarios for urban areas. The objective of the system is to simulate and forecast river and urban floods on the basis of real-time meteorological situation and rainfall available, and to serve as a tool for making decision.

scholarly journals Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1577
Author(s):  
David C. Mason ◽  
John Bevington ◽  
Sarah L. Dance ◽  
Beatriz Revilla-Romero ◽  
Richard Smith ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
Rural Areas ◽  
Urban Areas ◽  
Flood Hazard ◽  
Synthetic Aperture ◽  
Hazard Maps ◽  
Urban Flood ◽  
Flood Hazard Maps ◽  
Aperture Radar

Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method.

X-band weather radar monitoring real-time products in Rome and Naples urban areas

2012 ◽  
Author(s):  
Frank S. Marzano ◽  
Errico Picciotti ◽  
Mario Montopoli ◽  
Giorgio Budillon ◽  
Angelo Zinzi ◽  
...  
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Weather Radar ◽  
X Band ◽  
Radar Monitoring

scholarly journals Study on Water Level Prediction Using Observational Data from a Multi-Parameter Phased Array Weather Radar

2021 ◽  
Vol 16 (3) ◽  
pp. 410-414
Author(s):  
Kazuhiro Yoshimi ◽  
Masakazu Wada ◽  
Yukio Hiraoka ◽  
◽  
Keyword(s):  
Water Level ◽  
Observational Data ◽  
Urban Areas ◽  
High Speed ◽  
Phased Array ◽  
Three Dimensional ◽  
Weather Radar ◽  
Water Levels ◽  
Hydrological Processes ◽  
Basic Study

A dual-polarization, phased array weather radar, also known as the multi-parameter phased array weather radar (MP-PAWR), was developed by the Japanese Cross-ministerial Strategic Innovation Promotion (SIP) Program. Since this weather radar has been made into an active phased array, three-dimensional observation of weather phenomena can be realized at high speed by means of electrical scanning in the elevation direction and mechanical scanning in the azimuth direction. This is expected to shed light on hydrological processes in river basins, such as those of urban rivers, and improve prediction accuracy. In this study, river water levels in urban areas were estimated from vertically integrated liquid (VIL) Nowcast water content results, a meteorological forecasting method based on the three-dimensional observation MP-PAWR data, using a synthesized rational formula. A runoff analysis for urban basins was carried out using the rainfall forecast results based on MP-PAWR observational data. Since it is known that this formula can be used to deliver a rapid response time for runoff phenomena in the basin, it is possible to fully exploit the features of the MP-PAWR. This study shows how MP-PAWR is used in a series of hydrological processes. In this paper, we report the results of a basic study on water level predictions based on MP-PAWR observational data and also present future prospects for the use of this technology.

scholarly journals Real-time urban flood forecasting and modelling – a state of the art

2013 ◽  
Vol 15 (3) ◽  
pp. 717-736 ◽  
Author(s):  
Justine Henonin ◽  
Beniamino Russo ◽  
Ole Mark ◽  
Philippe Gourbesville
Keyword(s):  
Real Time ◽  
Flood Risk ◽  
Urban Areas ◽  
Urban Drainage ◽  
Flood Forecast ◽  
Coupled Models ◽  
Urban Flood ◽  
Maximum Rainfall ◽  
One Dimensional ◽  
Drainage Networks

All urban drainage networks are designed to manage a maximum rainfall. This situation implies an accepted flood risk for any greater rainfall event. This risk is often underestimated as factors such as city growth and climate change are ignored. But even major structural changes cannot guarantee that urban drainage networks would cope with all future rain events. Thus, being able to forecast urban flooding in real time is one of the main issues of integrated flood risk management. Runoff and hydraulic models can be essential elements of flood forecast systems, as an active part of the system or as studying tools. This paper gives an overview of current available options for pluvial flood modelling in urban areas, from basic estimations with a one-dimensional urban drainage model to detailed flood process representation with one dimensional–two dimensional hydrodynamic coupled models. Each type of modelling solution is described with pros and cons regarding urban flood analysis. The paper then elaborates on real-time flood forecast systems and the influence of their main components. A classification of real-time urban flood systems is given based on the use of urban models, i.e. empirical scenarios, pre-simulated scenarios and real-time simulations. A review of existing operational systems is done using this classification.

scholarly journals Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada

2020 ◽  
Vol 12 (19) ◽  
pp. 3141
Author(s):  
Ian Olthof ◽  
Nicolas Svacina
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Urban Areas ◽  
Cloud Cover ◽  
Data Availability ◽  
Flood Mapping ◽  
Eastern Canada ◽  
Urban Flood ◽  
Water Height

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points.

scholarly journals 1D/2D porosity model for urban flood modeling: case of a dense street networks

2018 ◽  
Vol 40 ◽  
pp. 06010
Author(s):  
Pascal Finaud-Guyot ◽  
Pierre-André Garambois ◽  
Shangzhi Chen ◽  
Guilhem Dellinger ◽  
Abdellah Ghenaim ◽  
...  
Keyword(s):  
Urban Areas ◽  
Mitigation Measures ◽  
Flood Modeling ◽  
Urban Floods ◽  
Urban Flood ◽  
Urban Networks ◽  
Cut Cell ◽  
Flow Features ◽  
Prediction Systems ◽  
New Generation

An increasing urbanization of floodplains has led to higher vul- nerability of urban areas and building a new generation of robust, accurate and computationally affordable models dedicated to urban floods is highly re- quired for improving prediction systems and mitigation measures. A better understanding of urban floods hydrodynamics may also be required. In view to achieve computationally affordable and reliable simulations a new 1D/2D parsimonious hydraulic model Flood1D2D is introduced for flood modeling in complex branched urban networks. It takes advantage of a cut-cell technique and a new effective model able to take into account some essential sub-grid flow features such as flow vein contractions due to recirculations downstream of a crossroad. It is shown that this local phenomenon can be correctly repro- duced thanks to this 1D/2D coupled SW model parameterized with roughness and porosity. The ability of the model to reproduce realistic flood discharge distributions at the subdistrict scale is also tested on a part of the branched net- work of ICUBE experimental rig. Further studies should tackle the issues of calibration and unsteady modeling.

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