Identifying societal challenges in flood early warning systems

Perera, Duminda; Agnihotri, Jetal; Seidou, Ousmane; Djalante, Riyanti

doi:10.1016/j.ijdrr.2020.101794

Cited by 64 publications

(44 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Is the information being used to make the right decisions?". Regarding the second point, experience gained from Google's system demonstrates the importance of using the local language for the alerts, in accordance withPerera et al (2020) who suggest community-specific warning messages composed by their recipients Perera et al (2020). also emphasize the importance of targeted warnings, as opposed to warnings that are communicated to the entire population, which is the approach taken here.…”

supporting

confidence: 54%

Flood forecasting with machine learning models in an operational framework

Nevo

Morin

Rosenthal

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Google’s operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the Long Short-Term Memory (LSTM) networks and the Linear models. Flood inundation is computed with the Thresholding and the Manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The Manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the Linear model, while the Thresholding and Manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287,000 km2, home to more than 350M people. More than 100M flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations, as well as improving modeling capabilities and accuracy.

show abstract

supporting

confidence: 54%

Flood forecasting with machine learning models in an operational framework

Nevo

Morin

Rosenthal

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This framework is widely used in different researches related to the inclusiveness of EWSs. In the study by (Perera et al, 2020), the importance of this framework in assessing the social challenges of flood warning system is clearly described. Through the use of this framework, the study evaluated the disability inclusiveness of flood EWS in each element.…”

Section: Dpm 304/5mentioning

confidence: 99%

Disability inclusiveness of early warning system: a study on flood-prone areas of Bangladesh

Chisty

Nazim

Rahman

et al. 2021

DPM

View full text Add to dashboard Cite

PurposePersons with disabilities face the impacts of disasters differently. Early warning systems can be one of the powerful tools to reduce the vulnerabilities of persons with disabilities and mitigate the impacts of disasters. The main objective of this study was to assess the disability inclusiveness of the current early warning system (EWS) in flood-prone areas of Bangladesh.Design/methodology/approachA qualitative method was focused on getting in-depth information. Persons with disabilities participated in focus group discussions (FGDs) and shared the inclusiveness and gaps of the current EWS. Through extensive literature review, a checklist was developed to conduct the FGDs. QDA Miner 6.0.6 software was used for coding and analyzing the data.FindingsResults indicated that, though persons with disabilities have proper risk knowledge, the current monitoring and warning service, dissemination and communication, and response capability are not fully inclusive. A significant gap in the EWS was found in response capability. Even if somehow persons with disabilities manage to receive a warning about a flood, they lack the capacity to respond to the warning.Research limitations/implicationsThe study proposed that to make an EWS inclusive and effective, the concerned authorities should focus on all four parts of the EWS.Originality/valueStudies related to disability and disaster management are not very common. Conducting a qualitative study provided the persons with disabilities the opportunity to share their perspectives. Future studies can focus on vulnerability and capacity assessment of persons with disabilities to identify areas requiring interventions to enhance resilience.

show abstract

“…At the time of the discharge, a siren was activated, however the same siren has previously been used for non-life-threatening water releases. As a result, the warning wasn't properly interpreted (Perera et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automated Alert System for River Water Level and Water Quality Assessment using Telegram Bot API

Halim

Mahamad

Fuzi

2021

JCRINN

View full text Add to dashboard Cite

Technology has advanced to the point that it can assist people in their daily lives. Human beings may benefit from this development in a variety of ways. Progress in river water monitoring is also one of them. There are many advantages in improving the river water monitoring system. The objective of this project is to develop an automated system for monitoring river water levels and quality with push notification features. Internet of Things (IoT) was implemented in this research by using NodeMCU as a microcontroller to connect both ultrasonic sensors and pH sensors to the Internet. An ultrasonic sensor is used to read the water level, and a pH sensor is used to read the water pH values. The results show the successful output from all of 10 time attempts to obtain more accurate test results. The results will be averaged to be analysed and concluded from the test. All the tests include testing for the accuracy of the ultrasonic sensor, the accuracy of the pH sensor, and the performance of the internet connection using integrated Wi-Fi module in NodeMCU microcontroller. The system test also shows that it performs perfectly with the requirement needed to send the real-time status of the water level, water quality and an alert to the user using the Telegram Bot API. This research can help to increase the level of awareness of the river water monitoring system. This research was done by looking at people's problems in the vicinity of the river area by producing a system tool that helps to monitor the river water in real-time status.

show abstract

Identifying societal challenges in flood early warning systems

Cited by 64 publications

References 27 publications

Flood forecasting with machine learning models in an operational framework

Flood forecasting with machine learning models in an operational framework

Disability inclusiveness of early warning system: a study on flood-prone areas of Bangladesh

Automated Alert System for River Water Level and Water Quality Assessment using Telegram Bot API

Contact Info

Product

Resources

About