Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review
Abstract
:1. Introduction
1.1. Problem Statement
1.2. Contribution
2. Materials and Methods
2.1. Hydrology Background
2.2. Water Quality Indicators
2.3. River Water Quality Forecasting in Recent Years
2.3.1. Real-Time Monitoring for Water Quality
2.3.2. Advances in Remote Sensing and Machine Learning for River Water Quality Prediction
2.3.3. Decision-Support Systems for Water Management
3. Machine Learning Models Used in River Water Quality Forecasting
3.1. Traditional Machine Learning Models
3.2. Deep Learning Models
3.2.1. Artificial Neural Networks
3.2.2. Recurrent Neural Networks
3.2.3. Convolutional Neural Networks
3.2.4. Long Short-Term Memory Networks
3.2.5. Transformer Models
3.3. Hybrid Machine Learning Models
3.4. Overview of the Useful Machine Learning Method for River Water Quality Forecasting
4. Limitations of the Present River Water Quality Forecasting Methods
4.1. Disadvantages of Machine Learning Models for Forecasting
4.1.1. Traditional Machine Learning Models
4.1.2. Deep Learning Models
4.1.3. Hybrid Machine Learning Models
4.2. Summary of the Machine Learning Models Used for River Water Quality
4.3. Challenges in Data Collection for River Water Quality Forecasting
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Indicators * | Core Function and Impact | Key Influencing Factors |
---|---|---|---|
Physical indicators | Temperature | Influences chemical, biological, and other physical indicators [23,47] (higher temperature causes lower DO [48]) | Geographical factors and water depth |
Discharge | Affects pollutants (high discharge decreases concentrations of contaminants like nutrients and heavy metals [49,50]). | Rainfall and climate change | |
Turbidity | Cloudiness of water, some suspended particles in water [19], [51,52,53,54,55,56,57]; reduces light penetration, limits primary production, and decreases DO. | Potential contamination and soil erosion. | |
Chemical indicators | TDS | Important drinking water quality indicators [58], indicating the exchange between groundwater and surface water [59]. Higher turbidity may cause higher TDS. | Geological composition, climate, and anthropogenic activities [60,61]. |
TSS | The solid particle concentration suspended in water [55,62,63]; important for aquatic ecosystems’ health and closely related to turbidity [57]. | Climate, and anthropogenic activities | |
DO | Important for the health and sustainability of aquatic ecosystems [64,65,66,67]. A lower DO level (less than 2 mg/L) shows hypoxic and dead zone [48]. Many water bodies have faced hypoxia [68,69,70,71,72]. | Temperature and algae bloom | |
BOD | The amount of DO aerobic microorganisms require to break down organic matter present [73,74,75], also an indicator of eutrophication. | Pollution release and organism consumption | |
COD | The level of pollution in water bodies [76,77]; includes biodegradable and non-biodegradable compounds; a faster alternative to BOD for assessing overall pollution levels. | Industry waste and surface runoff | |
pH | Aquatic life suffers from extreme pH values [55,78]. An extreme pH value threatens the life safety of most aquatic organisms. | Acid rain [79], industrial discharges and agricultural runoff | |
