Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Sustainability, Год журнала: 2024, Номер 16(4), С. 1376 - 1376

Опубликована: Фев. 6, 2024

Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood forecasting, optimal reservoir management, equitable water allocation. Despite significant advancements in the field, accurately predicting extreme events continues to be persistent challenge due complex surface subsurface watershed processes. Therefore, addition framework, numerous techniques have been used enhance accuracy physical consistency. This work provides well-organized review of more than two decades efforts SFP physically consistent way using process modeling flow domain knowledge. covers hydrograph analysis, baseflow separation, process-based (PBM) approaches. paper an in-depth analysis each technique discussion their applications. Additionally, existing are categorized, revealing research gaps promising avenues future research. Overall, this offers valuable insights into current state enhanced within consistent, knowledge-informed data-driven framework.

Язык: Английский

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Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm

Water Resources Management, Год журнала: 2023, Номер 37(10), С. 3953 - 3972

Опубликована: Июнь 9, 2023

Язык: Английский

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Bayesian joint longitudinal models for assessing the exploitation rates of sardine stock in the Mediterranean Sea

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(4), С. 1635 - 1646

Опубликована: Янв. 12, 2024

Abstract The European sardine is a pelagic species of great ecological importance for the conservation Mediterranean Sea as well economic countries. Its fishing has suffered significant decline in recent years due to various economic, cultural and reasons. This paper focuses on evolution catches from 1985 2018 according country type practised, artisanal industrial. We propose three Bayesian longitudinal linear mixed models assess differences temporal industrial fisheries between within Overall results confirm that fishery time series are highly diverse along their dynamics this heterogeneity persistent throughout time. Furthermore, our highlight positive correlation fishing. Finally, study observes consistent decreasing trend quantity fish landings. Although causes feature could be also linked motivations (such reduction demand or reorientation fleets towards more commercially beneficial species), it may indicate potential risk stock Sea.

Язык: Английский

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Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

Journal of Hydrology, Год журнала: 2024, Номер 636, С. 131220 - 131220

Опубликована: Апрель 19, 2024

Язык: Английский

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A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network

Acta Geophysica, Год журнала: 2024, Номер 72(6), С. 4575 - 4595

Опубликована: Май 15, 2024

Язык: Английский

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Proxy-based Bayesian inversion of strain tensor data measured during well tests

Geomechanics for Energy and the Environment, Год журнала: 2023, Номер 36, С. 100506 - 100506

Опубликована: Сен. 22, 2023

Язык: Английский

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Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of Iran

Environmental Science and Pollution Research, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 30, 2024

Язык: Английский

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Sediment yield modelling using SDR and MUSLE with high resolution satellite precipitation dataset in an ungauged basin

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Март 1, 2023

Abstract Erosion has become one of the extreme ecological dangers in up to date. Decrease minerals upper layer soil leads failure sustainable crop practices. Many researchers have developed prediction models Sediment Yield (SY) gauged basins. But modelling for an ungauged basin is very difficult due difficulty validating predicted model. The primary goal study was identify sedimentation area using multiple(SY) methodologies, including Delivery Ratio (SDR) and Modified Universal Soil Loss Equation (MUSLE), with a high-resolution satellite precipitation dataset.. Different attributes such as LULC (land use / land cover), texture, precipitation, topography, etc. incorporated estimate SY Ponnaniyar river basin,. generated map from SDR MUSLE evaluated by receiver operating characteristic curve (ROC). model found be efficient method determining basin, also satisfied criteria AUC value 0.752. severely affected sub-watershed identified help erosion yield spatial map. obtained results will prioritize locating water harvesting structures further studies. This suggests placing gauging station monitor daily observation discharge estimation prevent loss during flash flooding.

Язык: Английский

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Radon in Hydrograph Separation and Water Balance Studies

Environmental science and engineering, Год журнала: 2023, Номер unknown, С. 109 - 124

Опубликована: Янв. 1, 2023

Язык: Английский

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