Bayesian extreme learning machines for hydrological prediction uncertainty

Quilty, John; Jahangir, Mohammad Sina; You, John; Hughes, Henry; Hah, David; Tzoganakis, Ioannis

doi:10.1016/j.jhydrol.2023.130138

Cited by 3 publications

(1 citation statement)

References 87 publications

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“…Given this context, in order to carry out the attribution analysis of runoff variation in the reservoir construction area, reconstructing the runoff affected by the reservoir emerged as an appealing approach. The Long Short-term Memory network (LSTM) [40], with its aptitude for learning long-term, feature-rich data, demonstrated its suitability for hydrological modeling in recent studies [41][42][43][44][45]. As previously mentioned, the variation in short-duration extremes also holds significant information for water resource management, and the accurate simulation of extreme values is both a focal point and a challenge [46,47].…”

Section: Introductionmentioning

confidence: 99%

Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework

Zhang,

Chen,

Huang

et al. 2024

Atmosphere

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This study presents a framework to attribute river runoff variations to the combined effects of reservoir operations, land surface changes, and climate variability. We delineated the data into natural and impacted periods. For the natural period, an integrated Long Short-Term Memory and Random Forest model was developed to accurately simulate both mean and extreme runoff values, outperforming existing models. This model was then used to estimate runoff unaffected by human activities in the impacted period. Our findings indicate stable annual and wet season mean runoff, with a decrease in wet season maximums and an increase in dry season means, while extreme values remained largely unchanged. A Budyko framework incorporating reconstructed runoff revealed that rainfall and land surface changes are the predominant factors influencing runoff variations in wet and dry seasons, respectively, and land surface impacts become more pronounced during the impacted period for both seasons. Human activities dominate dry season runoff variation (93.9%), with climate change at 6.1%, while in the wet season, the split is 64.5% to 35.5%. Climate change and human activities have spontaneously led to reduced runoff during the wet season and increased runoff during the dry season. Only reservoir regulation is found to be linked to human-induced runoff changes, while the effects of land surface changes remain ambiguous. These insights underscore the growing influence of anthropogenic factors on hydrological extremes and quantify the role of reservoirs within the impacts of human activities on runoff.

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Section: Introductionmentioning

confidence: 99%

Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework

Zhang,

Chen,

Huang

et al. 2024

Atmosphere

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Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes

Tran,

Ivanov,

Kim

2023

Advances in Water Resources

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Analysis of discharge characteristics of a symmetrical stepped labyrinth side weir based on global sensitivity

Wan,

Shen,

et al. 2023

Journal of Hydroinformatics

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In this paper, the discharge coefficient prediction model for this structure in a subcritical flow regime is first established by extreme learning machine (ELM) and Bayesian network, and the model's performance is analyzed and verified in detail. In addition, the global sensitivity analysis method is introduced to the optimal prediction model to analyze the sensitivity for the dimensionless parameters affecting the discharge coefficient. The results show that the Bayesian extreme learning machine (BELM) can effectively predict the discharge coefficients of the symmetric stepped labyrinth side weir. The range of 95% confidence interval [−0.055,0.040] is also significantly smaller than that of the ELM ([−0.089,0.076]) and the Kernel extreme learning machine (KELM) ([−0.091,0.081]) at the testing stage. The dimensionless parameter ratio of upstream water depth of stepped labyrinth side weir p/y1 has the greatest effect on the discharge coefficient Cd, accounting for 55.57 and 54.17% under single action and other parameter interactions, respectively. Dimensionless step number bs/L has little effect on Cd, which can be ignored. Meanwhile, when the number of steps is less (N = 4) and the internal head angle is smaller (θ = 45°), a larger discharge coefficient value can be obtained.

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Bayesian extreme learning machines for hydrological prediction uncertainty

Cited by 3 publications

References 87 publications

Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework

Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework

Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes

Analysis of discharge characteristics of a symmetrical stepped labyrinth side weir based on global sensitivity

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