Nutrient levels | Water nutrient levels (total nitrogen (TN), total phosphorus (TP) and ammonia) support the growth of plants and algae [19,20,80]. Excessive nutrient concentrations lead to eutrophication, harmful algal blooms, and oxygen depletion. | Fertilizer application, domestic sewage | |
Heavy metals | Toxic (lead (Pb), mercury (Hg), cadmium (Cd), chromium (Cr) [54]) even when concentrations are low [81,82]. | Industry waste | |
Bio indicators | Algae | Algal blooms disrupt the ecosystem balance. Some harmful algae produce toxins [83,84]. Algae amounts are determined by the amount of Chlorophyll-a (Chl-a) [85]. | Nutrient levels [19,20,80,86], temperature and turbidity. |
Coliform bacteria | Live in warm-blooded animals’ body, soil, and water [34,52,87,88,89]. A higher number of coliform bacteria causes higher BOD and lower DO. | Fecal matter, agriculture and pollution |
Feature | LSTMs | GRUs |
---|---|---|
Parameter Complexity | More parameters | Fewer parameters |
Computational Efficiency | Slower due to complexity | Faster due to simplicity |
Performance | Better for long sequences | Comparable for shorter sequences |
Flexibility | Offers more control over memory management | Simpler and easier to implement |
Application | Capturing the effects of climate variability or upstream pollution events over extended timeframes | Modeling short-term impacts of rainfall events on downstream water quality |
A * | B * | C * | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Temperature | Discharge | Turbidity | DO | BOD | COD | TSS | TDS | Nutrient Levels | pH | Heavy metal | Algae | Total Coliform | ||
Traditional machine learning | ||||||||||||||
SVR (2015) [175] | ✓ | |||||||||||||
ARIMA (2020, 2022) [176,177] | ✓ | ✓ | ||||||||||||
ETR (2021) [165] | ✓ | ✓ | ✓ | ✓ | ||||||||||
Random Forest (2022) [178] | ✓ | ✓ | ||||||||||||
XGBoost (2022) [128,179] | ✓ | ✓ | ✓ | ✓ | ||||||||||
NuSVC (2022) [180] | ✓ | |||||||||||||
Regression (2025) [181] | ✓ | ✓ | ||||||||||||
GWR (2025) [182] | ✓ | |||||||||||||
Deep learning | ||||||||||||||
LMA (2009) [134] | ✓ | ✓ | ||||||||||||
MLP (2019) [183] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
DNN (2020, 2022, 2023) [180,184,185] | ✓ | ✓ | ✓ | ✓ | ||||||||||
FNN (2021) [186] | ✓ | |||||||||||||
LSTM (2022) [187] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
STNX (2024) [188] | ✓ | ✓ | ✓ | |||||||||||
LWQformer (2025) [189] | ✓ | ✓ | ✓ | |||||||||||
Hybrid machine learning | ||||||||||||||
CEEMDAN-XGBoost (2020) [190] | ✓ | ✓ | ||||||||||||
CEEMDAN-RF (2020) [190] | ✓ | ✓ | ||||||||||||
SWAT-ANN (2020) [191] | ✓ | ✓ | ✓ | |||||||||||
1-DRCNN-BiGRU (2021) [192] | ✓ | ✓ | ||||||||||||
PCA-QRF (2021) [193] | ✓ | |||||||||||||
TrAdaBoost-LSTM (2021) [194] | ✓ | |||||||||||||
SWT-ISSA-LSTM (2021) [195] | ✓ | |||||||||||||
VMD-SSA-LSSVM (2021) [167] | ✓ | ✓ | ||||||||||||
PSO-GEP (2021) [196] | ✓ | ✓ | ||||||||||||
CNN-LSTM (2021) [197,198,199] | ✓ | ✓ | ✓ | |||||||||||
ANN-WT-LSTM (2022) [200] | ✓ | ✓ | ✓ | |||||||||||
SOD-VGG-LSTM (2022) [201] | ✓ | ✓ | ✓ | |||||||||||
DLBL-WQA (2022) [202] | ✓ | ✓ | ||||||||||||
VMD-IGOA-LSTM (2023) [203] | ✓ | ✓ | ✓ | |||||||||||
SG-STL-TCN (2023) [204] | ✓ | |||||||||||||
GJO+CNN-LSTM-TCN (2023) [205] | ✓ | |||||||||||||
GNN-SAGE (2023) [206] | ✓ | |||||||||||||
ANN-GBM (2023) [207] | ✓ | |||||||||||||
ReliefF-GSA+ LightGBM (2024) [208] | ✓ | ✓ | ||||||||||||
SVM-RBF (2024) [209] | ✓ | |||||||||||||
CGAN-CA-CNN (2025) [210] | ✓ | |||||||||||||
EMD-RBMO-SVMD-CNN-BiGRU (2025) [211] | ✓ | |||||||||||||
