RiverCore: IoT Device for River Water Level Monitoring over Cellular Communications

Moreno, Carlos; Aquino, Raúl; Ibarreche, José; Pérez, Ismael; Castellanos, Esli; Álvarez, Elisa; Rentería, Raúl; Anguiano, Luis; Edwards, Arthur; Lepper, Paul A.; Edwards, R.M.; Clark, Ben

doi:10.3390/s19010127

Cited by 48 publications

(42 citation statements)

References 20 publications

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“…Power management, the cellular communication method, and other components described in a previous paper [ 21 ] remain unchanged. Their initial design effectively handles the extra load and demands of the new hardware now incorporated on the fixed node.…”

Section: Methodsmentioning

confidence: 99%

Flash Flood Early Warning System in Colima, Mexico

Ibarreche

Aquino

Edwards

et al. 2020

Sensors

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This paper presents a system of sensors used in flash flood prediction that offers critical real-time information used to provide early warnings that can provide the minutes needed for persons to evacuate before imminent events. Flooding is one of the most serious natural disasters humans confront in terms of loss of life and results in long-term effects, which often have severely adverse social consequences. However, flash floods are potentially more dangerous to life because there is often little or no forewarning of the impending disaster. The Emergency Water Information Network (EWIN) offers a solution that integrates an early warning system, notifications, and real-time monitoring of flash flood risks. The platform has been implemented in Colima, Mexico covering the Colima and Villa de Alvarez metropolitan area. This platform consists of eight fixed riverside hydrological monitoring stations, eight meteorological stations, nomadic mobile monitoring stations called “drifters” used in the flow, and a sniffer with data muling capability. The results show that this platform effectively compiles and forwards information to decision-makers, government officials, and the general public, potentially providing valuable minutes for people to evacuate dangerous areas.

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Section: Methodsmentioning

confidence: 99%

Flash Flood Early Warning System in Colima, Mexico

Ibarreche

Aquino

Edwards

et al. 2020

Sensors

Self Cite

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“…Perumal et al [65] proposed the IoT enable water monitoring station. Furthermore, Moreno et al [66] and Purnomo et al [67] proposed an early detection system by embedding rainfall, river slope and temperature sensors to monitor a continuous change in water level and forecast flash flooding. Moreover, Mostafa et al [68] proposed a WSN along with a multi-agent system to classify whether the data coming from the sensors are valid or invalid.…”

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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“…Moreno et al emphasized that one of the most frequent and costly natural disasters that affect humankind is flooding [18]. Their developed architecture was based on the Message Queuing Telemetry Transport (MQTT) protocol, with security and encryption mechanisms, to send real-time data packages from fixed nodes to a server.…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

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In recent years, scarce water resources became one of the main problems that endanger human species existence and the advancement of any nation. In this research, smart water meters were implemented, distributed, and installed in a regional area in Cairo while data were collected at uniform intervals then sent to the cloud instantly. The solution paradigm uses an Internet of Things (IoT) based on micro-services and containers. The design incorporates real-time streaming and infrastructure performance optimization to store data. A second layer to analyze the acquired data was used to model water consumption using Long Short-Term Memory (LSTM). The designed LSTM is validated and tested to be utilized in the forecast of future water demand. Moreover, two alternative machine learning methods, namely Support Vector Regression and Random Forest commonly utilized in time series forecasting applications, were used for a comparative analysis of which LSTM has proven to be superior. The proper integration of the system elements is the key to the proposed system success. Based on the success of the designed system, it can be applicable on a national scale. That can enable the optimal management of consumers' demand and improve water infrastructure utilization. The proposed paradigm presents a testbed for various scenarios that can be used in water resources management.

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RiverCore: IoT Device for River Water Level Monitoring over Cellular Communications

Cited by 48 publications

References 20 publications

Flash Flood Early Warning System in Colima, Mexico

Flash Flood Early Warning System in Colima, Mexico

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

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

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