# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 4 Advantages and Limitations  
### Advantages  
Our study has several distinguishing features that contribute to its value and relevance in the ongoing research on urban lake water quality prediction.  
1. Use of Long-Term Data: Our study stands out for its use of two decades' worth of water-quality data from Sloneczne Lake. This extensive dataset allows us to capture long-term trends and fluctuations in water-quality indices that would likely be missed in studies relying on shorter observational periods. The ability to analyze temporal patterns, including seasonal variations, contributes to the accuracy and reliability of our predictive model, making our findings more robust [73, 74, 75].
2. Interdisciplinary Approach: Another defining characteristic of our work is the successful integration of domain knowledge in water-quality science with advanced data science skills. This interdisciplinary approach has allowed us to understand the complexities of water-quality dynamics, appropriately select and preprocess relevant features, and intelligently tune model parameters. As a result, our models are not just data-driven, but also firmly rooted in the reality of water-quality science, enhancing their predictive accuracy and interpretability [76, 77, 78].
3. Comprehensive Analysis: We applied and evaluated a range of machine learning techniques including Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Random Forest (RF) models. Additionally, we used a variety of statistical measures such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R\({}^{2}\)) to gauge the models' predictive performance. This comprehensive approach not only enabled us to identify the best-performing model but also provided a rich, multi-faceted understanding of the models' strengths and weaknesses [74, 79, 40].
4. Actionable Insights: Our research goes beyond model development and evaluation. The model's ability to identify key parameters affecting water quality, notably Biological Oxygen Demand (BOD) and Turbidity, delivers actionable insights for water management. These findings can inform policymakers, environmental protection agencies, and urban planners as they devise targeted strategies for water-pollution control and urban lake preservation. By shedding light on the most influential factors in water quality, our work provides a crucial starting point for data-driven, effective interventions [80, 81, 82].
5. Inclusion of Diverse Urban Influences: The catchment area of the Sloneczne Lake comprises residential areas, fields, recreational plots, green areas, and production plants. By including data on water-quality factors relating to these diverse urban influences, we provide a more realistic and comprehensive view of the factors affecting urban lake water quality. This broad-based approach can help guide more holistic urban planning and water management strategies that consider a range of urban activities and their impacts on water bodies [83, 84, 32].  
Our study makes valuable contributions to the body of knowledge in urban water-quality prediction through its long-term data usage, interdisciplinary approach, comprehensive evaluation of multiple machine learning models, and the generation of actionable insights for water-quality management.