The combination of dimensionality reduction methods and machine learning algorithms in the optimization of Maroon River water quality prediction

Water resources face challenges such as climate change and human activities. Sustainable water management is extremely important to solve this problem. More and more people are using artificial intelligence, especially machine learning, to predict and manage water quality. These AI methods are excellent at identifying patterns in water data and improving water quality management. This study examines the water quality of the Maroon River using a combination of factor analysis and machine learning. Data on various water quality parameters were collected from three stations over a period of ten years and the water quality index was calculated. Then, different machine learning algorithms were used to predict the water quality index. In a further step, factor analysis was performed to extract the important features of the input for the optimal algorithm. The performance of the studied algorithms was determined at each step using evaluation criteria. The results showed that in the first step, the Random Forest algorithm (R2 (0.78), RMSE (2.65)) had the best performance in predicting water quality index. It was also found that among the three algorithms studied, nitrate is the most important input parameter, while acidity is the least important. By reducing the number of inputs to 3 important parameters, the performance of the Random Forest algorithm (R2 (0.74), RMSE (2.86)) almost reached the level of 8 input parameters. Combining insights from factor analysis and feature importance analysis can provide a more comprehensive understanding of the complex relationships among water quality parameters and help develop more effective water management.