Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review
Abstract
:1. Introduction
2. Methodology
Water AND Quality AND (Monitoring OR Prediction OR Forecasting OR Assessment) AND (Lake OR River OR Reservoir OR Watershed OR Fresh Water) AND (Remote Sensing OR Landsat OR Sentinel OR MODIS) AND (Machine Learning OR Support Vector Machine OR Random Forest OR Boosting OR Deep Learning) AND (LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2020) OR LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2023) OR LIMIT-TO (PUBYEAR, 2024))
- How effective is the integration of machine learning and remote sensing for monitoring water quality?
- What parameters can be estimated using remote sensing, and are these parameters sufficient for effective water quality monitoring?
- Which remote sensing products are useful for water quality estimation?
- Which machine learning techniques are best suited for remote sensing-based water quality monitoring?
3. Remote Sensing Technologies for Water Quality Monitoring
4. Comprehensive Overview of Water Quality Parameters
4.1. Chlorophyll-a
4.2. Turbidity
4.3. Temperature
4.4. Total Nitrogen and Total Phosphorous
4.5. Colored Dissolved Organic Matter
4.6. Total Suspended Solids
4.7. Dissolved Oxygen
4.8. Hydrogen Power
5. Advanced Machine Learning Approaches in Water Quality Assessment
5.1. Support Vector Machine
5.2. Random Forest
5.3. Boosting Algorithms
5.4. Deep Learning Models
6. Discussion
- Larger spatial and temporal coverage: The traditional methods need to establish hydrology stations across water bodies to collect water samples. However, establishing such stations over a large area is challenging due to cost and geographical constraints [27]. Therefore, ground-based monitoring may sometimes be expensive and limited. Satellites orbit around Earth and cover vast geographical area in a single pass. Satellites can capture images across large swaths of water bodies, allowing to monitor even the inaccessible locations consistently. With increasing resolutions, remote sensing-based monitoring can obtain useful information at the scale of water bodies such as lakes and rivers. Satellites orbit the Earth frequently at regular intervals, leading to short-term and long-term observations in water quality [166,167]. Frequent observations are crucial for analyzing seasonal variations and temporal changes.
- Parameter estimation: Several water-quality parameters that are optically active can be efficiently assessed with the help of remotely sensed imagery. These parameters include Chl-a, turbidity, TSSs, CDOM, etc. [168]. Remote sensing platforms equipped with specialized sensors measure the reflectance and absorption of particular wavelengths by water bodies to provide valuable data that can be processed to determine important water quality parameters [27]. The assessment of optically inactive parameters except temperature with the help of remote sensing techniques is a challenging task. Meanwhile, water temperature can be effectively measured by detecting infrared radiation emitted by water bodies [169]. Other parameters such as pH, nitrogen, phosphorous, and dissolved oxygen are determined indirectly using sophisticated algorithms and modeling [26].
- Automated monitoring: Remote sensing data can be captured without manual intervention. To obtain better accuracy, the captured data undergo preprocessing for atmospheric corrections, geometric corrections, and sensor calibration [170]. Machine learning algorithms process the remote sensing data to assess optically active and optically inactive water quality parameters [24,26,28]. The entire process from data capture to parameter estimation is performed automatically by the sensors and computer program. These automated systems can detect anomalies in water quality anomalies and can generate alerts when needed [171]. It helps to provide timely insights into water quality conditions.
- Cost effectiveness: Machine learning methods significantly rely on the availability of adequate amounts of data. The acquisition of remote sensing data has become highly cost effective in recent years due to space agencies’ data accessibility initiatives [172]. Satellite missions such as Landsat and Sentinel provide low-cost or free data access, alleviating financial burdens. Some investments are required for computing machines to develop and train the machine learning algorithms. However, the overall cost involved is significantly lower than establishing the hydrology stations [173].
- Robust algorithms: The existing algorithms mostly focus on the assessing of multiple water quality parameters. However, different parameters are sensitive to different data features [174]. Therefore, refined algorithms to assess specific water-quality parameters can be developed to enhance the accuracy and precision. In addition, the algorithms are usually developed and evaluated on data for specific locations [124]. Such algorithms may not generalize well under different conditions for other locations, leading to the need for more robust algorithms.
