# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 2 Related Works  
Numerous prior studies have investigated the realm of water quality prediction. In a notable contribution, [PERSON] and colleagues [14] conducted an extensive investigation aimed at ensuring the delivery of safe drinking water and estimating water quality indices through the implementation of machine learning techniques. Their research resulted in the development of a dual-component system: the first component's role is to assess the potability of water, while the second component is focused on predicting water quality index (WQI) values through regression analysis. The initial component employs a range of machine learning algorithms to facilitate water classification. The study strongly emphasizes the essential role of machine learning in improving the precision of water quality prediction models. As a result, it provides valuable insights that can advance the application of machine learning in the field of water quality assessment. The research is of significant importance by highlighting the critical need to ensure safe drinking water for the well-being of communities and the preservation of environmental sustainability. In 2022, [PERSON] et al. [15] conducted a study concerning a crucial aspect of water quality assessment through the presence of macrophytes. Their research was grounded in existing literature pertaining to water quality assessment, highlighting the significance of considering macrophytes as valuable indicators of water quality. They employed a variety of machine learning algorithms to construct a robust water quality assessment model. Their experimental results revealed that the SVM algorithm achieved the highest accuracy rate, an impressive 88%, in evaluating water quality based on the presence of macrophytes. This achievement underscores the substantial potential of machine learning in enhancing the precision and reliability of water quality detection methodologies, particularly in ecosystems where macrophytes play a vital role. In their notable work, [PERSON] et al. [16] conducted a comprehensive analysis of the performance of a water quality index model designed for predicting water conditions, leveraging the power of machine learning techniques. The study, situated within the existing body of research, provides a substantial contribution to the ongoing exploration of water quality assessment and prediction. It underscores the critical importance of robust modeling and machine learning techniques for better understanding and forecasting water condition. The implications of their findings extend to enhancing the precision and reliability of water quality prediction models, thereby facilitating more informed decision-making and effective water resource management. This research augments the growing body of knowledge in the field of environmental science, with a specific focus on advancing water quality assessment through the innovative application of machine-learning-based approaches. Furthermore, [PERSON] [17], assessed the efficacy of employing machine learning (ML) techniques for water quality prediction. In this investigation, a machine learning classifier model was developed to predict water quality utilizing real-world datasets, categorizing the water as potable or not. By employing multiple algorithms, the findings of this research indicate that the support vector machine and k-nearest neighbor models outperformed others, as evidenced by higher F1-score and ROC AUC values. On the other hand, LASSO LARS, and stochastic gradient descent exhibited superior performance when considering recall values. Additionally, [PERSON] et al. [18] investigated the potential of machine learning algorithms in assessing water quality across diverse environmental settings, including surface water, groundwater, and seawater. Their research not only offers a conceptual framework for potential applications in water environments but also suggests the use of specific algorithms, such as Support Vector Regression (SVR), Random Forest (RF), and deep neural networks (DNNs).