A review of deep learning and machine learning techniques for hydrological inflow forecasting

Environment Development and Sustainability, Journal Year: 2023, Volume and Issue: 25(11), P. 12189 - 12216

Published: March 17, 2023

Language: Английский

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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 155(1), P. 1 - 44

Published: Aug. 28, 2023

Abstract Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency intensity of extremes other associated are continuously increasing due climate change global warming. accurate prediction, characterization, attribution atmospheric is, therefore, a key research field in which many groups currently working by applying different methodologies computational tools. Machine learning deep methods have arisen the last years as powerful techniques tackle problems related events. This paper reviews machine approaches applied analysis, most important extremes. A summary used this area, comprehensive critical review literature ML EEs, provided. has been extended rainfall floods, heatwaves temperatures, droughts, weather fog, low-visibility episodes. case study focused on analysis temperature prediction with DL is also presented paper. Conclusions, perspectives, outlooks finally drawn.

Language: Английский

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Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods

Water, Journal Year: 2023, Volume and Issue: 15(6), P. 1179 - 1179

Published: March 18, 2023

Rainfall–runoff modeling has been of great importance for flood control and water resource management. However, the selection hydrological models is challenging to obtain superior simulation performance especially with rapid development machine learning techniques. Three under different categories methods, including support vector regression (SVR), extreme gradient boosting (XGBoost), long-short term memory neural network (LSTM), were assessed simulating daily runoff over a mountainous river catchment. The performances input scenarios compared. Additionally, joint multifractal spectra (JMS) method was implemented evaluate during wet dry seasons. results show that: (1) LSTM always obtained higher accuracy than XGBoost SVR; (2) impacts variables such as antecedent streamflow rainfall LSTM; (3) showed relatively high seasons, classification seasons improved performance, seasons; (4) JMS analysis indicated advantages hybrid model combined trained wet-season data dry-season data.

Language: Английский

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Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 131059 - 131059

Published: March 8, 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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Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling

Atmosphere, Journal Year: 2020, Volume and Issue: 11(11), P. 1252 - 1252

Published: Nov. 20, 2020

Precipitation is essential for modeling the hydrologic behavior of watersheds. There exist multiple precipitation products different sources and precision. We evaluate influence product on model parameters streamflow predictive uncertainty using a soil water assessment tool (SWAT) forest dominated catchment in India. used IMD (gridded rainfall dataset), TRMM (satellite product), bias-corrected (corrected satellite product) NCEP-CFSR (reanalysis dataset) over period from 1998–2012 simulating streamflow. The analysis statistical measures revealed that CFSR data slightly overestimate compared to ground-based data. However, estimates improved, applying bias correction. Nash–Sutcliffe (and R2) values TRMM, TRMMbias CFSR, are 0.58 (0.62), 0.62 (0.63) 0.52 (0.54), respectively at calibrated with (Scenario A). models each B) yielded 0.71 (0.76), 0.74 (0.78) 0.76 (0.77) datasets, respectively. Thus, hydrological model-based evaluation calibration individual as input showed increased accuracy simulation. forced perform better capturing simulations than reanalysis-driven model. Overall, our results after proper correction could be good alternative ground observations driving models.

Language: Английский

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Quantile-based Bayesian Model Averaging approach towards merging of precipitation products

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 604, P. 127206 - 127206

Published: Nov. 25, 2021

Language: Английский

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A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting – A case study in the Awash River Basin (Ethiopia)

Environmental Modelling & Software, Journal Year: 2021, Volume and Issue: 144, P. 105119 - 105119

Published: July 3, 2021

Language: Английский

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Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(4), P. 826 - 826

Published: Feb. 23, 2021

This study investigates the applicability of Satellite Precipitation Products (SPPs) in near real-time for simulation sub-daily runoff Vilcanota River basin, located southeastern Andes Peru. The data from rain gauge stations are used to evaluate quality Integrated Multi-satellite Retrievals GPM–Early (IMERG-E), Global Mapping Precipitation–Near Real-Time (GSMaP-NRT), Climate Prediction Center Morphing Method (CMORPH), and HydroEstimator (HE) at pixel-station level; these SPPs as meteorological inputs hourly hydrological modeling. GR4H model is calibrated with hydrometric station longest record, simulations also verified one upstream two downstream calibration point. Comparing precipitation observed, results show that IMERG-E product generally presents higher quality, followed by GSMaP-NRT, CMORPH, HE. Although present positive negative biases, ranging mild moderate, they do represent diurnal seasonal variability area. In terms average Kling-Gupta metric (KGE), GR4H_GSMaP-NRT’ yielded best representation discharges (0.686), GR4H_IMERG-E’ (0.623), GR4H_Ensemble-Mean (0.617) GR4H_CMORPH’ (0.606), GR4H_HE’ (0.516). Finally, showed a high potential monitoring floods basin operational level. obtained this research very useful implementing flood early warning systems will allow short-term forecasting Peruvian National Weather Hydrological Service.

Language: Английский

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Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 613, P. 128341 - 128341

Published: Aug. 22, 2022

Language: Английский

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