Water quality and ecological role of urban lake: a case study of Słoneczne Lake in Szczecin (NW-Poland)

Miller, Tymoteusz; Drozdowska, Aleksandra; Zawal, Andrzej; Poleszczuk, G.

doi:10.18276/ab.2017.24-07

Cited by 1 publication

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Słoneczne Lake, an artificial, flowing water reservoir, is located in the western part of Szczecin city within the Gumie ńce district. Created in the 1930s on marshy riverbeds alongside the Bukowa River, the lake forms part of the wider Bukowa River system, which extends through the urban expanses of Szczecin before merging with the Western Odra River [21,22].…”

Section: Study Areamentioning

confidence: 99%

“…The remaining area includes fields and recreational plots (20%), green spaces (5%), and industrial zones (5%). Several points of sewage discharge have been identified within this catchment, particularly from the fields, recreational plots, and industrial plants, contributing to the inflow of pollutants to the lake [21,23].…”

Section: Study Areamentioning

confidence: 99%

“…The lake exhibits an average depth of 1.1 m, extends 640 m at its longest point, and measures 90 m at its widest. The coastline stretches about 1320 m [21,24].…”

Section: Study Areamentioning

confidence: 99%

“…This unique composition of Słoneczne Lake and its catchment, combined with its significant residential influence and multiple points of sewage discharge, make it an insightful subject for studying water-quality dynamics in an urban [21,23].…”

Section: Study Areamentioning

confidence: 99%

See 3 more Smart Citations

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

Miller,

Durlik,

Adrianna

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Water-quality monitoring in urban lakes is of paramount importance due to the direct implications for ecosystem health and human well-being. This study presents a novel approach to predicting the Water Quality Index (WQI) in an urban lake over a span of two decades. Leveraging the power of Machine Learning (ML) algorithms, we developed models that not only predict, but also provide insights into, the intricate relationships between various water-quality parameters. Our findings indicate a significant potential in using ML techniques, especially when dealing with complex environmental datasets. The ML methods employed in this study are grounded in both statistical and computational principles, ensuring robustness and reliability in their predictions. The significance of our research lies in its ability to provide timely and accurate forecasts, aiding in proactive water-management strategies. Furthermore, we delve into the potential explanations behind the success of our ML models, emphasizing their capability to capture non-linear relationships and intricate patterns in the data, which traditional models might overlook.

show abstract

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

“…The lake exhibits an average depth of 1.1 m, extends 640 m at its longest point, and measures 90 m at its widest. The coastline stretches about 1320 m [21,24].…”

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

See 2 more Smart Citations