Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model
Water quality evaluation usually relies on limited state-controlled monitoring data, making it challenging to fully capture variations across an entire basin over time and space. The fine estimation of water quality in a spatial context presents a promising solution to this issue; however, tradition...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/4/731 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849719831758110720 |
|---|---|
| author | Fuliang Deng Wenhui Liu Mei Sun Yanxue Xu Bo Wang Wei Liu Ying Yuan Lei Cui |
| author_facet | Fuliang Deng Wenhui Liu Mei Sun Yanxue Xu Bo Wang Wei Liu Ying Yuan Lei Cui |
| author_sort | Fuliang Deng |
| collection | DOAJ |
| description | Water quality evaluation usually relies on limited state-controlled monitoring data, making it challenging to fully capture variations across an entire basin over time and space. The fine estimation of water quality in a spatial context presents a promising solution to this issue; however, traditional analyses often ignore spatial non-stationarity between variables. To solve the above-mentioned problems in water quality mapping research, we took the Yangtze River as our study subject and attempted to use a geographically weighted random forest regression (GWRFR) model to couple massive station observation data and auxiliary data to carry out a fine estimation of water quality. Specifically, we first utilized state-controlled sections’ water quality monitoring data as input for the GWRFR model to train and map six water quality indicators at a 30 m spatial resolution. We then assessed various geographical and environmental factors contributing to water quality and identified spatial differences. Our results show accurate predictions for all indicators: ammonia nitrogen (NH<sub>3</sub>-N) had the lowest accuracy (R<sup>2</sup> = 0.61, RMSE = 0.13), and total nitrogen (TN) had the highest (R<sup>2</sup> = 0.74, RMSE = 0.48). The mapping results reveal total nitrogen as the primary pollutant in the Yangtze River basin. Chemical oxygen demand and the permanganate index were mainly influenced by natural factors, while total nitrogen and total phosphorus were impacted by human activities. The spatial distribution of critical influencing factors shows significant clustering. Overall, this study demonstrates the fine spatial distribution of water quality and provides insights into the influencing factors that are crucial for the comprehensive management of water environments. |
| format | Article |
| id | doaj-art-6d0c51750a8c4c559816feab6fdd06df |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6d0c51750a8c4c559816feab6fdd06df2025-08-20T03:12:04ZengMDPI AGRemote Sensing2072-42922025-02-0117473110.3390/rs17040731Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression ModelFuliang Deng0Wenhui Liu1Mei Sun2Yanxue Xu3Bo Wang4Wei Liu5Ying Yuan6Lei Cui7School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaChinese Academy of Environmental Planning, United Center for Eco-Environment in Yangtze River Economic Belt, Beijing 100084, ChinaSichuan Academy of Environmental Policy and Planning, Chengdu 610093, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaNavigation College, Jimei University, Xiamen 361001, ChinaWater quality evaluation usually relies on limited state-controlled monitoring data, making it challenging to fully capture variations across an entire basin over time and space. The fine estimation of water quality in a spatial context presents a promising solution to this issue; however, traditional analyses often ignore spatial non-stationarity between variables. To solve the above-mentioned problems in water quality mapping research, we took the Yangtze River as our study subject and attempted to use a geographically weighted random forest regression (GWRFR) model to couple massive station observation data and auxiliary data to carry out a fine estimation of water quality. Specifically, we first utilized state-controlled sections’ water quality monitoring data as input for the GWRFR model to train and map six water quality indicators at a 30 m spatial resolution. We then assessed various geographical and environmental factors contributing to water quality and identified spatial differences. Our results show accurate predictions for all indicators: ammonia nitrogen (NH<sub>3</sub>-N) had the lowest accuracy (R<sup>2</sup> = 0.61, RMSE = 0.13), and total nitrogen (TN) had the highest (R<sup>2</sup> = 0.74, RMSE = 0.48). The mapping results reveal total nitrogen as the primary pollutant in the Yangtze River basin. Chemical oxygen demand and the permanganate index were mainly influenced by natural factors, while total nitrogen and total phosphorus were impacted by human activities. The spatial distribution of critical influencing factors shows significant clustering. Overall, this study demonstrates the fine spatial distribution of water quality and provides insights into the influencing factors that are crucial for the comprehensive management of water environments.https://www.mdpi.com/2072-4292/17/4/731water quality estimationGWRFRYangtze River basinsection control unit |
| spellingShingle | Fuliang Deng Wenhui Liu Mei Sun Yanxue Xu Bo Wang Wei Liu Ying Yuan Lei Cui Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model Remote Sensing water quality estimation GWRFR Yangtze River basin section control unit |
| title | Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model |
| title_full | Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model |
| title_fullStr | Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model |
| title_full_unstemmed | Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model |
| title_short | Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model |
| title_sort | fine estimation of water quality in the yangtze river basin based on a geographically weighted random forest regression model |
| topic | water quality estimation GWRFR Yangtze River basin section control unit |
| url | https://www.mdpi.com/2072-4292/17/4/731 |
| work_keys_str_mv | AT fuliangdeng fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel AT wenhuiliu fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel AT meisun fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel AT yanxuexu fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel AT bowang fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel AT weiliu fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel AT yingyuan fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel AT leicui fineestimationofwaterqualityintheyangtzeriverbasinbasedonageographicallyweightedrandomforestregressionmodel |