The introduction of SARS-CoV-2 containing human stool and sewage into water bodies may raise public health concerns. However, assessment of public health risks by faecally contaminated water is limited by a lack of knowledge regarding the persistence of infectious SARS-CoV-2 in water. In the present study the decay rates of viable infectious SARS-CoV-2 and SARS-CoV-2 RNA were determined in river and seawater at 4 and 20°C. These decay rates were compared to S. typhimurium bacteriophage MS2 and pepper mild mottle virus (PMMoV). Persistence of viable SARS-CoV-2 was temperature dependent, remaining infectious for significantly longer periods of time in both freshwater and seawater at 4°C than at 20°C. T90 for infectious SARS-CoV-2 in river water was 2.3 days and 3.8 days at 20°C and 4°C, respectively. The T90 values were 1.1 days and 2.2 days in seawater at 20°C and 4°C, respectively. In contrast to the rapid inactivation of infectious SARS-CoV-2 in river and sea water, viral RNA was relatively stable. The RNA decay rates were increased in non-sterilised river and seawater, presumably due to the presence of microbiota. The decay rates of infectious MS2, MS2 RNA and PMMoV RNA differed significantly from the decay rate of SARS-CoV-2 RNA, suggesting that their use as surrogate markers for the persistence of SARS-CoV-2 in the environment is limited.

Accurate prediction of wave overtopping at sea defences remains central to the protection of lives, livelihoods, and infrastructural assets in coastal zones. In addressing the increased risks of rising sea levels and more frequent storm surges, robust assessment and prediction methods for overtopping prediction are increasingly important. Methods for predicting overtopping have typically relied on empirical relations based on physical modelling and numerical simulation data. In recent years, with advances in computational efficiency, data-driven techniques including advanced Machine Learning (ML) methods have become more readily applicable. However, the methodological appropriateness and performance evaluation of ML techniques for predicting wave overtopping at vertical seawalls has not been extensively studied. This study examines the predictive performance of four ML techniques, namely Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machines—Regression (SVR), and Artificial Neural Network (ANN) for overtopping discharge at vertical seawalls. The ML models are developed using data from the EurOtop (2018) database. Hyperparameter tuning is performed to curtail algorithms to the intrinsic features of the dataset. Feature Transformation and advanced Feature Selection methods are adopted to reduce data redundancy and overfitting. Comprehensive statistical analysis shows superior performance of the RF method, followed in turn by the GBDT, SVR, and ANN models, respectively. In addition to this, Decision Tree (DT) based methods such as GBDT and RF are shown to be more computationally efficient than SVR and ANN, with GBDT performing simulations more rapidly that other methods. This study shows that ML approaches can be adopted as a reliable and computationally effective method for evaluating wave overtopping at vertical seawalls across a wide range of hydrodynamic and structural conditions.