Collaborating With Communities: Citizen Science Flood Monitoring in Urban Informal Settlements

Concerns regarding the impacts of climate change on marginalised communities in the Global South have led to calls for affected communities to be more active as agents in the process of planning for climate change. While the value of involving communities in risk management is increasingly accepted, the development of appropriate tools to support community engagement in flood risk management projects remains nascent. Using the Revitalising Informal Settlements and their Environments Program as a case study, the article interrogates the potential of citizen science to include disadvantaged urban communities in project-level flood risk reduction planning processes. This project collected more than 5,000 photos taken by 26 community members living in 13 informal settlements in Fiji and Indonesia between 2018 and 2020. The case study documents the method used as well as the results achieved within this two-year project. It discusses the method developed and implemented, outlines the main results, and provides lessons learned for others embarking on citizen science environmental monitoring projects. The case study indicates that the engagement model and the technology used were key to the success of the flood-monitoring project. The experiences with the practice of monitoring floods in collaboration with communities in Fiji and Indonesia provide insights into how similar projects could advance more participatory risk management practices. The article identifies how this kind of approach can collect valuable flood data while also promoting opportunities for local communities to be heard in the arena of risk reduction and climate change adaptation.

Eliciting User Requirement for Flood Monitoring Application

Flood disaster is the most disastrous hydrological event that can lead to property destruction and loss of lives. One of the efforts to mitigate the impact is by providing an advance technology in monitoring and event alert. The flood monitoring application is developed to provide real-time weather forecast and disaster warnings. To increase disaster management efficiency, we conducted this research to identify the needs and requirements for a flood monitoring application. The study is mainly focusing on user perspective and preferences. The participant of this survey includes the authorities, non-government agency (NGO) and public. The participant is given a set of questionnaires containing thirteen questions, including the combination of open-ended and close-ended questions covering three sub-topics: user background, user experience, and user knowledge. List of important flood monitoring application features based on user requirements analysis and empathy map has been used to visualize user attitudes and behaviours.

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

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.

Synergistic Use of Geospatial Data for Water Body Extraction from Sentinel-1 Images for Operational Flood Monitoring across Southeast Asia Using Deep Neural Networks

Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not been thoroughly tested for operational flood monitoring. Here, we present a novel water body extraction model based on a deep neural network that exploits Sentinel-1 data and flood-related geospatial datasets. For the model, the U-Net was customised and optimised to utilise Sentinel-1 data and other flood-related geospatial data, including digital elevation model (DEM), Slope, Aspect, Profile Curvature (PC), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Buffer for the Southeast Asia region. Testing and validation of the water body extraction model was applied to three Sentinel-1 images for Vietnam, Myanmar, and Bangladesh. By segmenting 384 Sentinel-1 images, model performance and segmentation accuracy for all of the 128 cases that the combination of stacked layers had determined were evaluated following the types of combined input layers. Of the 128 cases, 31 cases showed improvement in Overall Accuracy (OA), and 19 cases showed improvement in both averaged intersection over union (IOU) and F1 score for the three Sentinel-1 images segmented for water body extraction. The averaged OA, IOU, and F1 scores of the ‘Sentinel-1 VV’ band are 95.77, 80.35, and 88.85, respectively, whereas those of ‘band combination VV, Slope, PC, and TRI’ are 96.73, 85.42, and 92.08, showing improvement by exploiting geospatial data. Such improvement was further verified with water body extraction results for the Chindwin river basin, and quantitative analysis of ‘band combination VV, Slope, PC, and TRI’ showed an improvement of the F1 score by 7.68 percent compared to the segmentation output of the ‘Sentinel-1 VV’ band. Through this research, it was demonstrated that the accuracy of deep learning-based water body extraction from Sentinel-1 images can be improved up to 7.68 percent by employing geospatial data. To the best of our knowledge, this is the first work of research that demonstrates the synergistic use of geospatial data in deep learning-based water body extraction over wide areas. It is anticipated that the results of this research could be a valuable reference when deep neural networks are applied for satellite image segmentation for operational flood monitoring and when geospatial layers are employed to improve the accuracy of deep learning-based image segmentation.

A Computer Vision System for Staff Gauge in River Flood Monitoring

Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames.

Study on Construction of Hydrological Monitoring System of the Yangtze River and Application of New Technologies on Flood Monitoring

The flood of the Yangtze River has the characteristics of high peak, large quantity and long duration. The Yangtze River Hydrology Bureau summarizes and combs the complete business process chain of flood hydrological monitoring, and gradually constructs the Yangtze River flood hydrological monitoring system. Including station network layout, early warning response, monitoring technology, information processing, results output and other dimensions. The hydrological monitoring system of the Yangtze River flood has been gradually constructed and has been successfully applied in many flood basins. Especially under the special situation of COVID-19 epidemic situation in 2020 and the severe flood situation in the Yangtze River Basin, the scientific and practical nature and practicability of the hydrological monitoring system of the Yangtze River flood are further verified. In view of the shortcomings existing in the existing monitoring system, this paper looks forward to the frontier technologies involved in flood monitoring, and has a certain reference function for flood hydrological emergency monitoring.