- Data fusion: Data from different remote sensing platforms vary in spectral, spatial, and temporal resolution, data formats, and collection methods. Satellites have different frequencies and visiting times. The complementary information from different sources can be integrated for better efficiency and accuracy [175]. Merging multi-source data can improve spatial and temporal resolution. The integrating of spectral bands with different wavelengths enhances the system’s discriminating ability. However, combining data from different optical sensors with broad coverage remains challenging [175]. Advanced data fusion techniques can be explored to leverage the comprehensive water quality assessment strengths [154].
- Extended use of machine learning: Machine learning has exhibited great potential for processing remotely sensed data for water quality assessment. However, machine learning algorithms are highly affected by the variability of data across different water bodies. Generalizing machine learning models across water bodies is difficult due to varying conditions [176]. It is desirable that models can scale well to larger geographical areas and datasets without significant performance loss. The need for high-end computing resources is another constraint with machine learning models, especially for under-resourced conditions [177]. All these issues need further consideration to develop efficient and affordable advanced methods.
- Atmospheric correction: Sensors mounted on satellites and other aerial platforms record the electromagnetic radiation reflected or emitted by the objects on Earth. The radiation travels through the atmosphere of Earth. Various particles and gases in the atmosphere react with radiation, distorting the radiation or light before reaching the sensor. Scattering and absorption are the major atmospheric effects affecting electromagnetic radiation [178]. Atmospheric corrections [179,180] are applied as preprocessing steps to the remote sensing data to reduce the distortion caused by the atmosphere on the reflected values recorded by the sensor. Atmospheric corrections help to ensure that the data represent the actual characteristics of Earth’s features. Atmospheric conditions vary geographically and temporally, making atmospheric correction highly challenging. Any single correction method is not universally applicable. Developing an algorithm that can adapt to varying conditions is highly desirable.
7. Conclusions
7.1. Synthesis of Key Findings
7.2. Implications for Policy and Practice in Water Quality Assessment
7.3. Reflections on the Future of Water Quality Monitoring Techniques
8. Future Directions
- Enhanced Data Integration: Satellites and other remote sensing platforms capture data at varying resolutions in different formats that complicate the integration of data from multiple sources. On the other hand, different sensors have different radiometric and geometric characteristics. The variability in data quality can affect the accuracy of the assessments methods. The integration methods should be able to quantify uncertainties in resultant data [183]. The integration of large datasets induces a large computational burden also. The complex challenges in data integration should lead to the development of enhanced and robust algorithms to produce reliable, consistent, and comprehensive datasets.
- Generalized Algorithms: The characteristics of water bodies across the globe vary widely due to geographic conditions, environment, human activities, etc. [184]. Most algorithms are evaluated for specific water bodies and may lack broad applicability. Machine learning algorithms, which are highly influenced by the training data, may be inconsistent under varying conditions [185]. Generalized algorithms are required to monitor different types of water bodies across different regions consistently. Generalized algorithms are robust, scalable, and cost effective, and can be applied over a large area without significant adjustments.
- Emerging Contaminants: Much work has been conducted on assessing some well-known water quality parameters including Chl-a, pH, total nitrogen, total phosphorous, and dissolved oxygen, etc. [28,68]. However, due to anthropogenic activities, other chemicals and microorganisms such as pharmaceuticals, industrial waste, nanomaterials, personal care products, etc., enter water bodies. Such pollutants can badly affect the ecological system and human health. Future directions include working on monitoring these emerging contaminants using remote sensing data.
