RESUMEN
In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. A detailed numerical investigation is necessary to minimize harmful effects and assess environmental impact. Selecting appropriate geometry and working conditions for minimizing such effects often requires numerous experiments and numerical simulations. For this reason, the application of machine learning models is proposed. Several models including Support Vector Regression, Artificial Neural Networks, Random Forests, XGBoost, CatBoost and LightGBM were trained. The dataset was built with numerous OpenFOAM simulations, validated by experimental data from previous research. The average prediction of ML models has R2 0.94±0.05, RMSE 0.42±0.14 and RRSE 0.24 ± 0.09, whereas the best prediction was obtained by Artificial Neural Network with R2 0.98, RMSE 0.28 and RRSE 0.16. To understand the influence of input parameters on the geometrical characteristics of inclined buoyant jets, the SHAP feature interpretation method was used.
Asunto(s)
Aprendizaje Automático , Redes Neurales de la Computación , Bosques Aleatorios , Aguas ResidualesRESUMEN
This paper presents an in-depth analysis of seawater quality measurements during the bathing seasons from year 2009 to 2020 in the city of Rijeka, Croatia. Due to rare occurrences of measurements with less than excellent water quality, considered dataset is deeply imbalanced. Additionally, it incorporates measurements under the influence of submerged groundwater discharges (SGD), which were observed in some bathing locations. These discharges were previously thought to dry up during the summer season and are now suspected to be one of the causes of increased Escherichia coli values. Consequently, and in view of the fact that the accuracy of prediction models can be significantly influenced by temporal and spatial variation of the input data, a novel cascade prediction modeling strategy was proposed. It consists of a sequence of prediction models which tend to identify general environmental conditions which confidently lead to excellent bathing water quality. The proposed model uses environmental features which can rather easily be estimated or obtained from the weather forecast. The model was trained on a highly biased dataset, consisting of data from locations with and without SGD influence, and for the time period spanning extremely dry and warm seasons, extremely wet seasons, as well as normal seasons. To simulate realistic application, the model was tested using temporal and spatial stratification of data. The cascade strategy was shown to be a good approach for reliably detecting environmental parameters which produce excellent water quality. Proposed model is designed as a filter method, where instances classified as less-than-excellent water quality require further analysis. The cascade model provides great flexibility as it can be customized to the particular needs of the investigated area and dataset specifics.
Asunto(s)
Agua Subterránea , Microbiología del Agua , Monitoreo del Ambiente/métodos , Calidad del Agua , Agua de Mar/microbiología , Estaciones del Año , Escherichia coliRESUMEN
A comparative study of the two northeastern ports of the Adriatic Sea indicated that the port of Rijeka is microbiologically more loaded than the port of Pula and posing a greater threat to other ports through a potential transfer of pathogens by ballast water. Fecal indicator bacteria, Escherichia coli and intestinal enterococci, were investigated seasonally in 2014-2015 in the ports and during the bathing season monitoring in the two bays where ports are located in 2009-2020. In addition, the indicators and pathogens related to human health were determined in the ports' seawater and sediment. The determined factors contributing to microbiological pollution were higher number of tourists and locals, potential wastewater and ballast water discharge and enclosed port configuration, with high solar radiation and low precipitation reducing the negative effects. Our research points to the necessity of including Clostridium perfringens in monitoring beach sand during the bathing seasons and a wider list of pathogens in port monitoring due to a potential transfer by shipping ballast water.