Table 1 Presents a thorough examination of the current body of research. Table 1: comprehensive analysis.

Omeka, M. E. et al¹⁴.

GIS-based machine learning

It provides accurate prediction of irrigation water quality based on real-time data

The accuracy of the prediction is dependent on the quality and quantity of the input data.

Mellal, N. E. H., et al¹⁵.

Artificial Neural Network

It allows for accurate and reliable prediction of purified water quality

It may not take into account complex and changing environmental factors

Zhang, Y., et al¹⁶.

Hybrid feedback Machine factorization machine model

This model can accurately predict water quality parameters from hyperspectral images, enabling efficient data collection and analysis.

It may have limited applicability to areas with different environmental conditions and may require additional calibration for accurate results.

Bakhtiarizadeh, A., et al¹⁷.

Machine Learning Model

Improved accuracy in predicting groundwater quality, aiding in resource management and protection.

It requires extensive data collection and processing, may be less applicable in areas with limited data availability.

Suman, S. K., et al¹⁸.

Machine Learning Model

Fast and accurate predictions can be made using advanced Machine learning methods.

Availability and quality of residuals may affect the accuracy and applicability of the predictions.

Yang, S., et al¹⁹.

Machine Learning Model

This model allows for a more transparent and easily understandable approach to estimating water quality.

The model’s performance and accuracy may still be limited by the quality of input data and the assumptions made in creating the index.

Sundar, L. S., et al²⁰.

Machine Learning Algorithm

This model reduces carbon footprint in waste water treatment.

Dependence on accurate data input and complex computing may limit its practicality in all settings.

Cechinel, M. A. P., et al²¹.

Machine Learning Prediction

This model can accurately predict effluent quality, optimizing treatment processes for better environmental and cost outcomes.

This model may require complex data analysis and significant upfront investment in technology and training.

Xu, T., et al²².

Adaptive synthetic sampling algorithm

This model increases accuracy of predicting water quality, allowing for more informed decisions on recreational activities.

The model is dependent on accurate and up-to-date data, which may be difficult to obtain and maintain.

Zhong, H., et al²³.

Machine Learning Prediction

This model provides accurate predictions for MBR water quality.

Limited to the specific dataset used and may not be applicable to other MBR systems.

Sangwan, V., et al²⁴.

Machine Learning Framework

This model provides accurate and real-time predictions

This model may require large amounts of data and complex algorithms to achieve high accuracy.

Cai, D., et al²⁵.

Transformer-based Machine learning

This model provides accurate and timely prediction of water quality for effective management of water resources.

This model relies on accurate and sufficient historical data for training, may not be applicable for areas with sparse data

Liu, C., et al²⁶.

Machine Learning Framework

This model provides real-time monitoring and analysis of fish behaviour.

This model may not accurately detect and track fish in turbid or cluttered water conditions.

Ramesh, E., et al²⁷.

Machine-learning based optimization

Automated optimization process can efficiently explore wide range of design parameters.

This model dependent on availability and accuracy of training data for the machine learning algorithm.