Enhanced Water Quality Inversion in the Ningxia Yellow River Basin Using a Hybrid PCWA-ResCNN Model: Insights from Landsat-8 Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area
2.2. Data and Processing
2.2.1. Water Quality Monitoring Data
2.2.2. Remote Sensing Image Data
2.2.3. Remote Sensing Image Preprocessing
2.3. Methods
2.3.1. PCWA-ResCNN Model Construction
2.3.2. CNN Model Construction
2.3.3. LSTM Model Construction
2.4. Correlation Analysis
2.5. Statistical Evaluation of Indicators
3. Results
3.1. Prediction Results and Analysis of Water Quality Parameters
3.2. Spatiotemporal Analysis of Water Quality
4. Discussion
4.1. Advantages
4.2. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sampling Point No. | Monitoring Section | Longitude | Latitude |
---|---|---|---|
1 | Mahuang Ditch | 106°44′53″ | 39°22′35″ |
2 | Sand Lake | 106°20′52″ | 38°49′20″ |
3 | Pingluo Yellow River Bridge | 106°39′58″ | 38°48′46″ |
4 | Yingu Highway Bridge | 106°24′40″ | 38°21′33″ |
5 | Yazidang Reservoir | 106°32′12″ | 38°09′07″ |
6 | Yesheng Highway Bridge | 106°12′58″ | 38°08′14″ |
7 | Jinsha Bay | 105°55′34″ | 37°49′51″ |
8 | Xiangshan Lake | 105°12′01″ | 37°29′25″ |
9 | Zhongwei Xiaheyan | 105°07′27″ | 37°29′11″ |
Sensor | Wave Band | Wavelength Range/μm | Spatial Resolution/m |
---|---|---|---|
OLI | B1-Coastal | 0.43–0.45 | 30 |
OLI | B2-Blue | 0.45–0.51 | 30 |
OLI | B3-Green | 0.53–0.59 | 30 |
OLI | B4-Red | 0.64–0.67 | 30 |
OLI | B5-NIR | 0.85–0.88 | 30 |
OLI | B6-SWIR1 | 1.57–1.65 | 30 |
OLI | B7-SWIR2 | 2.11–2.29 | 30 |
OLI | B8-PAN | 0.50–0.68 | 15 |
OLI | B9-Cirrus | 1.36–1.38 | 30 |
TIRS | B10-TIRS1 | 10.60–11.19 | 100 |
TIRS | B11-TIRS2 | 11.50–12.51 | 100 |
Water Quality Parameter | Model | ||||
---|---|---|---|---|---|
TUB (NTU) | PCWA-ResCNN | 0.9792 | 80.4109 NTU | 2.5953 | 7.0347 |
CNN | 0.9376 | 139.2386 NTU | 3.1927 | 4.0626 | |
LSTM | 0.8499 | 215.8538 NTU | 3.4165 | 2.6206 | |
CODMn (mg/L) | PCWA-ResCNN | 0.9529 | 0.3005 mg/L | 0.0876 | 4.6780 |
CNN | 0.8729 | 0.4938 mg/L | 0.1549 | 2.8474 | |
LSTM | 0.7790 | 0.6512 mg/L | 0.1941 | 2.1591 | |
NH3-N (mg/L) | PCWA-ResCNN | 0.9501 | 0.0090 mg/L | 0.2291 | 4.5441 |
CNN | 0.8643 | 0.0149 mg/L | 0.3120 | 2.7550 | |
LSTM | 0.6842 | 0.0227 mg/L | 0.5212 | 1.8063 | |
DO (mg/L) | PCWA-ResCNN | 0.9612 | 0.1997 mg/L | 0.0188 | 5.1517 |
CNN | 0.8903 | 0.3356 mg/L | 0.0369 | 3.0651 | |
LSTM | 0.3387 | 0.8241 mg/L | 0.0897 | 1.2482 |
Evaluation Factor | Evaluation Level | ||||
---|---|---|---|---|---|
I | II | III | IV | V | |
CODMn (mg/L)≤ | 2 | 4 | 6 | 10 | 15 |
NH3-N (mg/L)≤ | 0.15 | 0.5 | 1 | 1.5 | 2 |
DO (mg/L)≥ | 7.5 | 6 | 5 | 3 | 2 |
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Li, Q.; Guo, Z.; Li, J.; Li, X.; Ban, B. Enhanced Water Quality Inversion in the Ningxia Yellow River Basin Using a Hybrid PCWA-ResCNN Model: Insights from Landsat-8 Data. Appl. Sci. 2024, 14, 8264. https://doi.org/10.3390/app14188264
Li Q, Guo Z, Li J, Li X, Ban B. Enhanced Water Quality Inversion in the Ningxia Yellow River Basin Using a Hybrid PCWA-ResCNN Model: Insights from Landsat-8 Data. Applied Sciences. 2024; 14(18):8264. https://doi.org/10.3390/app14188264
Chicago/Turabian StyleLi, Qi, Zhonghua Guo, Jialong Li, Xiaojun Li, and Bo Ban. 2024. "Enhanced Water Quality Inversion in the Ningxia Yellow River Basin Using a Hybrid PCWA-ResCNN Model: Insights from Landsat-8 Data" Applied Sciences 14, no. 18: 8264. https://doi.org/10.3390/app14188264
APA StyleLi, Q., Guo, Z., Li, J., Li, X., & Ban, B. (2024). Enhanced Water Quality Inversion in the Ningxia Yellow River Basin Using a Hybrid PCWA-ResCNN Model: Insights from Landsat-8 Data. Applied Sciences, 14(18), 8264. https://doi.org/10.3390/app14188264