Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data
Efficient, comprehensive, continuous, and accurate monitoring of organic pollution in lakes can provide a reliable basis for water quality assessment and water pollution prevention This paper takes Dianchi Lake as the research object, aiming at the four important water quality indexes of permanganat...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2022/3341713 |
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| author | Jiaju Cao Xingping Wen Dayou Luo Yinlong Tan |
| author_facet | Jiaju Cao Xingping Wen Dayou Luo Yinlong Tan |
| author_sort | Jiaju Cao |
| collection | DOAJ |
| description | Efficient, comprehensive, continuous, and accurate monitoring of organic pollution in lakes can provide a reliable basis for water quality assessment and water pollution prevention This paper takes Dianchi Lake as the research object, aiming at the four important water quality indexes of permanganate index (COD), dissolved oxygen (DO), hydrogen ion (pH), and ammonia nitrogen (NH3-N); based on the correlation analysis of Landsat 8 data and measured water quality data, an inversion model is constructed to obtain the spatial distribution of the four indexes. The results show that the relative errors of permanganate index (COD) in neural network and multiple regression are 9.68% and 17.48%, respectively; 3.81% and 3.36% in dissolved oxygen (DO); 1.25% and 1.58% in hydrogen ion (pH); in ammonia nitrogen (NH3-N), it is 15.39% and 24.97%, respectively. The lowest COD in the study area is 6.2 mg/L and the highest is 9.8 mg/L; in 2018, the DO is 5.81 mg/L at the lowest and 9.05 mg/L at the highest; the lowest pH is 5.9 mg/L, the highest is 8.54 mg/L, and the lowest NH3-N is 0.22 mg/L, the highest is 0.41 mg/L. The inversion results of the overall pollutant concentration in the study area are consistent with the actual situation, with only some slight deviations in some areas. The two inversion models can effectively monitor the water quality and spatial distribution of Dianchi Lake. The remote sensing inversion model of water quality has the value of in-depth research and promotion. |
| format | Article |
| id | doaj-art-5bffd6b53b9a465280862050ac76e3c6 |
| institution | Kabale University |
| issn | 2314-4939 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-5bffd6b53b9a465280862050ac76e3c62025-02-03T05:50:36ZengWileyJournal of Spectroscopy2314-49392022-01-01202210.1155/2022/3341713Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 DataJiaju Cao0Xingping Wen1Dayou Luo2Yinlong Tan3Faculty of Land Resource EngineeringFaculty of Land Resource EngineeringFaculty of Land Resource EngineeringFaculty of Land Resource EngineeringEfficient, comprehensive, continuous, and accurate monitoring of organic pollution in lakes can provide a reliable basis for water quality assessment and water pollution prevention This paper takes Dianchi Lake as the research object, aiming at the four important water quality indexes of permanganate index (COD), dissolved oxygen (DO), hydrogen ion (pH), and ammonia nitrogen (NH3-N); based on the correlation analysis of Landsat 8 data and measured water quality data, an inversion model is constructed to obtain the spatial distribution of the four indexes. The results show that the relative errors of permanganate index (COD) in neural network and multiple regression are 9.68% and 17.48%, respectively; 3.81% and 3.36% in dissolved oxygen (DO); 1.25% and 1.58% in hydrogen ion (pH); in ammonia nitrogen (NH3-N), it is 15.39% and 24.97%, respectively. The lowest COD in the study area is 6.2 mg/L and the highest is 9.8 mg/L; in 2018, the DO is 5.81 mg/L at the lowest and 9.05 mg/L at the highest; the lowest pH is 5.9 mg/L, the highest is 8.54 mg/L, and the lowest NH3-N is 0.22 mg/L, the highest is 0.41 mg/L. The inversion results of the overall pollutant concentration in the study area are consistent with the actual situation, with only some slight deviations in some areas. The two inversion models can effectively monitor the water quality and spatial distribution of Dianchi Lake. The remote sensing inversion model of water quality has the value of in-depth research and promotion.http://dx.doi.org/10.1155/2022/3341713 |
| spellingShingle | Jiaju Cao Xingping Wen Dayou Luo Yinlong Tan Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data Journal of Spectroscopy |
| title | Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data |
| title_full | Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data |
| title_fullStr | Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data |
| title_full_unstemmed | Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data |
| title_short | Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data |
| title_sort | study on water quality inversion model of dianchi lake based on landsat 8 data |
| url | http://dx.doi.org/10.1155/2022/3341713 |
| work_keys_str_mv | AT jiajucao studyonwaterqualityinversionmodelofdianchilakebasedonlandsat8data AT xingpingwen studyonwaterqualityinversionmodelofdianchilakebasedonlandsat8data AT dayouluo studyonwaterqualityinversionmodelofdianchilakebasedonlandsat8data AT yinlongtan studyonwaterqualityinversionmodelofdianchilakebasedonlandsat8data |