Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network
In the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-dri...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/3918524 |
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| _version_ | 1832551951254945792 |
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| author | Zhonghua Xu Changguo Dai Jing Wang Lejun Liu Lei Jiang |
| author_facet | Zhonghua Xu Changguo Dai Jing Wang Lejun Liu Lei Jiang |
| author_sort | Zhonghua Xu |
| collection | DOAJ |
| description | In the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-driven model, can accurately identify nonlinear relationships in the water environment. In this paper, a recognition model for black-odorous water bodies based on ANN was established to directly identify the sensory description of water bodies. This study used water quality data and sensory description (color and odor) as samples to train backpropagation (BP) neural networks. The training results show that the accuracy of the color and odor models reaches 86.7% and 85.8%, respectively. It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. The recognition models have been instrumental in water resource management. Meanwhile, the models provide a reference for the evaluation and early warning of black-odorous water bodies in other regions. |
| format | Article |
| id | doaj-art-600c46c1c9a54f8a9f5e7ff13ba31aa6 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-600c46c1c9a54f8a9f5e7ff13ba31aa62025-02-03T05:59:59ZengWileyAdvances in Civil Engineering1687-80942021-01-01202110.1155/2021/3918524Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural NetworkZhonghua Xu0Changguo Dai1Jing Wang2Lejun Liu3Lei Jiang4Institute of Geology and Mineral Resources Exploration of Shandong ProvinceInstitute of Geology and Mineral Resources Exploration of Shandong ProvinceInstitute of Geology and Mineral Resources Exploration of Shandong ProvinceInstitute of Geology and Mineral Resources Exploration of Shandong ProvinceInstitute of Geology and Mineral Resources Exploration of Shandong ProvinceIn the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-driven model, can accurately identify nonlinear relationships in the water environment. In this paper, a recognition model for black-odorous water bodies based on ANN was established to directly identify the sensory description of water bodies. This study used water quality data and sensory description (color and odor) as samples to train backpropagation (BP) neural networks. The training results show that the accuracy of the color and odor models reaches 86.7% and 85.8%, respectively. It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. The recognition models have been instrumental in water resource management. Meanwhile, the models provide a reference for the evaluation and early warning of black-odorous water bodies in other regions.http://dx.doi.org/10.1155/2021/3918524 |
| spellingShingle | Zhonghua Xu Changguo Dai Jing Wang Lejun Liu Lei Jiang Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network Advances in Civil Engineering |
| title | Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network |
| title_full | Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network |
| title_fullStr | Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network |
| title_full_unstemmed | Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network |
| title_short | Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network |
| title_sort | construction and application of recognition model for black odorous water bodies based on artificial neural network |
| url | http://dx.doi.org/10.1155/2021/3918524 |
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