Deep learning for water quality

Nature Water, Journal Year: 2024, Volume and Issue: 2(3), P. 228 - 241

Published: March 12, 2024

Language: Английский

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An interpretable hybrid deep learning model for flood forecasting based on Transformer and LSTM

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 54, P. 101873 - 101873

Published: June 27, 2024

Language: Английский

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A New Benchmark on Machine Learning Methodologies for Hydrological Processes Modelling: A Comprehensive Review for Limitations and Future Research Directions

Knowledge-Based Engineering and Sciences, Journal Year: 2023, Volume and Issue: 4(3), P. 65 - 103

Published: Dec. 31, 2023

The best practice of watershed management is through the understanding hydrological processes. As a matter fact, processes are highly associated with stochastic, non-linear, and non-stationary phenomena. Hydrological simulation modeling challenging issues in domains hydrology, climate environment. Hence, development machine learning (ML) models for solving those complex problems took essential place over past couple decades. It can be observed, data availability has increased remarkably, thus computational resources led to resurgence ML models’ development. been witnessed huge efforts on using facility several review researches have conducted. Literature studies approved capacity field hydrology classical “traditional models” based their forecastability, flexibility, precision, generalization, execution convergence speed. However, although potential merits were observed model’s development, limitations allied such as interpretability black-box models, practicality management, difficulty explain physical In this survey, an exhibition all published articles recognize research gaps direction. ultimate aim current survey establish new milestone interested environment researchers applications models.

Language: Английский

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A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Water, Journal Year: 2024, Volume and Issue: 16(10), P. 1407 - 1407

Published: May 15, 2024

Artificial intelligence has undergone rapid development in the last thirty years and been widely used fields of materials, new energy, medicine, engineering. Similarly, a growing area research is use deep learning (DL) methods connection with hydrological time series to better comprehend expose changing rules these series. Consequently, we provide review latest advancements employing DL techniques for forecasting. First, examine application convolutional neural networks (CNNs) recurrent (RNNs) forecasting, along comparison between them. Second, made basic enhanced long short-term memory (LSTM) analyzing their improvements, prediction accuracies, computational costs. Third, performance GRUs, other models including generative adversarial (GANs), residual (ResNets), graph (GNNs), estimated Finally, this paper discusses benefits challenges associated forecasting using techniques, CNN, RNN, LSTM, GAN, ResNet, GNN models. Additionally, it outlines key issues that need be addressed future.

Language: Английский

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A novel strategy for flood flow Prediction: Integrating Spatio-Temporal information through a Two-Dimensional hidden layer structure

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 638, P. 131482 - 131482

Published: June 13, 2024

Language: Английский

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Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets

Water, Journal Year: 2024, Volume and Issue: 16(13), P. 1904 - 1904

Published: July 3, 2024

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements artificial intelligence the availability large, high-quality datasets. This review explores current state ML hydrology, emphasizing utilization extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, GRACE. These provide critical data for modeling various parameters, including streamflow, precipitation, groundwater levels, flood frequency, particularly data-scarce regions. We discuss type methods used significant successes achieved through those models, highlighting their enhanced predictive accuracy integration diverse sources. The also addresses challenges inherent applications, heterogeneity, spatial temporal inconsistencies, issues regarding downscaling LSH, need incorporating human activities. In addition to discussing limitations, this article highlights benefits utilizing high-resolution compared traditional ones. Additionally, we examine emerging trends future directions, real-time quantification uncertainties improve model reliability. place a strong emphasis on citizen science IoT collection hydrology. By synthesizing latest research, paper aims guide efforts leveraging large techniques advance enhance water resource management practices.

Language: Английский

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Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101309 - 101309

Published: Aug. 1, 2024

Language: Английский

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River discharge prediction based multivariate climatological variables using hybridized long short-term memory with nature inspired algorithm

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132453 - 132453

Published: Dec. 1, 2024

Language: Английский

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Comparison and prediction of shallow groundwater nitrate in Shaying River basin based on urban distribution using multiple machine learning approaches

Water Environment Research, Journal Year: 2025, Volume and Issue: 97(2)

Published: Feb. 1, 2025

Groundwater, a pivotal water resource in numerous regions worldwide, confronts formidable challenges posed by severe nitrate pollution. Traditional research methodologies aimed at addressing groundwater contamination frequently struggle to accurately depict the intricate conditions of environment, particularly when dealing with high variability and nonlinear data. However, advent machine learning (ML) has heralded an innovative approach simulating dynamics. In this study, six ML algorithms were deployed model concentrations shallow nitrates Shaying River Basin. The efficacy each was assessed through comprehensive metrics including coefficient determination (R2), mean absolute error (MAE), root square (RMSE), gauging alignment between observed predicted levels. Subsequently, discern principal environmental factors influencing NO3-N concentrations, most proficient selected. Among array models, XGB algorithm, renowned for its capacity handle extreme values, demonstrated superior performance (R2 = 0.773, MAE 7.625, RMSE 11.92). Through in-depth analysis across major urban centers, Fuyang city identified as heavily contaminated locale, attributing phenomenon potential sources such domestic sewage agricultural activities (feature importance Cl- 78.64%). Conversely, Zhengzhou emerged least polluted city, notable influences from K+ NO2 - 52.06% 18.41%), indicative prevailing reducing environment compared other cities. summation, study explores methodology amalgamating diverse variables investigation contamination. Such insights hold profound implications effective management mitigation Basin, offering demonstration similar endeavors analogous regions. PRACTITIONER POINTS: Six models utilized simulate pollution prediction outperformed models. Relative using model. Impact main on discussed.

Language: Английский

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A state-of-the-art review of long short-term memory models with applications in hydrology and water resources

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112352 - 112352

Published: Oct. 1, 2024

Language: Английский

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