# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Applications and Implications
#### 3.3.4 Basis for Public Awareness Campaigns  
The results can also serve as a basis for public awareness campaigns about urban water pollution. Educating residents on the impact of their waste disposal habits on water quality could drive community-led initiatives to protect local water bodies.  
The high-performing predictive ML models developed in this study provide an innovative tool for combating water pollution in urban lakes. By highlighting key pollution contributors and their dynamic interactions, these models offer a scientific basis for targeted and effective strategies aimed at improving urban water quality [65; 66; 67].  
The use of ML algorithms for environmental data interpretation, particularly in the realm of water-quality prediction, presents a significant step towards harnessing the power of big data to understand and mitigate environmental challenges. This study aimed to employ ML algorithms to predict water quality in an urban lake over a 20-year span, using parameters such as Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), pH, Turbidity, Nitrate, and Phosphate, collected from communal sewage and storm drainage systems [68; 69; 70; 71].  
Our study implemented three ML models: Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Random Forest (RF), each offering unique strengths. MLR, while a simpler model, provides clear interpretability and can be useful in cases where relationships between variables are linear. ANN, being a representation of the human brain, is capable of capturing complex patterns and nonlinearities in the data.  
However, its 'black-box' nature makes interpretation less straightforward. The RF model, an ensemble method, combines predictions from a multitude of decision trees to produce a final prediction. The ability of RF to handle both linearity and non-linearity in data and its excellent performance in terms of both bias and variance makes it a robust choice for this study [17, 41, 72].  
Our results demonstrated that RF outperformed both MLR and ANN in terms of prediction accuracy, reinforcing its potential in water-quality prediction. It was successful in capturing the complex interactions among the variables affecting water quality, and it identified BOD and Turbidity as the most influential factors, which can be crucial inputs for targeted mitigation efforts.  
Further, the RF model's potential was demonstrated in its successful prediction of future water-quality indices, indicating its utility as a predictive tool for proactive water management. This tool will be invaluable for urban and environmental planners, aiding in the development of effective strategies for water-pollution control and remediation.  
This study also reveals that ML-based water-quality prediction can be affected by several factors, including data quality and quantity, model selection, and model tuning. As with any predictive model, the accuracy and reliability of the results depend on the quality of the input data. In our study, the robustness of the model can be attributed to the extensive dataset collected over 20 years.  
Despite the successful application of ML models in this study, there are potential areas for further research. Incorporating additional data, such as rainfall patterns, temperature trends, population growth, and land-use changes, can enrich the predictive power of the model. In addition, exploring more advanced ML techniques such as deep learning and reinforcement learning could offer greater predictive capabilities and insight into water quality dynamics.  
This research has demonstrated the viability and effectiveness of using ML algorithms for predicting urban lake water quality. The insights gained from this research can greatly inform and enhance urban water management practices, leading to improved water quality, ecosystem health, and overall urban sustainability.