A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake
Chlorophyll-a (Chl-a) is an important parameter of water bodies, but due to the complexity of optics in water bodies, it is currently difficult to accurately predict Chl-a concentration in water bodies by traditional methods. In this paper, Sentinel-2 remote sensing images is used as the data source...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10234569/ |
| _version_ | 1797692890335412224 |
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| author | Chenhao Wu Xueliang Fu Honghui Li Hua Hu Xue Li Liqian Zhang |
| author_facet | Chenhao Wu Xueliang Fu Honghui Li Hua Hu Xue Li Liqian Zhang |
| author_sort | Chenhao Wu |
| collection | DOAJ |
| description | Chlorophyll-a (Chl-a) is an important parameter of water bodies, but due to the complexity of optics in water bodies, it is currently difficult to accurately predict Chl-a concentration in water bodies by traditional methods. In this paper, Sentinel-2 remote sensing images is used as the data source combined with measured data, and Ulansuhai Lake is taken as the study area. An adaptive ant colony exhaustive optimization (A-ACEO) algorithm is proposed for feature selection and combined with a novel intelligent algorithm of optimizing support vector regression (SVR) by genetic algorithm (GA) for prediction of Chl-a concentration. The ant colony optimization (ACO) algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GA-SVR model is built by optimizing SVR using GA with the selected feature bands as input, and comparing with the traditional SVR model. The simulation results show that under the same conditions, using A-ACEO algorithm to select feature bands as inputs can effectively reduce the model complexity, and improve the model prediction performance, which provides a valuable reference for monitoring Chl-a concentration in lakes. |
| first_indexed | 2024-03-12T02:35:07Z |
| format | Article |
| id | doaj.art-b4dbb96b319045de8f3f397e37126748 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T02:35:07Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b4dbb96b319045de8f3f397e371267482023-09-04T23:01:57ZengIEEEIEEE Access2169-35362023-01-0111931809319210.1109/ACCESS.2023.331025010234569A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai LakeChenhao Wu0https://orcid.org/0009-0001-2947-8986Xueliang Fu1https://orcid.org/0009-0000-1882-409XHonghui Li2Hua Hu3Xue Li4https://orcid.org/0009-0007-2955-2510Liqian Zhang5College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaChlorophyll-a (Chl-a) is an important parameter of water bodies, but due to the complexity of optics in water bodies, it is currently difficult to accurately predict Chl-a concentration in water bodies by traditional methods. In this paper, Sentinel-2 remote sensing images is used as the data source combined with measured data, and Ulansuhai Lake is taken as the study area. An adaptive ant colony exhaustive optimization (A-ACEO) algorithm is proposed for feature selection and combined with a novel intelligent algorithm of optimizing support vector regression (SVR) by genetic algorithm (GA) for prediction of Chl-a concentration. The ant colony optimization (ACO) algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GA-SVR model is built by optimizing SVR using GA with the selected feature bands as input, and comparing with the traditional SVR model. The simulation results show that under the same conditions, using A-ACEO algorithm to select feature bands as inputs can effectively reduce the model complexity, and improve the model prediction performance, which provides a valuable reference for monitoring Chl-a concentration in lakes.https://ieeexplore.ieee.org/document/10234569/Chlorophyll-a (Chl-a)genetic algorithmlakemachine learning algorithmremote sensingsupport vector regression |
| spellingShingle | Chenhao Wu Xueliang Fu Honghui Li Hua Hu Xue Li Liqian Zhang A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake IEEE Access Chlorophyll-a (Chl-a) genetic algorithm lake machine learning algorithm remote sensing support vector regression |
| title | A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake |
| title_full | A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake |
| title_fullStr | A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake |
| title_full_unstemmed | A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake |
| title_short | A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake |
| title_sort | method based on improved ant colony algorithm feature selection combined with ga svr model for predicting chlorophyll a concentration in ulansuhai lake |
| topic | Chlorophyll-a (Chl-a) genetic algorithm lake machine learning algorithm remote sensing support vector regression |
| url | https://ieeexplore.ieee.org/document/10234569/ |
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