Reconstruction and analysis of negatively buoyant jets with interpretable machine learning.

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.

Predictive modeling of microbiological seawater quality in karst region using cascade model.

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.

Impacts of Atmospheric and Anthropogenic Factors on Microbiological Pollution of the Recreational Coastal Beaches Neighboring Shipping Ports.

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.

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection.

This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node's start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network.

In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations.

A Machine Learning-based Algorithm for Water Network Contamination Source Localization.

In this paper, a novel machine learning based algorithm for water supply pollution source identification is presented built specifically for high performance parallel systems. The algorithm utilizes the combination of Artificial Neural Networks for classification of the pollution source with Random Forests for regression analysis to determine significant variables of a contamination event such as start time, end time and contaminant chemical concentration. The algorithm is based on performing Monte Carlo water quality and hydraulic simulations in parallel, recording data with sensors placed within a water supply network and selecting a most probable pollution source based on a tournament style selection between suspect nodes in a network with mentioned machine learning methods. The novel algorithmic framework is tested on a small (92 nodes) and medium sized (865 nodes) water supply sensor network benchmarks with a set contamination event start time, end time and chemical concentration. Out of the 30 runs, the true source node was the finalist of the algorithm's tournament style selection for 30/30 runs for the small network, and 29/30 runs for the medium sized network. For all the 30 runs on the small sensor network, the true contamination event scenario start time, end time and chemical concentration was set as 14:20, 20:20 and 813.7 mg/L, respectively. The root mean square errors for all 30 algorithm runs for the three variables were 48 min, 4.38 min and 18.06 mg/L. For the 29 successful medium sized network runs the start time was 06:50, end time 07:40 and chemical concentration of 837 mg/L and the root mean square errors were 6.06 min, 12.36 min and 299.84 mg/L. The algorithmic framework successfully narrows down the potential sources of contamination leading to a pollution source identification, start and ending time of the event and the contaminant chemical concentration.

Assessment of head loss coefficients for water turbine intake trash-racks by numerical modeling.

In this work, numerical simulations of fluid flow around trash-rack for different bar cross sections are conducted to investigate cross section influence on head losses. Comparison with experimental data is conducted to validate the usage of numerical simulations which enable investigation of great number of trash-rack configurations. In previous experimental studies researchers mostly focused on trash-rack parameters (bar spacing, bar length, inclinations etc.) where bar cross section was mainly rectangular or streamlined shape. Therefore, 2D simulations for different cross sections are carried out for a range of trash-rack configurations in order to provide better insight how it affects energy losses. It is shown that head loss reduction due to change in cross section is greatly dependent on trash-rack configuration, therefore optimization of simplified real water turbine trash-rack is also conducted to produce the cross section that generates smallest head losses for given configuration.

Temporal variations analyses and predictive modeling of microbiological seawater quality.

Bathing water quality is a major public health issue, especially for tourism-oriented regions. Currently used methods within EU allow at least a 2.2 day period for obtaining the analytical results, making outdated the information forwarded to the public. Obtained results and beach assessment are influenced by the temporal and spatial characteristics of sample collection, and numerous environmental parameters, as well as by differences of official water standards. This paper examines the temporal variation of microbiological parameters during the day, as well as the influence of the sampling hour, on decision processes in the management of the beach. Apart from the fecal indicators stipulated by the EU Bathing Water Directive (E. coli and enterococci), additional fecal (C. perfringens) and non-fecal (S. aureus and P. aeriginosa) parameters were analyzed. Moreover, the effects of applying different evaluation criteria (national, EU and U.S. EPA) to beach ranking were studied, and the most common reasons for exceeding water-quality standards were investigated. In order to upgrade routine monitoring, a predictive statistical model was developed. The highest concentrations of fecal indicators were recorded early in the morning (6 AM) due to the lack of solar radiation during the night period. When compared to enterococci, E. coli criteria appears to be more stringent for the detection of fecal pollution. In comparison to EU and U.S. EPA criteria, Croatian national evaluation criteria provide stricter public health standards. Solar radiation and precipitation were the predominant environmental parameters affecting beach water quality, and these parameters were included in the predictive model setup. Predictive models revealed great potential for the monitoring of recreational water bodies, and with further development can become a useful tool for the improvement of public health protection.