Innovative solutions in monitoring systems in flood protection

Sekuła, Klaudia; Połeć, Marzena; Borecka, Aleksandra

doi:10.1051/e3sconf/20183001005

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Therefore, the developed alerting methods were not suitable as an early warning for prompt evacuation during flooding. Furthermore, Sekuła et al [22] developed a system that uses fiber optic technology to carry information to a central terminal where transmitters and repeaters are required. Another study developed a system that uses remote sensing satellite imagery to collect data from different flood-affected areas [23].…”

Section: Related Workmentioning

confidence: 99%

IoT-based system for automated floodwater detection and early warning in the East African region; a case study of arusha and dar es salaam, Tanzania

Uwayisenga¹,

Mduma²,

Ally³

2021

IJATEE

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Climate change is currently one of the most serious and extreme issues worldwide. Besides the contribution of human activities to climate change, the planet's climate is changing now much more than ever [1]. Due to climate-related and geophysical disasters, a total of 1.3 million people have died between 1988 and 2017, and a further 4.4 billion injured, homeless, and displaced [2]. Although a greater number of these disasters were due to geophysical events like earthquakes and tsunamis, 91% of all documented disasters due to natural hazards resulted from floods, droughts, tropical cyclones, heat waves, and other excessive weather events [3]. The effects of climate change are anticipated to further increase due to the general change and variability of climate.

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Section: Related Workmentioning

confidence: 99%

IoT-based system for automated floodwater detection and early warning in the East African region; a case study of arusha and dar es salaam, Tanzania

Uwayisenga¹,

Mduma²,

Ally³

2021

IJATEE

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“…Furthermore, another developed system (Sekuła et al, 2018) uses fiber optic technology to carry information to a central terminal where transmitters and repeaters are required. Another study developed a system that uses remote sensing satellite imagery to collect data from different flood-affected areas.…”

Section: Related Workmentioning

confidence: 99%

IoT-based system for automated floodwater detection and early warning in the East Africa Region: a case study of Arusha and Dar es salaam, Tanzania

Uwayisenga¹

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Climate change is a major cause of the increase of environmental natural disasters like floods, droughts, and storms. Although several countries have been affected by such natural disasters, the East African region is one of the most affected. This work focuses on floods as the most frequent disaster in the East African region where 280 died and about 2.8 million people were affected from the floods that occurred in 2019 alone. Different techniques have since been developed to mitigate the effects of floods. However, the methodologies used have not responded to the identified problems that include lack of community awareness, information inadequacy, and low-cost systems. To solve these problems, the present study aims at developing a low-cost system that detects and alerts the community on upcoming flood incidents. The proposed floodwater detection and early warning system comprise of three units. The sensing unit continuously monitors environmental parameters using ultrasonic, temperature and humidity sensors. The processing unit processes and analyses the collected data from sensors then, the alerting unit alerts the community and local authorities using a buzzer and a Short Message Service notification. The system uses the Global System for Mobile Communication to provide internet connectivity which enables data to be collected, stored, and monitored in the cloud. The system was implemented at Themi river and results showed that floods can be detected and the community near the flood-prone area alerted early. Therefore, the use of the developed system in flood-prone areas can contribute to environmental disaster risk mitigation. URI

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Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks

et al. 2021

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This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes.

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Innovative solutions in monitoring systems in flood protection

Cited by 3 publications

References 3 publications

IoT-based system for automated floodwater detection and early warning in the East African region; a case study of arusha and dar es salaam, Tanzania

IoT-based system for automated floodwater detection and early warning in the East African region; a case study of arusha and dar es salaam, Tanzania

IoT-based system for automated floodwater detection and early warning in the East Africa Region: a case study of Arusha and Dar es salaam, Tanzania

Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks

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