## 6 Future Research Directions
### AI Models  
It can be seen from Table 2 that different types of articles proposed in the literature attempt to implement artificial intelligence in the design and process of water quality monitoring. However, these attempts are limited to a total number of proposed models. Different machine learning algorithms were adopted to predict the quality of water with no clear or valid procedure to select the most suitable one. Since the evaluation of missing data, specifically looking at its impact on the accuracy of data labeling and classification, has not yet been conducted, the treatment technique for missing data is not clear within the literature. There is only one article considered the missing data effect on classification accuracy, others either removed the missing points or did not mention the way of treatment in Table 2 which shows that authors could not discuss the issue of unbalanced data, insufficient data, or data bias as they are well-known issues for classification algorithms. Multi-class categorization is a problematic, widely used approach. The issues with multi-class classifications that are most frequently mentioned are class separability, class overlaps, and imbalances between and within classes. Multi-class categorization might become severely complex as a result of nonlinear patterns that are not visible in the dataset [70; 71]. There is also disagreement about how many machine learning algorithms should be used for water quality modeling. Table 2 shows that the majority of the writers either used data collection systems (DAS), or specially created equipment to get their data, proving that there is no freely available, easily usable data. Data labeling is the process of giving each collected piece of information a name that accurately describes its category. A data pre-processing method for identifying the good quality of water is labeling. In the literature, there have been two basic approaches suggested for labeling datasets. Specifically, manual testing. This method involves doing a normal experiment to gather data on normal water quality patterns by employing manual procedures for data labeling by applying thresholds and rules. The second approach is automated data labeling which can be done using unsupervised machine learning. Without human intervention, the data is automatically labeled in automated labeling; however, the labeling process is carried out using rules and equations that have been developed for other case scenarios in a separate field with a different objective. Also possible are biases or errors in the expert labeling. However, there is no agreement on the best method to use most effective method (expert labeling or questionnaire, automated, unsupervised machine learning-based, etc.). The right way to label something is also a topic of disagreement. The labeling method has not yet taken unsupervised learning into account. To efficiently label data, it is advised to study the effectiveness of unsupervised learning algorithms. K nearest neighbors is a popular machine learning strategy for this work, although careful tuning is advised, especially when picking the K number. Although deep learning approaches may need a large number of datasets, they are still recommended. To evaluate whether they can improve recognition models, augmentation pre-processes and other data maximization techniques can be looked at. In addition, more research is needed to determine how imbalanced datasets affect the labeling process and model accuracy. Future research is advised to address missing data handling in the pre-processing stage. It is suggested that an investigation to be conducted on how missing data affects the labeling process and classification accuracy. The optimum machine learning to input the missing data should be chosen using a framework. Finding the sample size is another aspect of future directions. It is necessary to go into further detail on the sample sizes needed for real-time experimental experiments.