Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Prediction of chlorophyll a and risk assessment of water blooms in Poyang Lake based on a machine learning method

Four different methods were used to identify the important factors influencing chlorophyll-a (Chl-a) content: correlation analysis (CC-NMI), principal component analysis (PCA), decision tree (DT), and random forest recursive feature elimination (RF-RFE). Considering the relationship between Chl-a and its active and passive factors, we established machine learning combination models based on multiple linear regression (MLR), multi-layer perceptron (MLP), and support vector regression (SVR) to predict Chl-a content for Poyang Lake, China. Then, the predictive effects of different combination models were compared and evaluated from multiple perspectives. Considering the actual needs for eutrophication prevention and control, the concept of risk probability was then introduced to assess the risk degree of risk associated with water blooms in Poyang Lake. The results indicated that the mean R2 for the Chl-a predictions using the MLR, MLP, and SVR models was 0.21, 0.61, and 0.75, respectively. Consequently, the SVR model demonstrated higher precision and more accurate predictions. Compared to other methods, integrating the SVR model with the RF-RFE method significantly improved the prediction accuracy, with the R2 increasing to 0.94. For Poyang Lake, 8.8% of random samples indicated a low risk level with a water bloom probability of 21.1%-36.5%; one sample indicated a medium risk level with a risk probability of 45.5%. The research results offer valuable insights for predicting eutrophication and conducting risk assessments for Poyang Lake. They also provide reliable scientific support for making decisions about eutrophication in lakes and reservoirs. Therefore, the results hold significant theoretical importance, practical value, and potential for widespread application.

Analysis of marine diversity and anthropogenic pressures on Seixas coral reef ecosystem (northeastern Brazil)

Coral reefs, vital and ecologically significant ecosystems, are among the most jeopardized marine environments in the Atlantic Ocean, particularly along the northeastern coast of Brazil. The persistent lack of effective management and conservation has led to fragmented information on reef use and pressures, hindering the understanding of these ecosystems' health. Major difficulties and challenges include inadequate data, diverse anthropogenic pressures, and the complex interaction between marine species. This study sought to bridge this knowledge gap by conducting a comprehensive analysis of marine diversity and anthropogenic pressures, specifically focusing on Seixas coral reef near João Pessoa city, an area notably impacted by tourism. Utilizing 25 monitoring transects, subdivided into 1 m2 quadrants, the marine diversity was meticulously evaluated through innovative procedures including (a) sedimentological and geochemical field surveys, (b) application of Shannon-Weaver diversity and Simpson dominance indices, (c) cluster analysis, (d) species identification of macroalgae, coral, and fish, and (e) an examination of anthropogenic interactions and pressures on the coral reef. The assessment encompassed three distinct zones: Back Reef, Reef Top, and Fore Reef, and identified a total of 25 species across 15 genera and 10 fish families. The findings revealed the prevalence of brown macroalgae, fish, and coral, with heightened abundance of red macroalgae in the Fore Reef, which also exhibited the greatest diversity (2.816) and dominance (0.894). Original achievements include the identification of specific spatial variations, recognition of the anthropogenic factors leading to ecological changes, and the formulation of evidence-based recommendations. The study concludes that escalating urbanization and burgeoning daily tourist visits to the reef have exacerbated negative impacts on Seixas coral reef's marine ecosystem. These insights underscore the urgent need for strategic planning and resource management to safeguard the reef's biodiversity and ecological integrity.

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.

Quality assessment and prediction of municipal drinking water using water quality index and artificial neural network: A case study of Wuhan, central China, from 2013 to 2019

The sanitary security of drinking water is closely related to human health, but its quality assessment mainly focused on limited types of indicators and relatively restricted time span. The current study was aimed to evaluate the long-term spatial-temporal distribution of municipal drinking water quality and explore the origin of water contamination based on multiple water indicators of 137 finished water samples and 863 tap water samples from Wuhan city, China. Water quality indexes (WQIs) were calculated to integrate the measured indicators. WQIs of the finished water samples ranged from 0.24 to 0.92, with the qualification rate and excellent rate of 100 % and 96.4 %, respectively, while those of the tap water samples ranged from 0.09 to 3.20, with the qualification rate of 99.9 %, and excellent rate of 95.5 %. Artificial neural network model was constructed based on the time series of WQIs from 2013 to 2019 to predict the water quality thereafter. The predicted WQIs of finished and tap water in 2020 and 2021 qualified on the whole, with the excellent rate of 87.5 % and 92.9 %, respectively. Except for three samples exceeding the limits of free chlorine residual, chloroform and fluoride, respectively, the majority of indicators reached the threshold values for drinking. Our study suggested that municipal drinking water quality in Wuhan was generally stable and in line with the national hygiene standards. Moreover, principal component analysis illustrated that the main potential sources of drinking water contamination were inorganic salts and organic matters, followed by pollution from distribution systems, the use of aluminum-containing coagulants and turbidity involved in water treatment, which need more attention.

Evaluating the Performance of Water Quality Indices: Application in Surface Water of Lake Union, Washington State-USA

Water quality indices (WQIs) are practical and versatile instruments for assessing, organizing, and disseminating information about the overall quality status of surface water bodies. The use of these indices may be beneficial in evaluating aquatic system water quality. The CCME (Canadian Council of Ministers of the Environment) and NSF (National Science Foundation) WQIs were used for the assessment of surface water (depth = 1 m) in Lake Union, Washington State. These WQIs were used in surface water at Lake Union, Seattle. The modified versions of the applied WQIs incorporate a varied number of the investigated parameters. The two WQIs were implemented utilizing specialized, publicly accessible software tools. A comparison of their performance is offered, along with a qualitative assessment of their appropriateness for describing the quality of a surface water body. Practical conclusions were generated and addressed based on the applicability and disadvantages of the evaluated indexes. When compared to the CCME-WQI, it is found that the NSF-WQI is a more robust index that yields a categorization stricter than CCME-WQI.

Neural Network-Based Modeling of Water Quality in Jodhpur, India

In this paper, the quality of a source of drinking water is assessed by measuring eight water quality (WQ) parameters using 710 samples collected from a water-stressed region of India, Jodhpur Rajasthan. The entire sample was divided into ten groups representing different geographic locations. Using American Public Health Association (APHA) specified methodology, eight WQ parameters, viz., pH, total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), calcium hardness (Ca-H), residual chlorine, nitrate (as NO3−), and chloride (Cl−), were selected for describing the water quality for potability use. The quality of each parameter is examined as a function of the zone. Taking the average parametric values of different zones, a unique number was used to describe the overall quality of water. It was found that the average value of each parameter varies significantly with zones. Further, we used neural network (NN) modeling to map the nonlinear relationship between the above eight parametric inputs and the water quality index as the output. It can be observed that the NN designed in the present work acquired sufficient learning and can be satisfactorily used to predict the relational pattern between the input and the output. It can further be observed that the water quality index (WQI) from this work is highly efficient for a successful assessment of water quality in the study area. The major challenge to uniquely describing the drinking water quality lies in understanding the cumulative effect of various parameters affecting the quality of water; the quantified figure is subjected to debate, and this paper addresses the difficulty through a novel approach. The framework presented in this work can be automated with appropriate equipment and shall help government agencies understand changing water quality for better management.