- Impact of Climate Change: Climate changes such as rising temperature and increasing CO2 water influence water chemistry and water quality [186]. The climate change may cause temporal and spatial variations in water quality, making the monitoring of trends challenging. The monitoring methods need to be adaptive to emerging issues and able to generate early warnings on future conditions due to climate changes.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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# | Journal Name | Impact Factor | Cite Score | Citations | Publisher |
---|---|---|---|---|---|
1 | Remote Sensing | 4.2 | 8.3 | 173,611 | MDPI |
2 | Remote Sensing of Environment | 11.1 | 25.1 | 46,552 | Elsevier |
3 | Science of The Total Environment | 8.2 | 17.6 | 540,202 | Elsevier |
4 | Water | 3.0 | 5.8 | 89,517 | MDPI |
5 | ISPRS Journal of Photogrammetry and Remote Sensing | 10.6 | 21.0 | 21,901 | Elsevier |
6 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 4.7 | 9.3 | 27,333 | IEEE |
7 | Remote Sensing Applications: Society and Environment | 3.8 | 8.0 | 6053 | Elsevier |
8 | Sensors | 3.4 | 7.3 | 256,060 | MDPI |
9 | Journal of Environmental Management | 8.0 | 13.7 | 120,010 | Elsevier |
10 | Environmental Science and Pollution Research | - | 8.7 | 199,035 | Springer |
Satellite | Water Quality Parameter | References |
---|---|---|
Landsat | Chlorophyll-a | [17,26,28,29,31,32,34,61,63,64,65,66,67,68,72,77,103,109] |
Turbidity | [17,24,65,75,77,107] | |
Temperature | [17,77,88,89,107] | |
Total Nitrogen and Total Phosphorous | [26,28,29,31,32,34,92] | |
Colored Dissolved Organic Matter | [32,92] | |
Total Suspended Solids | [66,102,103,105,106,107,109,110] | |
Dissolved Oxygen | [17,24,30,89,92] | |
Hydrogen Power (pH) | [17,24,89,107,118] | |
Sentinel | Chlorophyll-a | [39,41,42,43,44,62,64,65,66,72,76,87,103,104,109,113] |
Turbidity | [42,45,65,72,74,76,78,79,81,87] | |
Temperature | [87,113] | |
Total Nitrogen and Total Phosphorous | [36,37,93,94] | |
Colored Dissolved Organic Matter | [36,42,93,99] | |
Total Suspended Solids | [41,66,103,104,109,111] | |
Dissolved Oxygen | [87] | |
Hydrogen Power (pH) | [104] | |
MODIS | Chlorophyll-a | [54,55] |
Turbidity | [54] | |
Temperature | [54] | |
Total Nitrogen and Total Phosphorous | [48,51] | |
Total Suspended Solids | [55,56] | |
Dissolved Oxygen | [57] |
Reference | Method | Water Body/Location | Water Quality Parameters | Performance |
---|---|---|---|---|
[68] | RF, XGB, GB, AB, SVM, ANN | Lake Tana, Ethiopia | Chl-a, Turbidity, TDS | R2 = 0.78–0.80, MARE = 0.072–0.082 |
[126] | SVM | Sefidrud basin, Iran | pH, DO, TDS, Temp, NO3, PO4, BOD, Turbidity, and FCB | R2 = 0.87, RMSE = 0.061 |
[127] | SVM | Langat basin, Malayasia | pH, TSS, DO, AN, COD, and BOD | CC 0.979–0.998 and MSE = 0.004–0.681 |
[128] | SVM, ANN | Tolo Harbour, Hong Kong | Chl-a, pH, BOD, DO, TIN, PO4, Temp | CC: 0.972–0.984, and RMSE = 0.66–0.765 |