LSTM-MOOTLBO (2025) [212] | ✓ |
Reference | Model | Climate Zone | Impacted by | Best Predicted Parameters |
---|---|---|---|---|
Callegari et al. (2015) [175] | SVR | Cold alpine region | Seasonal river discharge; snow cover area | RMSE = 24%; = 0.81 (discharge) |
Alnahit et al. (2022) [178] | Random Forest | Humid subtropical | Agriculture and urbanization | RMSE = 0.061; MAE = 0.022; NSE = 0.56 (nutrient levels) |
Khoi et al. (2022) [179] | XGBoost | Tropical monsoon | Agriculture, urbanization and industry | RMSE = 0.107; = 0.989 (WQI) |
Peterson et al. (2020) [184] | DNN | Temperate continental | Agricultural runoff | = 0.883 (Chl-a), 0.894 (DO), 0.839 (turbidity) RMSE = 7.561 (Chl-a), 1.806 (DO), 5.190 (turbidity) MAPE = 26.71 (Chl-a), 9.08 (DO), 9.87 (turbidity) |
Feigl et al. (2021) [186] | FNN | Temperate | Climate-driven river temperature | RMSE = 0.422; MSE = 0.329 (temperature) |
Yan et al. (2021) [192] | 1-DRCNN-BiGRU | Temperate | Industrial and agricultural runoff | MAPE = 2.4866; = 0.9431 (nutrient levels) |
Song et al. (2021) [167] | VMD-SSA-LSSVM | South subtropical monsoon | Precipitation and industry | MAE = 0.2178; RMSE = 0.2934; MAPE = 2.4637%; = 0.6351; NSE = 0.9541 (DO) |
Wang et al. (2025) [209] | CGAN-CA-CNN | subtropical monsoon | Agriculture and industry | RMSE = 0.0019; MAE = 0.0009; = 0.9805 (nutrient levels) |
Doroudi et al. (2025) [212] | LSTM-MOOTLBO | Subtropical dry summer | Urbanization | = 0.967; RMSE = 0.303; NSE = 0.935 (DO) |
Traditional ML | Deep Learning | Hybrid ML | |
---|---|---|---|
Advantages | Strong predictive ability for simple nonlinear relationships; highly adaptable | High accuracy, capable of learning spatial–temporal dynamics, suitable for large-scale, complex datasets | Highly comprehensive, significantly improves accuracy, ideal for addressing complex water quality issues |
Disadvantages | Relies heavily on data quality; prone to overfitting; difficult to interpret | High computational cost; requires substantial resources; lacks transparency | Complex architecture, longer development cycles, requires advanced technical expertise |
Common methods | Random Forest, SVM, XGBoost | NN-based model | Deep learning (like LSTM and GRU) models; integration of physical and statistical/ML models |
Suitable forecast parameters | Heavy metal concentrations | Captures spatial–temporal dynamics, suitable for complex time-variant parameters like DO, BOD and COD | Comprehensive modeling, able to handle multi-dimensional parameters like discharge, and long-term trends |
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Pan, D.; Deng, Y.; Yang, S.X.; Gharabaghi, B. Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review. Environments 2025, 12, 158. https://doi.org/10.3390/environments12050158
Pan D, Deng Y, Yang SX, Gharabaghi B. Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review. Environments. 2025; 12(5):158. https://doi.org/10.3390/environments12050158
Chicago/Turabian StylePan, Daiwei, Ying Deng, Simon X. Yang, and Bahram Gharabaghi. 2025. "Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review" Environments 12, no. 5: 158. https://doi.org/10.3390/environments12050158
APA StylePan, D., Deng, Y., Yang, S. X., & Gharabaghi, B. (2025). Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review. Environments, 12(5), 158. https://doi.org/10.3390/environments12050158