Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

• DOI: 10.2166/hydro.2021.126
• Publication Date: 2021-04-26T16:50:06Z
• Authors: O. Mendoza-Cano; R. Aquino-Santos; J. López-de la Cruz; R. M. Edwards; A. Khouakhi; I. Pattison; V. Rangel-Licea; E. Castellanos-Be...; M. A. Martinez-Pr...; P. Rincón-Avalos; P. Lepper; A. Gutiérrez-Gómez; J. M. Uribe-Ramos; J. Ibarreche; I. Perez
• Keywords: 0207 environmental engineering; early warning systems; Information technology; 02 engineering and technology; 15. Life on land; T58.5-58.64; Environmental technology. Sanitary engineering; 6. Clean water; digital water network; flooding; 13. Climate action; 11. Sustainability; tropical storms; TD1-1066

Abstract Urban flooding is one of the major issues in many parts of the world, and its management is often challenging. One of the challenges highlighted by the hydrology and related communities is the need for more open data and monitoring of floods in space and time. In this paper, we present the development phases and experiments of an Internet of Things (IoT)-based wireless sensor network for hydrometeorological data collection and flood monitoring for the urban area of Colima-Villa de Álvarez in Mexico. The network is designed to collect fluvial water level, soil moisture and weather parameters that are transferred to the server and to a web application in real-time using IoT Message Queuing Telemetry Transport protocol over 3G and Wi-Fi networks. The network is tested during three different events of tropical storms that occurred over the area of Colima during the 2019 tropical cyclones season. The results show the ability of the smart water network to collect real-time hydrometeorological information during extreme events associated with tropical storms. The technology used for data transmission and acquisition made it possible to collect information at critical times for the city. Additionally, the data collected provided essential information for implementing and calibrating hydrological models and hydraulic models to generate flood inundation maps and identify critical infrastructure.