Forecasting Dissolved Oxygen in Lakes Using Different AI Models
DOI:
https://doi.org/10.29304/jqcsm.2025.17.11959Keywords:
Heart disease, features selection, ; mutual information, Random ForestAbstract
The freshwater ecological situation and water quality management would be impossible without the accurate prediction of the dissolved oxygen (DO) concentration in lakes. This study focuses on the use of different advanced Artificial Intelligence (AI) models in predicting values of the DO level for a given input environmental data. To make the data usable for analysis, a number of pre-processing steps are carried out. These processes include, but are not limited to, the ability to deal with the missing data and standardization of the features in the data set, so that the features are on the same level. After the pre-processing, the dataset aggregated in features and a target variable, which is the concentration of the target substance, the dissolved oxygen, is selected. Also, as a part of the building process of the model, the analysis of the features with regard to the target variable is performed in order to discern contributing features for better prediction. Additionally, in order to increase the accuracy of the machine learning models, standardization transformations to the mean of zero and to the variance of one were applied in the training set and test set using Standard Scaler. A selection of different machine learning models was therefore performed in order to identify the best predictor of the DO concentration. In the case of Linear Regression, it’s R² has indicated a high degree of predictive accuracy at 0.9974 with a very low Mean Squared Error (MSE) of 9.6146e-05. Meagerly performing as an alternate method, Support Vector Regression (SVR) managed to attain 0.9448 of R² and an MSE of 0.0021, so it works but not as accurately as Linear Regression. In addition, multiple hidden layers were applied through Artificial Neural Networks (ANN) in a bid to model the data as uncertainty that appears in higher orders. Most notably, the ANN model achieved an R² of 0.995 and an MSE of 0.000161 which is nearly comparable to that of Linear Regression.
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Copyright (c) 2025 Zahraa Ch. Oleiwi, Karrar Khudhair Obayes , Nagham Kamil Hadi, Rahmah.Q yaseen, Asaad Jabbar Sahib
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