Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study

Miller, Tymoteusz; Durlik, Irmina; Adrianna, Krzemińska; Kisiel, Anna; Cembrowska-Lech, Danuta; Spychalski, Ireneusz; Tuński, Tomasz

doi:10.3390/app132011217

Cited by 8 publications

(3 citation statements)

References 88 publications

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“…This allows us to anticipate changes in environmental quality, evaluate the impact of implemented measures, and identify specific areas where these measures may need further adaptation. The use of AI in environmental forecasting allows for more dynamic and reactive management, improving the timeliness of responses to changes in environmental matrices [47]. For example, in the case of a sudden increase in pollutants, algorithms can identify the source and suggest immediate corrective actions.…”

Section: Discussionmentioning

confidence: 99%

The Contribution of Open Source Software in Identifying Environmental Crimes Caused by Illicit Waste Management in Urban Areas

Massarelli,

Uricchio

2024

Urban Science

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This study focuses on the analysis, implementation and integration of techniques and methods, also based on mathematical algorithms and artificial intelligence (AI), to acquire knowledge of some phenomena that produce pollution with an impact on environmental health, and which start from illicit practices that occur in urban areas. In many urban areas (or agroecosystems), the practice of illegal waste disposing by commercial activities, by abandoning it in the countryside rather than spending economic resources to ensure correct disposal, is widespread. This causes an accumulation of waste in these areas (which can also be protected natural areas), which are then also set on fire to reduce their volume. Obviously, the repercussions of such actions are many. The burning of waste releases contaminants into the environment such as dioxins, polychlorinated biphenyls and furans, and deposits other elements on the soil, such as heavy metals, which, by leaching and percolating, contaminate water resources such as rivers and aquifers. The main objective is the design and implementation of monitoring programs against specific illicit activities that take into account territorial peculiarities. This advanced approach leverages AI and GIS environments to interpret environmental states, providing an understanding of ongoing phenomena. The methodology used is based on the implementation of mathematical and AI algorithms, integrated into a GIS environment to address even large-scale environmental issues, improving the spatial and temporal precision of the analyses and allowing the customization of monitoring programs in urban and peri-urban environments based on territorial characteristics. The results of the application of the methodology show the percentages of the different types of waste found in the agroecosystems of the study area and the degree of concentration, allowing the identification of similar areas with greater criticality. Subsequently, through network and nearest neighbour analysis, it is possible to start targeted checks.

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Section: Discussionmentioning

confidence: 99%

The Contribution of Open Source Software in Identifying Environmental Crimes Caused by Illicit Waste Management in Urban Areas

Massarelli,

Uricchio

2024

Urban Science

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“…Miller's work [20] introduces a novel method for predicting theWater Quality Index (WQI) using machine learning algorithms over a two-decade period in an urban lake. The study not only predicts WQI but also uncovers intricate relationships between various water-quality parameters.…”

Section: Related Workmentioning

confidence: 99%

Water Quality Monitoring on Streaming Data

Kumar,

Singh,

Kumar

et al. 2024

CJACSIT

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The increasing contamination of natural water bodies due to diverse human activities necessitates a comprehensive approach to monitoring water quality, especially considering its widespread use in daily life. This study addresses the escalating contamination of natural water bodies, emphasizing the need for a robust real-time water quality monitoring system. Focused on evaluating Triveni Sangam, Prayagraj, where the Ganga and Yamuna rivers converge, the study recognizes the crucial role of continuous monitoring in safeguarding precious water resources. To achieve this, a sophisticated framework has been proposed, leveraging a Spark server to simulate streaming data. This dynamic approach ensures uninterrupted and real-time assessment of water quality, crucial for the effective management of water resources. The system categorizes training data using the Water Quality Index (WQI) and employs Naive Bayes classification for real-time data, achieving an impressive accuracy of 82.21%. The results underscore the effectiveness of learning from streaming data, emphasizing its utility for monitoring water quality in real-time. This study contributes significantly to ongoing water resource management initiatives but also highlights the pivotal role of machine learning in addressing pressing environmental challenges.

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“…Machine learning (ML) seems to be the perfect method for all ecological data mining problems, as it is able to find hidden patterns, relationships, and correlations in large amounts of data [486][487][488][489]. Machine learning also focuses on the development of algorithms and models that can learn from data and perform predictive analyses [490][491][492][493]. The primary limitations or failure to meet expectations in machine learning methods may result from poor data quality, e.g., missing values, mislabelling, duplicates, and interpretability of results.…”

Section: Machine Learning Paradigmmentioning

confidence: 99%

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Samborska-Goik,

Pogrzeba

2024

Applied Sciences

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The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and facilitating remediation. The objectives of this article are to: (i) to provide a concise overview of the mechanisms that influence the migration of hydrocarbons in groundwater and to improve the understanding of the processes that affect contamination levels, (ii) to compile the most commonly used models to simulate the migration and fate of hydrocarbons in the subsurface; and (iii) to evaluate these solutions in terms of their functionality, limitations, and requirements. The aim of this article is to enable potential users to make an informed decision regarding the modelling approaches (deterministic, stochastic, and hybrid) and to match their expectations with the characteristics of the models. The review of 11 1D screening models, 18 deterministic models, 7 stochastic tools, and machine learning experiments aimed at modelling hydrocarbon migration in the subsurface should provide a solid basis for understanding the capabilities of each method and their potential applications.

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Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study

Cited by 8 publications

References 88 publications

The Contribution of Open Source Software in Identifying Environmental Crimes Caused by Illicit Waste Management in Urban Areas

The Contribution of Open Source Software in Identifying Environmental Crimes Caused by Illicit Waste Management in Urban Areas

Water Quality Monitoring on Streaming Data

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

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