This study aims to explore the possibility of employing remote sensing images to build a probabilistic flood extent forecasting model. This model is constructed and tested in two study areas: New Orleans and Miami. Images that recorded flooding events are first performed with segmentation method Seed Region Growing, and segmented images are classified by Maximum Likelihood classifier. Area detected as water subtracting the permanent water area is the detected flood extent. In total there are nineteen images being processed. The flood detection result is validated by flooded locations from NOAA flood reports and the news, and the accuracy is at 70.4%. The detection result, with flood conditioning factors which include precipitation, sea level, elevation, drainage capacity and distance to the water area, is the input to the probabilistic forecasting model. All inputs are standardised to a common grid system and every cell in that grid system contains a set of data. Two kinds of model structures are proposed and both models are trained with logistic regression and probit regression, both of which are the members of the Generalised Linear Model(GLM). The first kind of model structure is only tested in the New Orleans study area and the second kind of model structure is examined at both study areas. The precision of the first model structure is at 20% with a kappa value at zero. For the second model structure, over-sampling method SMOTE is used to increase the number of data points of the class 'flooded'. The highest precision of the second model structure at New Orleans is 12.6% and at Miami 23.6%, and the highest cohen's kappa values are 0.127 and 0.131 for New Orleans and Miami respectively. The first model structure actually failed in building a success model at a large portion of the study area due to limited records. For the second kind of model structure, most variables are linked to the flooding by the model correctly. The precipitation has a positive relation with flooding, especially when time effect is considered. Elevation reduces the probability of flooding. At the Miami study area where no sea dike exists the sea level has a strong positive relation with flooding. Drainage capacity used in the New Orleans study area does not show an influence on flooding, which requires modelling the intricate drainage system more accurately. In Miami study area, when the study area is confined to the seaside and Miami Beach area, the accuracy of prediction is improved, which informs that land-use type is crucial to be considered in the input. This study innovative collected information from several remote sensing images of different flood events and applied the information to build a probabilistic model, which shows that the information provided by mages could link flooding conditioning factors with flooding. It is recommended to incorporate the remote sensing technique in the flood extent forecast model in the future.

Installation and maintenance strategies regarding offshore wind farm operations involve extensive logistics, since the main focus is the right temporal and spatial placement of personnel and equipment, while taking into account forecasted meteorological and hydrodynamic conditions. In order for these operations to be successful weather windows characterized by certain permissive wave, wind and current conditions is of enormous importance, whereas unforeseen events result in high cost and risk in terms of safety.
For that purpose, Deltares created Meteo Dashboard, an integrated software system that collects, stores, computes and presents measured and forecasted meteorological and hydrodynamic data for decision making of maintenance or installation activities in an offshore wind farm. The wind speed, as well as the air and water temperatures, result from a meteorological model and serve as an input for the numerical modelling (e.g. SWAN or Delft3D) of waves, water levels and current related parameters. To account for the inherited uncertainty, several error modelling techniques, such as Artificial Neural Networks (ANN), Copulas, Stochastic Interpolation (SI), ARMA models, and Linear Regression (REG), already run operational on Meteo Dashboard and can be implemented in order for the numerical model forecasts to be corrected. A number of the aforementioned techniques require training using historical or present time data, while others can be incorporated forthwith.
In this research, a fully automated ARIMA model and different kinds of Bayesian Network (BN) models are incorporated in order to enhance the accuracy of the significant wave height (Hs) predictions even further. Both techniques are implemented using packages provided by the free software environment of R, namely the bnlearn and forecast. The implemented BN models differ in terms of training and structure, and provide overall the most satisfying accuracy in comparison to the rest of the error correction techniques, when tested with data retrieved from stations deployed in the Irish Sea (adjacent to the Gwynt-y-Mor and Rhyl Flats offshore wind farms) corresponding to the whole year of 2017 (from January 2017 – to January 2018).
Supplementary, it is also shown that the BN models illustrate even more advantages when compared to the rest of the error correction techniques, since they provide information about the incorporated variables dependence relationship through their structures, while producing estimates for the underlying uncertainty of the phenomenon, by means of 95% confidence intervals extracted by the significant wave height (Hs) conditional distribution.
Finally, all error correction models are tested in operational (online) mode, with real-time data from the aforementioned locations, with the newly implemented BN models producing results of enhanced accuracy, even in the absence of measurements