[130] | SVM | Chao Phraya River, Thailand | NH3, BOD, DO, FCB, TCB, etc. | Accuracy: 0.94, Precision: 0.84, Recall: 0.84, F1: 0.84 |
[131] | SVR | Trans-Mexican Volcanic Belt, Mexico | Chl-a, TSM, SDD | |
[62] | SVR | Finger Lakes, USA | Chl-a | R2 = 0.76, RMSE = 0.633 μg/L, MAE 0.728 μg/L |
[132] | SVM | Paraopeba River, Brazil | Turbidity | Accuracy: 0.96 |
[135] | SVM | Lake Houston, USA | Turbidity and Specific Conductance | R2 = 0.55–0.90, MAPE = 12.01–25.03 |
[36] | SVM, SR | Zhejiang, China | CDOM, TN, and TP | R = 0.68–0.82 |
[26] | ANN | Gheshlagh reservoir, Iran | TN and TP | R = 0.81–0.93 |
[34] | ANN | Lake Urmia, Thailand | Chl-a, TP, and Secchi depth | NSE = 0.74–0.96 |
[66] | ANN | San Francisco Bay | Chl-a, TSM | R2 = 0.71–0.89 |
[48] | ANN | Balik Lake, Turkey | TN and TP | R = 0.36–0.59 |
[17] | LASSO | Tigris River, Iraq | Chl-a, Temp, EC, TDS, pH, Turbidity, Algae, and DO | R2 = 0.42–0.80 |
[31] | SR | Yangtze River, China | Chl-a, TN, and TP | MAPE = 4.30–25.88% |
[29] | SR | Trichonis Lake, Greece | Chl-a, NH4-N, and TP | R = 0.4–0.7 |
[65] | SR | Oklahoma, USA | Chl-a, Turbidity, Secchi depth | R2 = 0.58–0.85 |
[44] | SR | San Roque, Argentina | Chl-a | R2 = 0.77 |
[31] | SR | Yangtze River, China | Chl-a, TN, and TP | RMSE = 0.475 μg/L–0.110 mg/L, MAPE = 4.3–25.88% |
[51] | SR | Wisconcin, USA | TN, TP, NO3-N | R2 = 0.51–0.80 |
[99] | DT | Lake Khanka, China/Russia | CDOM | R2 = 0.84 |
[42] | C2X-Net | Chao Phraya River, Thailand | Chl-a, CDOM, and Turbidity | R = 0.47–0.84 |
[39] | RF | Miyun Reservoir | Chl-a | R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56% |
[74] | RF | - | Turbidity | MSE = 67.133, NMGE = 0.5763, PSNR = 29.861 |
[139] | RF | Yangtze River, Guojiaba, China | Chl-a | R2 = 0.8104, MAPE = 6.46% |
[140] | RF | Nansi Lake, China | Phytoplankton density | R2 = 0.67, RMSE = cells/L, MAE = cells/L |
[141] | RF | TN, TP, Turbidity | ||
[102] | RF | Yellow River, China | TSM | R2 = 0.90, RMSE = 0.56 mg/L |
[142] | RF | River Ganga, India | Chl-a, Turbidity | R2 = 0.91–0.97, MAE = 0.59–1.13, MAPE = 2.07–7.76% |
[92] | RF | Hulun Lake, China | Chl-a, COD, DO, NH3-N, TN, and TP | R2 = 0.7128–0.8376, RMSE = 0.0029–0.8923, MAE = 0.0017–0.6757 |
[93] | RF, SVR, ANN | Tianjin, China | COD, TN, and TP | R2 = 0.86–0.94 |
[119] | RF, XGB, GB | Karst wetlands, China | Turbidity, DO | R2 = 0.649–0.844 |
[150] | RF, XGB, GB, CB, DT | Ebinur Lake, China | TSM | R2 = 0.45–0.59, RMSE = 68.67–73.71 mg/L |
[151] | RF, XGB, AB, LB | Büyük Menderes Basin, Turkey | WQI | Accuracy = 93.03–95.60% |
[102] | RF | Liaohe River, China | TSS | R2 = 0.90, RMSE = 0.56 mg/L |
[76] | RF, XGB, LGB, CB | Nansi Lake, China | Chl-a and Turbidity | R2 = 0.7015–0.7927, RMSE = 2.1747–5.0617 μg/L, MAE = 1.6791–3.9776 μg/L |
[75] | DT | Saki Lake, India | Turbidity | R2 = 0.776, RMSE = 3.802, MAE = 3.246 |
[78] | RF, XGB, GB, KNN | China | Turbidity | R2 = 0.88, RMSE = 9.90 NTU, MAE = 6.71 NTU |
[81] | RF | China | Turbidity | R2 = 0.92 |
