Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques
In recent years, there has been a noticeable surge in population, accompanied by the rapid development of industries, services, and agriculture within communities. This growth has intensified pressure on water resources, resulting in a simultaneous decline in both the quantity and quality of these v...
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2024-12-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| author | Peng He Chengjun Ma |
| author_facet | Peng He Chengjun Ma |
| author_sort | Peng He |
| collection | DOAJ |
| description | In recent years, there has been a noticeable surge in population, accompanied by the rapid development of industries, services, and agriculture within communities. This growth has intensified pressure on water resources, resulting in a simultaneous decline in both the quantity and quality of these vital resources. Dissolved organic carbon (DOC) stands out as a pivotal factor in determining the quality of natural freshwater bodies. It serves as a crucial indicator of water pollution, offering insights into the presence of both natural and man-made organic pollutants. This study focuses on predicting the levels of dissolved organic carbon in South Florida utilizing artificial intelligence algorithms. Specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Decision Tree (DT), and Support Vector Regression (SVR) algorithms were employed. The Sparrow Search Algorithm was utilized to fine-tune the hyperparameters of these algorithms. The effectiveness of the hybrid models generated was evaluated through correlation and error parameters, along with the number of iterations required to achieve the lowest error rate. Upon comprehensive review, the SVR-SSA hybrid model emerged as the most promising, exhibiting an R2 value of 0.997293, a Root Mean Square Error (RMSE) of 0.2906, and a Normalized Mean Square Error (NMSE) of 5×10-4 at the thousandth iteration in the train dataset. These values for the test data set are calculated as 0.999754, 0.1625, and 2×10-4, respectively. Hence, it is deemed the most optimum model for this research endeavour. |
| format | Article |
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| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-05f1c345ad9e48929b67ff54830853a42025-02-12T08:48:16ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-12-0100304334810.22034/aeis.2024.482609.1237212429Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence TechniquesPeng He0Chengjun Ma1School of Architecture, Changsha University of Science and Technology, Changsha, Hunan, 410076, ChinaCollege of Malanshan New Media, University of Changsha, Changsha, Hunan, 410022, ChinaIn recent years, there has been a noticeable surge in population, accompanied by the rapid development of industries, services, and agriculture within communities. This growth has intensified pressure on water resources, resulting in a simultaneous decline in both the quantity and quality of these vital resources. Dissolved organic carbon (DOC) stands out as a pivotal factor in determining the quality of natural freshwater bodies. It serves as a crucial indicator of water pollution, offering insights into the presence of both natural and man-made organic pollutants. This study focuses on predicting the levels of dissolved organic carbon in South Florida utilizing artificial intelligence algorithms. Specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Decision Tree (DT), and Support Vector Regression (SVR) algorithms were employed. The Sparrow Search Algorithm was utilized to fine-tune the hyperparameters of these algorithms. The effectiveness of the hybrid models generated was evaluated through correlation and error parameters, along with the number of iterations required to achieve the lowest error rate. Upon comprehensive review, the SVR-SSA hybrid model emerged as the most promising, exhibiting an R2 value of 0.997293, a Root Mean Square Error (RMSE) of 0.2906, and a Normalized Mean Square Error (NMSE) of 5×10-4 at the thousandth iteration in the train dataset. These values for the test data set are calculated as 0.999754, 0.1625, and 2×10-4, respectively. Hence, it is deemed the most optimum model for this research endeavour.https://aeis.bilijipub.com/article_212429_398e521dc4ca2277776ef4f720be9b8b.pdfdissolved organic carbonsupport vector regressionmeta-heuristic algorithmspredictionhybrid models |
| spellingShingle | Peng He Chengjun Ma Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques Advances in Engineering and Intelligence Systems dissolved organic carbon support vector regression meta-heuristic algorithms prediction hybrid models |
| title | Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques |
| title_full | Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques |
| title_fullStr | Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques |
| title_full_unstemmed | Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques |
| title_short | Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques |
| title_sort | forecasting dissolved organic carbon levels utilizing metaheuristic optimization with artificial intelligence techniques |
| topic | dissolved organic carbon support vector regression meta-heuristic algorithms prediction hybrid models |
| url | https://aeis.bilijipub.com/article_212429_398e521dc4ca2277776ef4f720be9b8b.pdf |
| work_keys_str_mv | AT penghe forecastingdissolvedorganiccarbonlevelsutilizingmetaheuristicoptimizationwithartificialintelligencetechniques AT chengjunma forecastingdissolvedorganiccarbonlevelsutilizingmetaheuristicoptimizationwithartificialintelligencetechniques |