Artificial neural networks in merging wind wave forecasts with field observations

7-17An attempt to improve wind wave short-term forecasts based on artificial neural networks is reported. The novelty of the study consists in the use of relatively short time series of wave observations collected over 2 consecutive months to accomplish the tasks of wave predictions and data assimilation. Separate neural networks were developed to predict five wind wave parameters, namely, the significant wave height, zero-up-crossing wave period, peak wave period, mean direction at the peak period and directional spreading over intervals of 3, 6, 12 and 24 hours, and to correct these predictions. Data from a directional buoy were used to train and validate the networks. The results of the simulations carried out without and with the proposed methodology were favourably compared to time series of wave parameters estimated in the field. Moreover, time series plots and scatterplots of the wave characteristics as well as statistics show an improvement of the results achieved due to data merging

Augmented chaos-multiple linear regression approach for prediction of wave parameters

Prediction of wave parameters is one of the significant component for several coastal applications; for instance, coastal erosion, inshore and offshore structures, wave energy and others. The current research investigates the potential of the Chaos theory integrated with multiple linear regression (Chaos-MLR) in prediction of wave heights and wave periods. The wave data were collected at four moorings in the coastal environment of Tasmania. In the first stage, reconstructing the phase space and determine the input data for Chaos-MLR model, the delay time and embedding dimension are computed using average mutual information and false nearest neighbors’ analyses. The presence of chaotic dynamics in the used data is identified by the correlation dimension methods. In the second stage, the Chaos-MLR and pure MLR models are constructed for prediction model. Absolute error and best-fit-goodness diagnostic indicators are utilized to inspect the proficient of the proposed model in comparison with the pure MLR model. The inter-comparisons demonstrated that the Chaos-MLR and pure MLR models yield almost the same accuracy in predicting the significant wave heights and the zero-up-crossing wave periods. Whereas, the augmented Chaos-MLR model is performed better results in term of the prediction accuracy vis-a-vis the previous prediction applications of the same case study

Identification, impacts, and prioritisation of emerging contaminants present in the GBR and Torres Strait marine environments

Heavy/trace metals and metalloids are major anthropogenic contaminants in estuarine and coastal waters. Their concentrations in the GBR and TS marine environments are typically low, except in areas within ports and harbours and those adjacent to intense urban, industrial or agricultural activity. It is likely that heavy metal contamination in the GBR and TS will increase with increasing coastal and industrial development in these regions. This presents an ecological concern given the persistent nature of heavy metals and metalloids, known toxicity to marine organisms, and their estimated residence in the GBR lagoon ranging from years to decades. Metals and metalloids at some sites in the GBR and TS have concentrations that exceed water and sediment quality guidelines, indicating potential health risks to marine species. While point sources are often highly regulated to ensure that discharges and emissions of contaminants do not exceed levels of environmental concern, less is known about inputs from diffuse sources. For example, runoff from Papua New Guinean (PNG) catchments affects sediment quality in the northern and north-central TS. Similarly, our preliminary estimate of the dissolved aluminium load from the Calliope catchment near Gladstone suggests that diffuse source contribution could be considerable. This suggests that current management arrangements, which do not consider the risks of metals and metalloids from diffuse sources may need to be re-assessed and associated research recommendations are provided

Identification, impacts, and prioritisation of emerging contaminants present in the GBR and Torres Strait marine environments

Heavy/trace metals and metalloids are major anthropogenic contaminants in estuarine and coastal waters. Their concentrations in the GBR and TS marine environments are typically low, except in areas within ports and harbours and those adjacent to intense urban, industrial or agricultural activity. It is likely that heavy metal contamination in the GBR and TS will increase with increasing coastal and industrial development in these regions. This presents an ecological concern given the persistent nature of heavy metals and metalloids, known toxicity to marine organisms, and their estimated residence in the GBR lagoon ranging from years to decades. Metals and metalloids at some sites in the GBR and TS have concentrations that exceed water and sediment quality guidelines, indicating potential health risks to marine species. While point sources are often highly regulated to ensure that discharges and emissions of contaminants do not exceed levels of environmental concern, less is known about inputs from diffuse sources. For example, runoff from Papua New Guinean (PNG) catchments affects sediment quality in the northern and north-central TS. Similarly, our preliminary estimate of the dissolved aluminium load from the Calliope catchment near Gladstone suggests that diffuse source contribution could be considerable. This suggests that current management arrangements, which do not consider the risks of metals and metalloids from diffuse sources may need to be re-assessed and associated research recommendations are provided