A review of hybrid deep learning applications for streamflow forecasting

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130141 - 130141

Published: Sept. 12, 2023

Language: Английский

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Application of Machine Learning in Water Resources Management: A Systematic Literature Review

Water, Journal Year: 2023, Volume and Issue: 15(4), P. 620 - 620

Published: Feb. 5, 2023

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing importance world’s water supply throughout rest this century, much research has been concentrated on application strategies integrated resources management (WRM). Thus, a thorough well-organized review that is required. To accommodate underlying knowledge interests both artificial intelligence (AI) unresolved issues in WRM, overview divides core fundamentals, major applications, ongoing into two sections. First, basic are categorized three main groups, prediction, clustering, reinforcement learning. Moreover, literature organized each field according new perspectives, patterns indicated so attention can be directed toward where headed. second part, less investigated WRM addressed provide grounds for future studies. The widespread tools projected accelerate formation sustainable plans over next decade.

Language: Английский

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A deep learning interpretable model for river dissolved oxygen multi-step and interval prediction based on multi-source data fusion

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 629, P. 130637 - 130637

Published: Jan. 14, 2024

Language: Английский

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Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 624, P. 129888 - 129888

Published: July 1, 2023

Language: Английский

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Applications of machine learning to water resources management: A review of present status and future opportunities

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 441, P. 140715 - 140715

Published: Jan. 11, 2024

Water is the most valuable natural resource on earth that plays a critical role in socio-economic development of humans worldwide. used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at global scale, coupled with predicted climate change-induced impacts, warrants need proactive effective management water resources. Over recent decades, machine learning tools have been widely applied to resources management-related fields often shown promising results. Despite publication several review articles applications water-related fields, this paper presents first time comprehensive techniques management, focusing achievements. study examines potential advanced improve decision support systems sectors within realm which includes groundwater streamflow forecasting, distribution systems, quality wastewater treatment, demand consumption, marine energy, drainage flood defence. This provides an overview state-of-the-art approaches industry how they can be ensure supply sustainability, quality, drought mitigation. covers related studies provide snapshot industry. Overall, LSTM networks proven exhibit reliable performance, outperforming ANN models, traditional established physics-based models. Hybrid ML exhibited great forecasting accuracy across all showing superior computational power over ANNs architectures. In addition purely data-driven physical-based hybrid models also developed prediction performance. These efforts further demonstrate Machine powerful practical tool management. It insights, predictions, optimisation capabilities help enhance sustainable use development, healthy ecosystems human existence.

Language: Английский

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A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting

Water, Journal Year: 2023, Volume and Issue: 15(7), P. 1265 - 1265

Published: March 23, 2023

Improving the accuracy and stability of daily runoff prediction is crucial for effective water resource management flood control. This study proposed a novel stacking ensemble learning model based on attention mechanism prediction. The has two-layer structure with base meta model. Three machine models, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient (XGB) are used as models. to integrate output obtain predictions. applied predict inflow Fuchun River Reservoir in Qiantang basin. results show that outperforms models other terms accuracy. Compared XGB weighted averaging (WAE) 10.22% 8.54% increase Nash–Sutcliffe efficiency (NSE), an 18.52% 16.38% reduction root mean square error (RMSE), 28.17% 18.66% absolute (MAE), 4.54% 4.19% correlation coefficient (r). significantly simple indicated by both Friedman test Nemenyi test. Thus, can produce reasonable accurate reservoir inflow, which great strategic significance application value formulating rational allocation optimal operation resources improving breadth depth hydrological forecasting integrated services.

Language: Английский

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A stacked machine learning model for multi-step ahead prediction of lake surface water temperature

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 890, P. 164323 - 164323

Published: May 20, 2023

Language: Английский

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Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 627, P. 130380 - 130380

Published: Oct. 21, 2023

Streamflow forecasting is crucial in water planning and management. Physically-based hydrological models have been used for a long time these fields, but improving forecast quality still an active area of research. Recently, some artificial neural networks found to be effective simulating predicting short-term streamflow. In this study, we examine the reliability Long Short-Term Memory (LSTM) deep learning model streamflow lead times up ten days over Canadian catchment. The performance LSTM compared that process-based distributed model, with both using same weather ensemble forecasts. Furthermore, LSTM’s ability integrate observed on issue date data assimilation process required reduce initial state biases. Results indicate forecasted streamflows are more reliable accurate lead-times 7 9 days, respectively. Additionally, it shown recent flows as predictor can smaller errors first without requiring explicit step, generating median value mean absolute error (MAE) day lead-time across all dates 25 m3/s 115 assimilated model.

Language: Английский

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Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Environmental Pollution, Journal Year: 2024, Volume and Issue: 355, P. 124242 - 124242

Published: May 27, 2024

Language: Английский

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Advancements in daily precipitation forecasting: A deep dive into daily precipitation forecasting hybrid methods in the Tropical Climate of Thailand

MethodsX, Journal Year: 2024, Volume and Issue: 12, P. 102757 - 102757

Published: May 31, 2024

Language: Английский

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