Monitoring and early warning system for rain-induced landslides in a ravine in Tacna: Implementation of a wireless sensor network

Descripción del Articulo

Landslides, triggered by heavy rains, constitute globally impactful natural disasters that wreak havoc on both people and infrastructure. It is crucial to develop predictive systems and efficient alerts, incorporating technologies such as the Internet of Things (IoT) to gather environmental data and...

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Detalles Bibliográficos
Autores: Córdova Miranda, Tito Leoncio, Rivera Herrera , Hugo Javier, Mamani Coaquira, Jeanpier Billy, Salgado Villanueva, William Eduardo, Maquera Aguilar, Mauricio Andree
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad Privada de Tacna
Repositorio:Revistas - Universidad Privada de Tacna
Lenguaje:español
OAI Identifier:oai:revistas.upt.edu.pe:article/855
Enlace del recurso:https://revistas.upt.edu.pe/ojs/index.php/ingenieria/article/view/855
Nivel de acceso:acceso abierto
Descripción
Sumario:Landslides, triggered by heavy rains, constitute globally impactful natural disasters that wreak havoc on both people and infrastructure. It is crucial to develop predictive systems and efficient alerts, incorporating technologies such as the Internet of Things (IoT) to gather environmental data and prevent significant losses. Therefore, the aim of the study was to design and develop an early warning system for landslides using a microcontroller-based wireless sensor network. A digital rocker rain gauge and the MPU 6050, DHT22 and FC-28 sensors were employed. These sensors were distributed at nodes along the Quebrada del Diablo in the city of Tacna. The values captured at each node were processed by the DEVKIT V1 NodeMCU-32 development board, based on the ESP32 microcontroller, and transmitted to a central node through a ZigBee mesh network using Digi's XBee3 devices. The central node was connected to the Blynk cloud via Wi-Fi to monitor the recorded values at each location. In the event of an alert condition, the alarm node is remotely activated, facilitating the evacuation of people at risk. The functionality of the wireless sensor network was verified by measuring the RSSI, thus demonstrating the effectiveness of data logging at the central node and remote alarm activation.
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