Survey on River Water Level Measuring Technologies: Case Study for Flood Management Purposes of the C2-SENSE Project

Bączyk, Anna; Piwiński, Jan; Kłoda, Rafał; Grygoruk, Mateusz

doi:10.1007/978-3-319-48923-0_65

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Several methods use sensors to estimate the water level flood warnings can work optimally [8]. However, several advantages and disadvantages are using this sensor, such as a pressure transducer type sensor which is accurate, so it can read with an accuracy of 1 mm and has good compatibility with the interfacing of several controllers [9]. On the other hand, the sensor is susceptible to changes in a vertical position from the first installation, which can decrease accuracy due to changes in hydrostatic pressure [8].…”

Section: Related Workmentioning

confidence: 99%

Accuracy of Computer Vision Hough Line Transform for Water Level Measurement

Owen

Wijaya

Wardhana

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Floods are natural disasters that often occur in countries with high rainfall, such as Indonesia. Floods occur due to sudden changes in water level; therefore, the dam cannot withstand the increase in water causing water to overflow into the riverbank area. As a result of this issue, a system was created that can be used to provide information about river water levels. This system relies on computer vision to program the camera to monitor the water level through image processing. Image processing allows the camera to process the image of the water surface detected at the ditch’s edge. The edge detection result is used as a water level detector using the Hough line transform, which generates a line to indicate the water level. The height detected by image processing differs from the actual height on the detected surface. The minimum and maximum error reading for both methods recorded in this experiment is 0,8 % and 16,67 %, respectively

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Section: Related Workmentioning

confidence: 99%

Accuracy of Computer Vision Hough Line Transform for Water Level Measurement

Owen

Wijaya

Wardhana

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

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“…There are several available sensors which are useful for estimating water level and, thus, improving early warning systems [29]. Bączyk et al [30] discussed the pros and cons of using these sensors to monitor and measure water level. The first type of sensor is a pressure transducer.…”

Section: Computer Vision and Iot Sensors For Early Warning Systemsmentioning

confidence: 99%

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Arshad

Ogie

Barthélemy

et al. 2019

Sensors

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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

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“…Addressing this issue is crucial in enhancing the effectiveness of flood warning systems and reducing the adverse impact of floods on vulnerable communities. However, despite the fact that staff gauges are cheap to implement, there are several challenges that are associated with this type of monitoring when using them for river level measurement [8]. Some of the challenges include errors due to bad angle reading, turbulence, or dirt on the scale due to debris.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania

Mdegela

Bock

Municio

et al. 2023

Sensors

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Reliable and accurate flood prediction in poorly gauged basins is challenging due to data scarcity, especially in developing countries where many rivers remain insufficiently monitored. This hinders the design and development of advanced flood prediction models and early warning systems. This paper introduces a multi-modal, sensor-based, near-real-time river monitoring system that produces a multi-feature data set for the Kikuletwa River in Northern Tanzania, an area frequently affected by floods. The system improves upon existing literature by collecting six parameters relevant to weather and river flood detection: current hour rainfall (mm), previous hour rainfall (mm/h), previous day rainfall (mm/day), river level (cm), wind speed (km/h), and wind direction. These data complement the existing local weather station functionalities and can be used for river monitoring and extreme weather prediction. Tanzanian river basins currently lack reliable mechanisms for accurately establishing river thresholds for anomaly detection, which is essential for flood prediction models. The proposed monitoring system addresses this issue by gathering information about river depth levels and weather conditions at multiple locations. This broadens the ground truth of river characteristics, ultimately improving the accuracy of flood predictions. We provide details on the monitoring system used to gather the data, as well as report on the methodology and the nature of the data. The discussion then focuses on the relevance of the data set in the context of flood prediction, the most suitable AI/ML-based forecasting approaches, and highlights potential applications beyond flood warning systems.

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Survey on River Water Level Measuring Technologies: Case Study for Flood Management Purposes of the C2-SENSE Project

Cited by 7 publications

References 9 publications

Accuracy of Computer Vision Hough Line Transform for Water Level Measurement

Accuracy of Computer Vision Hough Line Transform for Water Level Measurement

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania

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