On the use of global flood forecasts and satellite-derived inundation maps for flood monitoring in data-sparse regions

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012-2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: 1) general agreement was found between the GFDS and MODIS flood detection systems, 2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and 3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, the satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large scale flood monitoring tools.JRC.H.7-Climate Risk Managemen

Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data

Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly available Sen1Flood11 benchmark dataset. The network outperformed the existing state-of-the-art (uni-temporal) flood detection method by 6\% IOU. The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.Comment: Accepted in IGARSS202

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy. The accuracy was reduced in urban areas partly because of TerraSAR-X’s restricted visibility of the ground surface due to radar shadow and layover

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively

Flood Detection and Prevention System

Flood is the most damaging natural disaster that happens everywhere in the world. The government would spend billions of ringgits to tackle it by creating public info flood website. The website could only be accessed by the user with an internet connection. To tackle this the researcher, develop a Flood Detection and Prevention System to sends a flood SMS alert directly to the user without the use of an internet connection. FDAP hardware consists of one microcontroller to process input and output, three sensors to detect rain, water level, and temperatures, and One GSM module to send SMS to the user directly to their phone. Development of the system will use Rapid Prototyping that focused on creating multiple prototypes until a finished product is developed. The product then will be analyzed for its usage on flood detection and prevention of flooding. A fully functional system will be developed including an SMS feature and a Web Server. Test involved functionality and acceptance test which uses a simulation environment that is created using a physical model and replica of flood phenomenon. FDAP system developed will be useful to rural villages to detect flood earlier and prevent it from harming live or properties. The finding of this study is expected to gain informative data for future flood analysis and study

A novel distributed denial-of-service detection algorithm

The applicability of implementing Ratio-based SYN Flood Detection (RSD) on a network processor is explored, and initial results are presented

A near real-time algorithm for flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management and flood forecasting. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy

Flood Detection Design based on the Internet of Things

Flood detection devices and water levels from several previous research studies were not optimal because they were still running and manual information, such as through loudspeakers, in some research, electronic devices have been used, but no information has been obtained, and it is not optimal if there is a danger sign. So this research is a study on the development of an automatic flood detection system and water level based on the IoT (Internet of Things). The system uses a NodeMCU Esp8266 controller with a combination of potentiometer sensors mounted on a water-level mechanic and connected to the Thingspeak IoT platform. Based on the results of the analysis and testing that have been done, the system is designed to combine the previous research algorithms so that it works more optimally and is better. The flood detection system and water level are made in two parts: one is placed upstream and the other is placed downstream, where the devices are connected. The device will turn on a danger alert when the altitude percentage is more than 85% of the maximum height. The lag time in the upload and download process is included in the Fast category (≤10 seconds). The resulting information can be monitored through the media portal website

Real-time cross-layer design for large-scale flood detection and attack trace-back mechanism in IEEE 802.11 wireless mesh networks

IEEE 802.11 WMN is an emerging next generation low-cost multi-hop wireless broadband provisioning technology. It has the capability of integrating wired and wireless networks such as LANs, IEEE 802.11 WLANs, IEEE 802.16 WMANs, and sensor networks. This kind of integration: large-scale coverage, decentralised and multi-hop architecture, multi-radios, multi-channel assignments, ad hoc connectivity support the maximum freedom of users to join or leave the network from anywhere and at anytime has made the situation far more complex. As a result broadband resources are exposed to various kinds of security attacks, particularly DoS attacks