## 1 Introduction
Tidal-induced sea level changes have been obtained based on the past observations of the long-term sea level tidal oscillations from the relevant tidal gauge. * _Estimation of the risk of flooding for humans_: Using a digital terrain model where each pixel is georeferenced (X, Y coordinates) and assigned altitude Z, the calculated sea heights will be compared with the altitudes, and if the sea elevation is greater than the altitude Z of a pixel, that pixel will be marked as flooded. According to the depth of the sea on land, the risk of flooding for humans will be defined based on an analytical function so that it can be easily integrated into the rest of the system. This information is necessary for the input into the early warning system. For a simple and understandable presentation of the flooding risk, the analyzed coastal area is divided into zones in order to define the total sea level. This division is made according to the criteria of the coast type and the coast height, which have a direct impact on the height of the waves. * _Dissemination and communication of risk information by mobile application_: Flood warnings will be given to people who have the mobile application, which was developed for the purpose of the dissemination of information about flood risk in the observed area. Those that have installed the application on their mobile phone and enabled that app to send them push notifications will receive the information. Recently, Internet of Things (IoT) systems that monitor in different scenarios to ensure a green, sustainable future have been widely used to create smart environments tailored to particular human needs [17; 18]. Affordable equipment, miniaturized in its deployment, is ensured a long lifetime through solar/battery power; the communication ranges are also extremely increased and can be measured in kilometers in urban areas, which is especially pertinent in meteorological scenarios [19; 20]. In this study, the established sea level monitoring system uses an IoT-based, solar-powered anemometer ([PERSON] design--Meteo Wind) and solar-powered meteo-station ([PERSON] design Meteo-helix) for monitoring air pressure. The system is deployed to acquire current meteorological information used as an input for the developed model to estimate the sea level. The system is based on the LoRaWAN IoT radio, which delivers the information to The Things Network (TTN) cloud system, which is then used by the cross-platform web/mobile application called Waves, which is used as an early warning monitoring system. The multilanguage system itself implements: (1) the algorithm that defines sea level based on the tidal, wind speed/direction and air pressure information; (2) information on the shore height at the dedicated measurement and early warning zones; (3) push notification logics that can be separately activated based on user's zone of the interest. This paper presents a methodological approach for the development of an early warning system for coastal flooding. It is based on frequently measured meteorological data and sea level predictions integrated on a single platform to provide real-time information about potential risk for the citizens of the city of Kastela.