Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting

Ghaith, Maysara; Siam, Ahmad; Li, Zhong; El‐Dakhakhni, Wael

doi:10.1061/(asce)he.1943-5584.0001866

Cited by 65 publications

(22 citation statements)

References 45 publications

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“…Recently, to overcome the drawbacks of the aforementioned modelling techniques, a number of researchers coupled hydrological models with statistical tools (Noori and Kalin, 2016; Ghaith et al ., 2020). For example, Humphrey et al .…”

Section: Introductionmentioning

confidence: 99%

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An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

Liu

et al. 2021

Intl Journal of Climatology

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As one of the most pressing issues in the world, climate change has already caused evident impacts on natural and human systems (e.g., hydrological cycle, eco‐environment and socio‐economy) in recent decades. In this study, an integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis (MBHA) method is developed for quantifying climate change impacts on runoff. MBHA incorporates multiple global climate models (multi‐GCMs), hydrological model (HBV‐light), and Bayesian neural network (BNN) within a general framework. MBHA can provide the reliable prediction for runoff as well as reflect the impact of climate change on data scarcity catchments. MBHA is applied to the Amu Darya River basin in Central Asia. Climate data are derived from multiple GCMs (i.e., GFDL‐ESM2G, HadGEM2‐AO and NorESM1‐M) under RCP4.5 and RCP8.5. Several findings can be summarized: (1) during 2021–2100, both precipitation and temperature would increase, with more precipitation falling as rain instead of snow; (2) by 2100, glacier areas are predicted to reduce by 62.3% (RCP4.5) and 71.9% (RCP8.5); (3) under RCP8.5, monthly runoff would increase by 11.2% in 2021–2060 and reduce by 5.0% in 2061–2100; this is because the glaciers would rapidly disappear with the rising temperature after 2060. The findings suggest that the shrinked glacier and the reduced runoff threaten the water availability especially in summer seasons as well as affect the agricultural irrigation in the downstream of the Amu Darya River.

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Section: Introductionmentioning

confidence: 99%

“…Ghaith et al . (2020) combined HYMOD (the standard conceptual hydrological model) and ANN for daily runoff simulation, where the hybrid model had made a significant improvement to the individual modelling approach. Generally, the hybrid hydrological models can effectively improve runoff simulation accuracy.…”

Section: Introductionmentioning

confidence: 99%

An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

Liu

et al. 2021

Intl Journal of Climatology

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“…In order to perform GLUE, initially, parameter distribution was selected at random, and a huge number of model parameter combinations from that distribution were used for running the model for obtaining behavioral sets based on a probability threshold value that denotes the goodness of fit of the respective parameter combination that is simulating the system. As the likelihood function value increases, it indicates that the predicted discharge best correlates to the observed discharge [18,19]. "The threshold value which is used to separate behavioral parameter as well as non-behavioral parameter combinations can be obtained either as a permissible deviation from the maximum likelihood value in the sample or in the form of a fixed percentage of the total simulations count" [12,13].…”

Section: Glue (Generalized Likelihood Uncertainty Estimation) -mentioning

confidence: 99%

“…Second, there are questions about satellite imagery. These two concerns are discussed using the Generalized Probability Uncertainty Calculation (GLUE) method [10,11]. Calibration of the model by GLUE is conducted using the width of the river water surface derived from satellite observation, as well as uncertainty estimate for both width extraction and discharge prediction.…”

Section: Introductionmentioning

confidence: 99%

Estimation of River Discharge from Satellite Observations

Bahekar¹

2021

TURCOMAT

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The role of streamflow is very important in any type of hydrologic. For very effective flood routing and hydraulic structure design, it is important to have a large dataset of past years. We now have a conceptual rainfall-runoff model that can predict streamflow based on pre-existing datasets. Because there is no or very little observed data in un-gauged basins, calibrating these models to predict daily streamflow becomes difficult. Nowadays, parameters for example river width can be observed using satellite images, and some studies show a promising associated relation between discharge and river width. The suggested study demonstrates a method for calculating streamflow from river width extracted with the help of satellite imagery. To predict streamflow, hydrological models are calibrated using river width instead of in site observed streamflow, and for estimating uncertainty Generalized Likelihood Uncertainty Estimation (GLUE) is used. For validation, the suggested method is implemented in the Kharun river basin situated in the Chhattisgarh state of India. The obtained Nash-Sutcliffe efficiency is 92.6 % for simulated river discharge in 2019-2020 at the 50% quantile, which is promising.

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“…From the application area viewpoint, another significant consideration is the selection of the best model input that drives the dependent variable [50,51]. Several techniques were applied in different studies, such as principal component analysis (PCA) [52,53], variance inflation factor (VIF) [21,35] and mutual information (MI) [54,55].…”

Section: Introductionmentioning

confidence: 99%

Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

Zubaidi

Ortega‐Martorell

Al-Bugharbee

et al. 2020

Water

147

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The proper management of a municipal water system is essential to sustain cities and support the water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Moreover, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth.

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Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting

Cited by 65 publications

References 45 publications

An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

Estimation of River Discharge from Satellite Observations

Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

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