Flood Early Warning System by Twitter Using LoRa

Leon, Ernesto; Alberoni, Cristian; Wister, Miguel A.; Hernández-Nolasco, José Adán

doi:10.3390/proceedings2191213

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…These findings suggest several courses of action to record, send, and monitor floods using a new generation of smart water level gauges [64]. Another important practical implication is, for example, as occurred in Tabasco, Mexico, that the climatic information from a flood early warning system is transmitted in real time and published using a social network (Twitter) by radio frequency (915 MHz) using LoRa modulation [65]. An implication of this is the possibility that in the LAC region, a simple extreme rainfall warning system can be implemented at low cost, due to the fact that the application of the CRHUDA model only requires three measured climatic variables.…”

Section: Discussionmentioning

confidence: 99%

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Gutiérrez-López

Cruz-Paz

Mandujano

2019

Climate

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Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods.

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Section: Discussionmentioning

confidence: 99%

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Gutiérrez-López

Cruz-Paz

Mandujano

2019

Climate

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“…The TTGO microcontroller processes the sensors data. The collected data are sent to a Raspberry Pi 3B + via LoRa [24]. The raspberry pi keeps the data on a database and in the same way, the data will be published in a Twitter account.…”

Section: Tabasco Méxicomentioning

confidence: 99%

Forecasting and communication key elements for low-cost fluvial flooding early warning system in urban areas

Acosta-Coll

Solano-Escorcia

Ortega-Gonzalez

et al. 2021

IJECE

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Fluvial flooding occurs when a river overspills its banks due to excessive rainfall, and it is the most common flood event. In urban areas, the increment of urbanization makes communities more susceptible to fluvial flooding since the excess of impervious surfaces reduced the natural permeable areas. As flood prevention strategies, early warning systems (EWS) are used to reduce damage and protect people, but key elements need to be selected. This manuscript proposes the monitoring instruments, communication protocols, and media to forecast and disseminate EWS alerts efficiently during fluvial floods in urban areas. First, we conducted a systematic review of different EWS architectures for fluvial floods in urban areas and identified that not all projects monitor the most important variables related to the formation of fluvial floods and most use communication protocols with high-energy consumption. ZigBee and LoRaWAN are the communication protocols with lower power consumption from the review, and to determine which technology has better performance in urban areas, two wireless sensor networks were deployed and simulated in two urban areas susceptible to fluvial floods using Radio Mobile software. The results showed that although Zigbee technology has better-received signal strength, the difference with LoRAWAN is lower than 2 dBm, but LoRaWAN has a better signal-to-noise ratio, power consumption, coverage, and deployment cost.

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“…An overview of the hardware setup is not provided, along with a description of the testing methods and results. In [39], a LoRa sensor node side analysis is carried out, with the discussion of a water height measuring approach showing how it is possible to measure water height through an ultrasonic sensor and implement a Twitter-based social network alarm, although this approach makes the alarm mechanism not generalizable to different alert interfaces. The database, interface and alarm developments are not reported.…”

Section: Related Workmentioning

confidence: 99%

“…Flooding can be caused by adverse rain conditions, wind, temperature changes [32]. A forecast based on a theoretical approach to this event is complex [33] and often not rapidly obtainable, therefore the development of early warning systems [34][35][36][37][38][39][40] is useful and necessary. In this work, a flood-monitoring system was developed, designed for installation and use in urban and non-urban settings, to obtain an early warning of the possible phenomenon.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Low-Power LoRa-Based Flood-Monitoring System

Ragnoli

Barile

Leoni

et al. 2020

JLPEA

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The development of Internet of Things (IoT) systems is a rapidly evolving scenario, thanks also to newly available low-power wide area network (LPWAN) technologies that are utilized for environmental monitoring purposes and to prevent potentially dangerous situations with smaller and less expensive physical structures. This paper presents the design, implementation and test results of a flood-monitoring system based on LoRa technology, tested in a real-world scenario. The entire system is designed in a modular perspective, in order to have the capability to interface different types of sensors without the need for making significant hardware changes to the proposed node architecture. The information is stored through a device equipped with sensors and a microcontroller, connected to a LoRa wireless module for sending data, which are then processed and stored through a web structure where the alarm function is implemented in case of flooding.

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Flood Early Warning System by Twitter Using LoRa

Cited by 16 publications

References 6 publications

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Forecasting and communication key elements for low-cost fluvial flooding early warning system in urban areas

An Autonomous Low-Power LoRa-Based Flood-Monitoring System

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