# Flash Flood Early Warning System in Colima, Mexico
Vue.js is used for the frontend of the web framework and Google Firebase Cloud Messaging service is used to dispatch push notifications to the mobile end devices [10]. FLOODIS is a cloud-based flood emergency system which is composed of different subsystems that work independently; the main components are:GEO Gateway: Interfaces with all existing European Emergency Systems like Copernicus EMS and EFAS to provide flood extent and flood forecast maps. * Multi-platform Mobile Application: Sends real-time geolocated flood user reports, receives terrain maps together with additional layers and nearby user reports, and receives flood-related alerts from the authorities via FLOODIS. * Augmentation service: Receives GNSS (Global Navigation Satellite System) data from the mobile application and computes an augmented and validated GNSS position, featuring increased accuracy and integrity. * Service layer: Acts as the FLOODIS centralized service provider, implementing web services to receive/provide geolocated flood Reports from/to the mobile application. The system for detecting and forecasting natural disasters based on IoT [11] was modeled with the aid of the ns-3 simulator and compiled with the IoT standards available in the simulator. The authors in [12] describe the design and implementation of a real-time monitoring system for hydrologic applications. The proposed monitoring system presents useful characteristics such as large network capacity, sensor hardware compatibility, low power consumption, low cost, and minor impact on the natural environment. There are many low-cost early warning systems like EWIN; one of them is \"FLOOD MONITORING AND ALERTING SYSTEM\" [13]. It shares some similarities, although key features of the systems are different, like the alert trigger method and the precision of the sensors used. It is observed that EWIN might offer a superior approach, since it uses faster communication protocols and a centralized information system. In another similar work, \"The Implementation of an IoT-Based Flood Alert System\" [14], the alert processing is made at each node. The node processing power can be a handicap in comparison with the proposed system that uses a dedicated server for such task. In the state of Tabasco, Mexico, attempts have been made to develop early warning systems such as the \"Water Level Meter for Alerting Population about Floods\" [15]; in this case, only laboratory experimental tests are presented. EWIN is one step ahead--it has been tested in real hydrometeorological phenomena and the system has worked in a favorable way, as shown in this document. A detailed comparison of various early warning systems is found in the paper \"Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review\" [16]. EWIN follows the latest methods and recommendations for this type of system; it can be placed on par with the latest flood alert systems that use IoT technology. EWIN is an early warning system, adapted for the City of Colima, developed to alert the population of a possible overflow of the rivers, through the reading of hydrometeorological variables. The elements that help early warning are:  
RiverCore: They are fixed nodes and their main function is to measure the depth of the river in real-time. Drifters: They are mobile nodes; these are released into the river when there is a possible overflow; their contribution to the system is to better understand the behavior of the river in these events. Weather stations: These measures, in real-time, the variables: solar radiation, precipitation, vapor pressure, relative humidity, air temperature, humidity sensor temperature, barometric pressure, horizontal wind speed, wind gust humidity, wind direction, tilt, lightning strike count, and lightning average distance. These will be used in the future to complement the prediction model and allow more time for a possible evacuation.