Advancing flood warning procedures in ungauged basins with machine learning

Rasheed, Zimeena; Aravamudan, Akshay; Sefidmazgi, Ali Gorji; Anagnostopoulos, Georgios C.; Nikolopoulos, Efthymios I.

doi:10.1016/j.jhydrol.2022.127736

Cited by 22 publications

(6 citation statements)

References 74 publications

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“…where g 1 i and g 2 i are dimensionless time variant gain parameters of surface runoff for the ith subcatchment. API i (t) is the antecedent precipitation index of the ith subcatchment at time t, which represents the recent soil moisture state of the subcatchment, mm [31][32][33]:…”

Section: Runoff Module Of Distributed Time Variant Gain Modelmentioning

confidence: 99%

Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City

et al. 2023

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The storm water management model (SWMM) has been used extensively to plan, implement, control, and evaluate low impact development facilities and other drainage systems to solve storm-related problems in sponge cities. However, the calibration of SWMM involves a variety of sensitive parameters and may bring significant uncertainties. Here we incorporated the distributed time variant gain model (DTVGM), a model with a simple structure and few parameters, into the SWMM (called DTVGM-SWMM) to reduce the complexity but keep the mechanistic representation of the hydrological process. The DTVGM runoff module parameters were calibrated and validated using the Nash–Sutcliffe efficiency (NSE), based on measured data and the results of SWMM. It was then coupled with the SWMM routing module to estimate catchment runoffs and outflows. Finally, the performance was evaluated using NSE (0.57~0.94), relative errors of the flow depth (−7.59~19.79%), and peak flow rate (−33.68~54.37%) under different storm events. These implied that the DTVGM-SWMM simulations were generally consistent with those of the control group, but underperformed in simulating peak flows. Overall, the proposed framework could reasonably estimate the runoff, especially the outflow process in the urban catchment. This study provides a simple and reliable method for urban stormwater simulation.

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Section: Runoff Module Of Distributed Time Variant Gain Modelmentioning

confidence: 99%

Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City

et al. 2023

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“…However, if all these data were used, the machine learning model could be trained with substantially more data, which could facilitate more reasonable predictions of extreme streamflow. The eventual result would be an effective predictive model for the highest streamflow status and may be highly correlated with flood events [10].…”

Section: Identifying Flood Peaksmentioning

confidence: 99%

“…The development of statistical models and physically based distributed hydrological models (PB-DHMs) has traditionally been the focal point for simulating streamflow and flood peaks [8][9][10]. Statistical models utilize empirical datasets to determine underlying patterns for predicting future situations [11].…”

Section: Introductionmentioning

confidence: 99%

Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea

Yang

Lim

HaeWon

et al. 2023

Forests

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The severity and incidence of flash floods are increasing in forested regions, causing significant harm to residents and the environment. Consequently, accurate estimation of flood peaks is crucial. As conventional physically based prediction models reflect the traits of only a small number of areas, applying them in ungauged catchments is challenging. The interrelationship between catchment characteristics and flood features to estimate flood peaks in ungauged areas remains underexplored, and evaluation standards for the appropriate number of flood events to include during data collection to ensure effective flood peak prediction have not been established. Therefore, we developed a machine-learning predictive model for flood peaks in ungauged areas and determined the minimum number of flood events required for effective prediction. We employed rainfall-runoff data and catchment characteristics for estimating flood peaks. The applicability of the machine learning model for ungauged areas was confirmed by the high predictive performance. Even with the addition of rainfall-runoff data from ungauged areas, the predictive performance did not significantly improve when sufficient flood data were used as input data. This criterion could facilitate the determination of the minimum number of flood events for developing adequate flood peak predictive models.

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“…A newly developed machine learning time series algorithm, Prophet, has recently been applied to hydrology problems, such as predicting streamflow from precipitation and temperature data [19]. Machine learning has also been used to develop flood warning systems that can predict the possibility of flooding in the future based on information, such as precipitation levels [20][21][22]. These machine learning prediction techniques can be helpful for stream and risk managers to help prepare for future flooding events.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Bolick

Post

Naser

et al. 2023

Water

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Flooding in urban streams can occur suddenly and cause major environmental and infrastructure destruction. Due to the high amounts of impervious surfaces in urban watersheds, runoff from precipitation events can cause a rapid increase in stream water levels, leading to flooding. With increasing urbanization, it is critical to understand how urban stream channels will respond to precipitation events to prevent catastrophic flooding. This study uses the Prophet time series machine learning algorithm to forecast hourly changes in water level in an urban stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Machine learning was highly accurate in predicting changes in water level for five locations along the stream with R2 values greater than 0.9. Yet, it can be challenging to understand how these water level prediction values will translate to water volume in the stream channel. Therefore, this study collected terrestrial Light Detection and Ranging (LiDAR) data for Hunnicutt Creek to model these areas in 3D to illustrate how the predicted changes in water levels correspond to changes in water levels in the stream channel. The predicted water levels were also used to calculate upstream flood volumes to provide further context for how small changes in the water level correspond to changes in the stream channel. Overall, the methodology determined that the areas of Hunnicutt Creek with more urban impacts experience larger rises in stream levels and greater volumes of upstream water during storm events. Together, this innovative methodology combining machine learning, terrestrial LiDAR, 3D modeling, and volume calculations provides new techniques to understand flood-prone areas in urban stream environments.

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Advancing flood warning procedures in ungauged basins with machine learning

Cited by 22 publications

References 74 publications

Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City

Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City

Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

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