# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 3 State of the Art on Visualisation and Modelling of River Flooding
### Prediction Modelling  
[PERSON] et al. [14] pursued the objective of estimating vertical uncertainties of different DEMs on inundation simulations and examined the effect of different inundation models on quantifying flood risk and prevention. The study area was London and they justified it by specifying that London has 300,000 proprieties estimated to be near the tidal flood risk area. The bathtub method for the inundation simulation was used and it was assumed that an area with an elevation less than a projected flood level would be flooded. Flooding areas are determined through a simple calculation procedure in a GIS environment and the bathtub method can be filled with and without hydrological connectivity. They concluded that the adapted bathtub was a useful alternative to hydrodynamic models considering computational requirements and data availability. They also explained how a small difference in the vertical accuracy of a DEM has a strong effect on the quantitative assessment of a sea-level-rise inundation simulation. [PERSON] et al. [15] used and prototyped the installation of a hybrid physical-numerical flood model facility in an augmented reality environment. The installation consisted of an AR-sandbox module that can be used for educational purposes and prevention-risk-use cases. Emphasis was placed on addressing the \"inherent conflict\" when using numerical and physical models. They demonstrated that numerical models are nowadays more advanced and less complex than physical models. As precise and more complex as physical models are, their installation cost makes them an expensive solution for simple and non-complex flood simulations. The paper shows several existing prototypes and installations of interactive displays of topographic models created in the past: TerraVision, SANTI and MIT's SandScape. In physical models, testing alternatives in real time by changing a small part of the physical reality (objects in the test zone such as buildings, cars and voluntary obstructions) makes replication more complex. In a hybrid model, the real world (physical model) consists of a sandbox that is mapped through a Kinect sensor and recreated in a virtual environment. The numerical model is then applied and the simulation calculated is then projected onto the sandbox for visualisation, making it straightforward and practical for the end user, especially for educational applications. The paper concludes and notices that employing an explicit-in-time finite model solver results in a high-fidelity, physically based mathematical model. [PERSON] et al. [16] worked on the prediction of areas susceptible to floods and inundations in Nigeria, based on historical flooding records from 1985 to 2020. The authors stated that they used 15 variables like topographic, climatic and land use information. They also trained two machine learning models, an Artificial Neural Network (ANN) and Logistic Regression (LR), to develop and generate a susceptibility flood map. The methodology employed consisted of creating a geospatial database containing the conditioning variables and historical flood occurrences, building and validating the machine learning, and produced as an output a flood susceptibility map. As a result of the comparison between the two models, the ANN was better than the LR (76.4% and 62.5%). The authors concluded by stating that the use of machine learning can effectively map and predict flood-prone areas and can be used in policy and plan prevention. [PERSON] et al. [17] emphasised in a review of remote sensing technologies used for flood predictions that floods are uncertain as they depend on several climatic and environmental factors, and so, are difficult to predict. They stated that the improvement in the adoption of the latest technology is a must for disaster prediction and forecasting.