Coastal water quality assessment is an essential task to keep "good water quality" status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess water quality but this technique has received much criticism due to the model's reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models' performance, the RMSE, MSE, MAE, R², and PREI metrics were used in this study. The tree-based DT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R² = 1.0 and PERI = 0.0) and the ExT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R² = 1.0 and PERI = 0.0) and ensemble tree-based XGB (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R² = 1.0 and PERI = +0.16 to -0.17) and RF (RMSE = 2.0, MSE = 3.80, MAE = 1.10, R² = 0.98, PERI = +3.52 to -25.38) models outperformed other models. The results of model performance and PREI indicate that the DT, ExT, and GXB models could be effective, robust and significantly reduce model uncertainty in predicting WQIs. The findings of this study are also useful for reducing model uncertainty and optimizing the WQM-WQI model architecture for predicting WQI values.

Wave characteristic assessments of wave energy test sites provide a greater understanding of prevailing wave conditions and are therefore extremely important to both wave energy test site operators and clients as they can inform wave energy converter design, optimisation, deployment, operation and maintenance. This research presents an assessment of the wave resource at the Atlantic Marine Energy Test Site (AMETS) on the west coast of Ireland based on 12-years of modelled data from January 2004 to December 2015. The primary aim is to provide an assessment of annual and seasonal wave characteristics and resource variability at the two deployment berths which comprise the site. A nested model has been developed using Simulating WAves Nearshore (SWAN) to replicate wave propagations from regional to local scale with a 0.05° resolution model covering the northeast Atlantic and a 0.0027° resolution model covering AMETS. The coarse and fine models have been extensively validated against available measured data within Irish waters. 12-year model outputs from the high resolution model were analysed to determine mean and maximum conditions and operational, high and extreme event conditions for significant wave height, energy period and power. Annual and seasonal analyses are presented. The 12-year annual mean P were 68 kW/m at Berth A (BA) and 57 kW/m at Berth B (BB). The resource shows strong seasonal and annual variations and the winter mean power levels were found to be strongly correlated with the North Atlantic Oscillation (NAO).

The emergence of tidal energy as a key renewable energy source requires the development of computational design models for accurate prediction of turbine performance and wake effects whilst also being computationally efficient. In this paper, we develop and validate a three-dimensional CFD model for vertical axis turbines, which achieves high accuracy. We also investigate the limitations of two-dimensional models and present a blockage correction for improved prediction. The two-dimensional blockage correction model is potentially attractive for preliminary design studies due to its computational advantage over three-dimensional models.

In the long-term, tidal turbines will most likely be deployed in farms/arrays where energy extraction by one turbine may significantly affect the energy available to another turbine. Given the prohibitive cost of experimental and/or field investigations of such turbine interactions, numerical models can play a significant role in determining the optimum layout of tidal turbine arrays with respect to energy capture. In the present research, a low-cost modelling solution for optimising turbine array layouts is presented and assessed. Nesting is used in a far-field model to telescope spatial resolution down to the scale of the turbines within the turbine array, allowing simulation of the interactions between adjacent turbines as well as the hydrodynamic impacts of individual turbines. The turbines are incorporated as momentum sinks. The results show that the model can compute turbine wakes with similar far-field spatial extents and velocity deficits to those measured in published experimental studies. The results show that optimum spacings for multi-row arrays with regard to power yield are 3–4 rotor diameters (RD) across-stream and 1–4 RD along-stream, and that turbines in downstream rows should be staggered to avoid wake effects of upstream turbines and to make use of the accelerated flows induced by adjacent upstream turbines.

Nested circulation models developed to date either exclude the flooding and drying process or prohibit flooding and drying of nested boundaries; they are therefore ill-suited to the accurate modelling of inter-tidal areas. The authors have developed a nested model with moving boundaries which permits flooding and drying of both the interior domain and the nested boundaries. The model uses a novel approach to boundary formulation; ghost cells are incorporated adjacent to the nested boundary cells so that the nested boundaries operate as internal boundaries. When combined with a tailored adaptive interpolation technique, the approach facilitates a dynamic internal boundary. Details of model development are presented with particular emphasis on the treatment of the nested boundary. Results are presented for Cork Harbour, a natural coastal system with an extensive inter-tidal zone and a complex flow regime which provided a rigorous test of model performance. The nested model was found to achieve the accuracy of a high resolution single grid model for a much lower computational cost.

A numerical wave model is developed to simulate wave conditions at the 1/4 scale wave energy test site in Galway Bay on the west coast of Ireland. The Simulating Waves Nearshore gridded wave model (version 41.01) is used and a nesting methodology has been implemented to downscale from oceanic scale to a local scale. The nesting methodology does not require the development of a global wave model and instead uses freely available data from existing global models to provide boundary conditions to the regional coarse model. The nesting methodology can be relatively easily implemented for different parts of the world's oceans depending on available data and study requirements. In the present application, the regional coarse domain covers a large area of the north-eastern Atlantic Ocean at a resolution of 0.05° and the local Galway Bay domain is resolved at 0.0027° (approximately 300 m). Boundary conditions for the local high-resolution model are supplied from the coarse model. A detailed description of model input preparation is presented. The models were calibrated against wave buoy stations within the north-eastern Atlantic Ocean for the period of November 2013 and sensitivity tests were conducted on domain size, wave growth formulations and boundary specification. Model validation was conducted for a 3-year hindcast from 2011 to 2013 and it is shown that the developed wave model performs satisfactorily for this longer period and is therefore suitable for use as a forecast model.