Real-Time Updating of Rainfall Threshold Curves for Flood Forecasting

Golian, Saeed; Fallahi, Mohammad Reza; Behbahani, Seyyed Mahmoudreza; Sharifi, Soroosh; Sharma, Ashish

doi:10.1061/(asce)he.1943-5584.0001049

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Several papers have discussed the relationship between flood warnings and flood events [7,17,55,56] as shown in Table 3. Table 3 lists all possible combinations in the relationship between flood warnings and flood events.…”

Section: Assessment Criteriamentioning

confidence: 99%

Using Tabu Search Adjusted with Urban Sewer Flood Simulation to Improve Pluvial Flood Warning via Rainfall Thresholds

Liao

Pan

Chang

et al. 2019

Water

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Pluvial floods are the most frequent natural hazard impacting urban cities because of extreme rainfall intensity within short duration. Owing to the complex interaction between rainfall, drainage systems and overland flow, pluvial flood warning poses a challenge for many metropolises. Although physical-based flood inundation models could identify inundated locations, hydrodynamic modeling is limited in terms of computational costs and sophisticated calibration. Thus, herein, a quick pluvial flood warning system using rainfall thresholds for central Taipei is developed. A tabu search algorithm is implemented with hydrological-analysis-based initial boundary conditions to optimize rainfall thresholds. Furthermore, a cross test is adopted to evaluate the effect of each rainfall event on rainfall threshold optimization. Urban sewer flood is simulated via hydrodynamic modeling with calibration using crowdsourced data. The locations and time of occurrence of pluvial floods can be obtained to increase the quality of observed data that dominate the accuracy of pluvial flood warning when using rainfall thresholds. The optimization process is a tabu search based on flood reports and observed data for six flood-prone districts in central Taipei. The results show that optimum rainfall thresholds can be efficiently determined through tabu search and the accuracy of the issued flood warnings can be significantly improved.

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Section: Assessment Criteriamentioning

confidence: 99%

Using Tabu Search Adjusted with Urban Sewer Flood Simulation to Improve Pluvial Flood Warning via Rainfall Thresholds

Liao

Pan

Chang

et al. 2019

Water

View full text Add to dashboard Cite

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Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China

Tian

Bricker

et al. 2020

Advances in Water Resources

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Forecasting of water quality parameters of Sandia station in Narmada basin, Central India, using AI techniques

Tiwari,

Singh,

Kumar

2024

Journal of Water and Climate Change

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In addition to the influence of climate change on water availability and hydrological risks, the effects on water quality are in the early stages of investigations. This study aims to consolidate the latest interdisciplinary research in the application of artificial intelligence (AI) in the field of assessment of water quality parameters and its prediction. This research paper specifically explores the intricate relationship between climate change and water quality parameters at Sandia station, situated within the Narmada basin in Central India. As global climatic patterns continue to shift, the repercussions on water resources have gained prominence. In this work, electrical conductivity is predicted using the KERAS data processing environment on TensorFlow. The root mean square error (RMSE), coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), etc. are calculated between observed and predicted values to assess the model performance. A total of 10 models are developed depending upon the input geometry from past monthly timelines. The results indicate that Model no. 8, with 10 inputs performs the best based on the R2 value of 0.889. These results indicate that AI can be very helpful in analyzing the possible threats in the future for drinking, water, livestock feeding, irrigation, and so on.

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Real-Time Updating of Rainfall Threshold Curves for Flood Forecasting

Cited by 3 publications

References 25 publications

Using Tabu Search Adjusted with Urban Sewer Flood Simulation to Improve Pluvial Flood Warning via Rainfall Thresholds

Using Tabu Search Adjusted with Urban Sewer Flood Simulation to Improve Pluvial Flood Warning via Rainfall Thresholds

Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China

Forecasting of water quality parameters of Sandia station in Narmada basin, Central India, using AI techniques

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