# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 5 Conclusions  
The application of machine learning algorithms for analyzing and predicting the water quality of urban lakes has been demonstrated as a valuable approach in this study. These methods provide a means to identify key water quality indicators and to forecast future changes based on trends and patterns in existing data.  
Our research highlighted the superior performance of the Random Forest (RF) model in predicting water-quality indices. Its robustness, coupled with its ability to model complex, non-linear relationships between various factors, makes it a particularly promising tool for managing urban water bodies. The use of the RF model provides a tangible way for urban and environmental planners to simulate and test various scenarios, thereby informing effective strategies to mitigate the adverse impacts of urbanization on water bodies.  
Moreover, our study illuminated the significance of Biological Oxygen Demand (BOD) and Turbidity as key contributors to water-quality changes. The insights derived from the models regarding these factors provide a crucial understanding of the sources of pollution and can guide targeted interventions to address these specific issues.  
However, as with any study, there is scope for expansion and enhancement. Future research could integrate more diverse and complex data such as rainfall patterns, temperature variations, changes in land use and population growth, among others. These additional factors would enrich the models' inputs, thereby potentially improving their predictive capabilities and providing an even more nuanced understanding of the dynamics affecting urban lake water quality.  
The inclusion of more advanced machine learning techniques, such as deep learning and reinforcement learning, could also be beneficial. Deep learning, with its capacity to learn high-level features from raw data, could offer a way to capture even more complex relationships and patterns in the data. Meanwhile, reinforcement learning could provide a means to not only predict trends, but also to recommend optimal actions for water-quality improvement based on learned environmental responses.  
Our research underscores the significant potential of machine learning as a tool for understanding and predicting urban lake water quality. It also points to exciting avenues for future research that could further enhance our capacity to protect and manage these vital urban ecosystems in the face of rapid urbanization and environmental change.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132011217/s1](https://www.mdpi.com/article/10.3390/app132011217/s1)]([https://www.mdpi.com/article/10.3390/app132011217/s1](https://www.mdpi.com/article/10.3390/app132011217/s1)), Supplementary S1: WQI, Table S1 WQI, weights, Supplementary S2: Computational Setup and Code.  
Conceptualization, T.M.; methodology, T.M. and D.C.-L.; software, T.M. and D.C.-L.; validation, T.M., D.C.-L. and A.K.; formal analysis, T.M.; investigation, T.M. and K.A.; resources, T.M.; data curation, T.M.; writing--original draft preparation, T.M. and I.D.; writing--review and editing, T.M., I.D., I.S. and T.T.; visualization, T.M., I.S. and T.T.; supervision, T.M.; project administration, T.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data and code available at [[https://github.com/TyMill/stoten_1_sloneczne](https://github.com/TyMill/stoten_1_sloneczne)]([https://github.com/TyMill/stoten_1_sloneczne](https://github.com/TyMill/stoten_1_sloneczne)) (accessed on 1 July 2023).  
The authors declare no conflict of interest.