[87] | RF, SVR, ANN | Shenzhen Bay, Hong Kong | Chl-a, Temp, Turbidity | Errors = 0.02–1.7% |
[89] | XGB, ANN | Ganaga Basin, India | EC, DO, pH, TDS, Temp | R2 = 0.72–0.98 |
[104] | SVR, RF, ANN, KNN, SR | Brazil | Chl-a, pH, and TSS | R2 = 0.31–0.90, RMSE = 0.0052–0.0364 |
[94] | RF, ANN | Chaohu Lake, China | TN and TP | R2 = 0.46–0.78, RMSE = 0.0034–0.37 mg/L, MAPE = 8.34–38.60% |
Reference | Method | Architectural Details | Water Quality Parameters | Performance |
---|---|---|---|---|
[28] | CNN | Fusion of U-Net (23 layers) and SegNet (13 layers) | Chl-a, TN, and TP | MAE = 32.57–42.58% |
[158] | LSTM | Neurons/layer: 100, Optimizer: Adam, Loss function: MSE, No. of epochs: 100 | pH, DO, CDOM, turbidity, NH3-N, and electrical conductivity | MSE = 0.0017–0.0020 |
[32] | Conv-LSTM | Convolutional layers: 3, Fully connected LSTM layers: 3, Dropout layer: 1, Activation function: ReLU | Chl-a, TN, NH3-N, CODMn, TP, and BOD | R2 = 0.77–0.92 |
[159] | LSTM | Hidden layers: 15, Time step: 20, Learning rate: 0.0005, Training time: 10,000 | pH and Temperature | RMSE = 0.0025–0.0479, MAPE = 0.0012–0.0692, MAE = 0.0027–0.0149 |
[37] | CNN | Convolutional layers: 5, Pooling layers: 1, Fully connected layers: 1, learning rate = , momentum = 0.9, Epochs = 500 | Water quality levels | - |
[154] | GRU, LSTM, RNN | Hidden Layers: 3, Neurons/layer: 50 | BOD, COD, TN, TP, NH4-N | R2 = 0.90–0.94 |
[160] | CNN, LSTM | Convolutional layers: 3, Flattening layer: 1, LSTM layer: 1, Dropout: 0.001, Learning rate: 0.01 | Chl-a and DO | R = 0.869–0.97 |
[161] | Bi-LSTM | Convolutional layers: 2, Flattening layers: 2, Bi-LSTM layer: 1, Optimizer: Softmax, Batch size: 120, Learning rate: 0.001, Epochs: 2500 | BOD and COD | MSE = 0.015–0.107, RMSE = 0.108–0.117, MAE = 0.115–0.124, MAPE = 18.22–20.32 |
[157] | LSTM, RNN, GRU | Activation function: ReLU, Optimizer, Adam, Dropout = 0.001, Learning rate: 0.001, Epochs: 100 | Chl-a | Correlation Coefficient: 0.98, Standard Deviation: 0.93 |
[80] | LSTM | LSTM layers: 2, ReLU layer: 1, Dropout layer: 3, Dense layers: 2 | Turbidity | Accuracy = 97.2, Precision = 94.88, Recall = 86.3, and F1-score = 90.3 |
[56] | LSTM | - | TSS | R2 = 0.82, RMSE = 16.69 mg/L, MAE = 13.85 mg/L |
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Mohan, S.; Kumar, B.; Nejadhashemi, A.P. Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review. Sustainability 2025, 17, 998. https://doi.org/10.3390/su17030998
Mohan S, Kumar B, Nejadhashemi AP. Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review. Sustainability. 2025; 17(3):998. https://doi.org/10.3390/su17030998
Chicago/Turabian StyleMohan, Shashank, Brajesh Kumar, and A. Pouyan Nejadhashemi. 2025. "Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review" Sustainability 17, no. 3: 998. https://doi.org/10.3390/su17030998
APA StyleMohan, S., Kumar, B., & Nejadhashemi, A. P. (2025). Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review. Sustainability, 17(3), 998. https://doi.org/10.3390/su17030998