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Special Issue "Sensing Technology for Flood Monitoring and Forecasting"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 2889

Special Issue Editors

Department of Geosciences, University of Oslo, N-0316 Oslo, Norway
Interests: hydrological modeling; flood forecasting, regionalization; uncertainty; impact of climate change and land use change; evapotranspiration
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Interests: image recognition; radar technology; hydrology monitoring; hydrology simulation; artificial intelligence; remote sensing
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Hydrology-Water Resources and Hydraulics Engineering, Hohai University, Nanjing 210098, China
Interests: remote sensing and GIS applications; hydrological modeling; statistical downscaling; climate change and land use/land cover change impact on water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the rapid development of information technology, more and more emerging technologies have been applied in water resource management, such as remote sensing (RS), Artificial Intelligence (AI), the Internet of Things (IoT), intelligent image recognition, etc. These technologies can be directly applied to the monitoring of hydrological variables and can also be indirectly applied to hydrological modeling, providing technical support for flood forecasting and warning in a basin. RS technology can be applied to rainfall observation to obtain the continuous spatial distribution of rainfall, such as TRMM and GPM satellites, and it can also be used to monitor the changing of soil moisture, glaciers, lake water body, and flood inundation, which greatly enrich the traditional means of hydrological observation. IoT technology provides new technical means for data collection and transmission, which overcomes the difficulty of data acquisition in some remote areas. Image recognition technologies are also widely used in water level and flow velocity monitoring, such as particle image velocimetry (PIV) and space–time image velocimetry (STIV), which provide new technical means for quickly obtaining water level and flow data. These sensing technologies greatly enrich the ways for flood forecasting and early warning and also provide strong technical support for improving the accuracy of flood forecasting.

Therefore, this Special Issue is aimed at representing the latest advances on current efforts to aid advancing flood monitoring and management through new sensing technologies. We welcome contributions in all fields of remote sensing, flood modeling, flood monitoring, including new systems, signal processing algorithms, as well as new applications. Those include but are not limited to:

RS and GIS in flood forecasting

Flood monitoring and mapping

Flood inundation modelling

Multiple satellite precipitation estimation

The IoT applied in flood monitoring

Spatial data downscaling and assimilation

Image recognition technologies

Particle image velocimetry (PIV)

Space–time image velocimetry (STIV)

AI in flooding forecasting and warning

You may choose our Joint Special Issue in Water.

Prof. Dr. Chong-Yu Xu
Prof. Dr. Hua Chen
Prof. Dr. Zengxin Zhang
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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16 pages, 4178 KiB  
Technical Note
Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method
Sensors 2021, 21(16), 5614; - 20 Aug 2021
Cited by 3 | Viewed by 2450
Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, to expand flood depth data [...] Read more.
Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, to expand flood depth data source taking use of online image resources in an efficient manner, an automated, low-cost, and real-time working frame called FloodMask was developed to obtain flood depth from online images containing flooded traffic signs. The method was built on the deep learning framework of Mask R-CNN (regional convolutional neural network), trained by collected and manually annotated traffic sign images. Following further the proposed image processing frame, flood depth data were retrieved more efficiently than manual estimations. As the main results, the flood depth estimates from images (without any mirror reflection and other inference problems) have an average error of 0.11 m, when compared to human visual inspection measurements. This developed method can be further coupled with street CCTV cameras, social media photos, and on-board vehicle cameras to facilitate the development of a smart city with a prompt and efficient flood monitoring system. In future studies, distortion and mirror reflection should be tackled properly to increase the quality of the flood depth estimates. Full article
(This article belongs to the Special Issue Sensing Technology for Flood Monitoring and Forecasting)
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