Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation
Maintaining high water quality is essential not only for human survival but also for social and ecological safety. In recent years, due to the influence of human activities and natural factors, water quality has significantly deteriorated, and effective water quality monitoring is urgently needed. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/12/3/61 |
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| author | Zhanqiang Yu Hangnan Yu Lan Li Jiangtao Yu Jie Yu Xinyue Gao |
| author_facet | Zhanqiang Yu Hangnan Yu Lan Li Jiangtao Yu Jie Yu Xinyue Gao |
| author_sort | Zhanqiang Yu |
| collection | DOAJ |
| description | Maintaining high water quality is essential not only for human survival but also for social and ecological safety. In recent years, due to the influence of human activities and natural factors, water quality has significantly deteriorated, and effective water quality monitoring is urgently needed. Traditional water quality monitoring requires substantial financial investment, whereas the remote sensing and random forest model not only reduces operational costs but also achieves a paradigm shift from discrete sampling points to spatially continuous surveillance. The random forest model was adopted to establish a remote sensing inversion model of three water quality parameters (conductivity, total nitrogen (TN), and total phosphorus (TP)) during the growing period (May to September) from 2020 to 2022 in the Songhua River Basin (SRB), using Landsat 8 imagery and China’s national water quality monitoring section data. Model verification shows that the R<sup>2</sup> of conductivity is 0.67, followed by that of TN at 0.52 and TP at 0.47. The results revealed that the downstream conductivity of SRB (212.72 μS/cm) was significantly higher than that upstream (161.62 μS/cm), with TN and TP concentrations exhibiting a similar increasing pattern. This study is significant for improving ecological conservation and human health in the SRB. |
| format | Article |
| id | doaj-art-75f5e0f707bb45dbada3ba85e5c4239e |
| institution | OA Journals |
| issn | 2306-5338 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj-art-75f5e0f707bb45dbada3ba85e5c4239e2025-08-20T01:49:04ZengMDPI AGHydrology2306-53382025-03-011236110.3390/hydrology12030061Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and GeoinformationZhanqiang Yu0Hangnan Yu1Lan Li2Jiangtao Yu3Jie Yu4Xinyue Gao5College of Geography and Ocean Sciences, Yanbian University, Yanji 133000, ChinaCollege of Geography and Ocean Sciences, Yanbian University, Yanji 133000, ChinaCollege of Geography and Ocean Sciences, Yanbian University, Yanji 133000, ChinaCollege of Geographical Science, Harbin Normal University, Harbin 150025, ChinaCollege of Geography and Ocean Sciences, Yanbian University, Yanji 133000, ChinaCollege of Geography and Ocean Sciences, Yanbian University, Yanji 133000, ChinaMaintaining high water quality is essential not only for human survival but also for social and ecological safety. In recent years, due to the influence of human activities and natural factors, water quality has significantly deteriorated, and effective water quality monitoring is urgently needed. Traditional water quality monitoring requires substantial financial investment, whereas the remote sensing and random forest model not only reduces operational costs but also achieves a paradigm shift from discrete sampling points to spatially continuous surveillance. The random forest model was adopted to establish a remote sensing inversion model of three water quality parameters (conductivity, total nitrogen (TN), and total phosphorus (TP)) during the growing period (May to September) from 2020 to 2022 in the Songhua River Basin (SRB), using Landsat 8 imagery and China’s national water quality monitoring section data. Model verification shows that the R<sup>2</sup> of conductivity is 0.67, followed by that of TN at 0.52 and TP at 0.47. The results revealed that the downstream conductivity of SRB (212.72 μS/cm) was significantly higher than that upstream (161.62 μS/cm), with TN and TP concentrations exhibiting a similar increasing pattern. This study is significant for improving ecological conservation and human health in the SRB.https://www.mdpi.com/2306-5338/12/3/61water qualitySonghua River Basinrandom forestremote sensing inversion |
| spellingShingle | Zhanqiang Yu Hangnan Yu Lan Li Jiangtao Yu Jie Yu Xinyue Gao Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation Hydrology water quality Songhua River Basin random forest remote sensing inversion |
| title | Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation |
| title_full | Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation |
| title_fullStr | Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation |
| title_full_unstemmed | Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation |
| title_short | Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation |
| title_sort | inferring water quality in the songhua river basin using random forest regression based on satellite imagery and geoinformation |
| topic | water quality Songhua River Basin random forest remote sensing inversion |
| url | https://www.mdpi.com/2306-5338/12/3/61 |
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