## 1 Introduction  
Coastal flooding events, including their occurrence and triggers for the appearance of waves, have been investigated in the past to contribute to a better understanding of flood events in coastal areas as well as efficient risk management. Recent investigations have studied the occurrence of storm and sea wave events [1, 2], the numerical modelling of wave propagation in coastal areas [3], flood water movement over land areas [4], the impact of storms on coasts [5] and flood vulnerability [6]. Special attention has been given to the development of an early warning system (EWS) for the timely warning of residents in exposed areas as well. Early warning systems are also an integral part of coastal flood management plans and contribute to the development of long-term management strategies [7]. Existing sea-state monitoring technology, historical databases, numerical forecasting models and computer science have been parts of the operational coastal flood early warning system [8]. A EWS modelling framework based on a Bayesian network has been used to link coastal hazards to their socio-economic and environmental factors [9] and connect available field measurements, data obtained from numerical wave simulations and an empirical wave run-up approach [10]. An EWS that determines the total sea level height by combining predictions of tides and sea level anomalies with wave runup estimates has been presented in [11]. Technological progress in computation and communication sciences in the last decade allows for work with large databases that can store registered meteorological data that affect the occurrence of sea floods. This type of data represents a basis for developing different forecasting platforms, which use input data and boundary conditions from global or regional scales and open sea and weather forecast databases, providing wave propagation in the ports [12]. These platforms use a number of effective hydrodynamic numerical models for the simulation of storm surge and the prediction of sea water level [13]. It is important to notice that the sea water level prediction procedure in real time is a time-consuming process which needs significant computational resources, as well as the development and monitoring of a software architecture [14] to model coupling and integration. The models based on machine learning [15] and artificial intelligence provide easy implementation of numerical models with low computational cost, as well as fast training, validation, testing and evaluation [16]. In the present study, coastal flooding caused by the simultaneous combination of several mechanisms, such as tidal-induced sea level oscillations resulting from barometric pressure changes and wind-generated waves, have been investigated in the Kastela Bay. A special focus has been given to the area of the city of Kastela, placed at the north part of the bay (Figure 1), with many cultural and historical buildings and/or areas located near the coastline, which are potentially endangered by coastal flooding and subject to significant consequences and damage (Figure 2). The design of the early warning system for sea flooding risk in the city of Kastela includes the following components:  
* _A monitoring system_ consisting of an installation of hardware at the site location and appropriate software for real-time data collection. This system observes the wind speed and the barometric pressure. * _Application of mathematical and numerical models for sea level prediction on the coastline_: Numerical model is used to analyze the wave transform mechanism. A specific procedure has been established to incorporate simultaneous effects of wind-generated waves, tides and barometric pressure-induced changes in the sea level to determine sea level at the coastline based on the systematic observations of the abovementioned parameters via a real-time monitoring system (wind speed and direction and barometric pressure).