Assessment of Using Machine and Deep Learning Applications in Surface Water Quantity and Quality Predictions: A Review
Keywords:
Assessment, Deep learning, Machine Learning, surface water, water qualityAbstract
This study aims to provide a comprehensive review of Machine learning (ML) and deep learning (DL) applications to predict surface water quantity and quality. Analysis of numerous research papers reveals that deep learning models, specifically those designed for handling time series data like Long Short-Term Memory (LSTM) and those processing image-like data like Convolutional Neural Networks (CNN), often achieve greater accuracy than traditional ML methods. Hybrid and ensemble machine and deep learning models generally exhibited the best performance for surface water quantity prediction, as demonstrated by models like One Dimensional Convolutional Neural Networks (1D-CNN), Water Balance Model-Support Vector Regression (WBM-SVR), Boruta Feature Selection Algorithm- Long-Short Term Memory (BRF-LSTM), Nonlinear Auto Regressive Exogenous Multi-Layer Perceptron- Random Forest (NARX-MLP-RF), Sparrow Search Algorithm - Artificial Neural Networks (SSA-ANN), and Support Vector Regression- Grey Wolf Optimization (SVR-GWO). A variety of models were applied for water quality prediction, with hybrid models combining aspects of different approaches Convolutional-LSTM (Conv-LSTM), and Random Tree- bagging (RT-BA) leveraging multiple algorithms' strengths. Deep learning models including LSTM, and CNN, commonly demonstrated strong predictive skills based on metrics like R2, NSE, and RMSE. In contrast, simpler machine learning models like Support Vector Regression (SVR), Gaussian Process Regression (GPR), Enhanced Extreme Learning Machine (EELM), and Artificial Neural Networks (ANNs) often showed moderate to low predictive ability. Future research should focus on developing models that can effectively address data limitations, incorporate climate change impacts, and are evaluated using more comprehensive metrics that capture factors beyond accuracy, such as uncertainty quantification and model interpretability.
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