Urban flash flood index based on historical rainfall events

Mohtar, Wan Hanna Melini Wan; Abdullah, Jazuri; Maulud, Khairul Nizam Abdul; Muhammad, Nur Shazwani

doi:10.1016/j.scs.2020.102088

Cited by 48 publications

(17 citation statements)

References 26 publications

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“…Studies in recent years have demonstrated that rainfall characteristics, such as rainfall, rainfall duration, peak rainfall, position coefficient, rainfall intensity variance, and peak multiple, have different effects on water accumulation [54], [55]. However, for fixed water accumulation points, an important factor that affects the depth of water accumulation at a certain moment in the water accumulation process is the location of early larger rainfall intensity.…”

Section: Selection Of Sensitivity Index For Depth Prediction Of Acmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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Section: Selection Of Sensitivity Index For Depth Prediction Of Acmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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“…The evaluation of the problems associated with floods has typically been fundamentally approached from a hydrological perspective [1,2], although several points of view can be found in the scientific literature. However, in the field of flood risk management, apart from traditional methodological approaches based on the simulation of flood models [3,4], there are other perspectives based on multicriteria socioeconomic assessments [5], statistical rainfall indicators [6], or several different natural resilience strategies [7][8][9][10], for example.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical Analysis of the Spatial Correlation between Territorial Anthropization and Flooding Vulnerability: Application to the DANA Phenomenon in a Mediterranean Watershed

García-Ayllón

Radke

2021

Applied Sciences

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Climate change is making intense DANA (depresión aislada en niveles altos) type rains a more frequent phenomenon in Mediterranean basins. This trend, combined with the transformation of the territory derived from diffuse anthropization processes, has created an explosive cocktail for many coastal towns due to flooding events. To evaluate this problem and the impact of its main guiding parameters, a geostatistical analysis of the territory based on GIS indicators and an NDVI (Normalized Difference Vegetation Index) analysis is developed. The assessment of the validity of a proposed methodology is applied to the case study of the Campo de Cartagena watershed located around the Mar Menor, a Mediterranean coastal lagoon in Southeastern Spain. This area has suffered three catastrophic floods derived from the DANA phenomenon between 2016 and 2019. The results show that apart from the effects derived from climate change, the real issue that amplifies the damage caused by floods is the diffuse anthropization process in the area, which has caused the loss of the natural hydrographic network that traditionally existed in the basin.

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“…Numerous studies [1][2][3] have suggested that the occurrence of flooding in urban areas is likely to increase in the future due to increased urbanisation and changes in precipitation patterns, making intense rainfall events and the inundation of local drainage systems more common. The majority of urban flooding hazard studies focus on the economic damage, or direct risks to the public derived from hydraulic modelling of the depth and velocity of floodwaters resulting from historic or design rainfall events (see, e.g., in [4,5]). However, an increasing number of studies have also considered the public health risks of exposure to flood water, which may take the form of long term mental impacts [6], or illness from direct exposure of the public to contaminated flood water.…”

Section: Introductionmentioning

confidence: 99%

CFD Modelling of the Transport of Soluble Pollutants from Sewer Networks to Surface Flows during Urban Flood Events

Beg

Rubinato

Carvalho

et al. 2020

Water

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Surcharging urban drainage systems are a potential source of pathogenic contamination of floodwater. While a number of previous studies have investigated net sewer to surface hydraulic flow rates through manholes and gullies during flood events, an understanding of how pollutants move from sewer networks to surface flood water is currently lacking. This paper presents a 3D CFD model to quantify flow and solute mass exchange through hydraulic structures featuring complex interacting pipe and surface flows commonly associated with urban flood events. The model is compared against experimental datasets from a large-scale physical model designed to study pipe/surface interactions during flood simulations. Results show that the CFD model accurately describes pipe to surface flow partition and solute transport processes through the manhole in the experimental setup. After validation, the model is used to elucidate key timescales which describe mass flow rates entering surface flows from pipe networks. Numerical experiments show that following arrival of a well-mixed solute at the exchange structure, solute mass exchange to the surface grows asymptotically to a value equivalent to the ratio of flow partition, with associated timescales a function of the flow conditions and diffusive transport inside the manhole.

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Urban flash flood index based on historical rainfall events

Cited by 48 publications

References 26 publications

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Geostatistical Analysis of the Spatial Correlation between Territorial Anthropization and Flooding Vulnerability: Application to the DANA Phenomenon in a Mediterranean Watershed

CFD Modelling of the Transport of Soluble Pollutants from Sewer Networks to Surface Flows during Urban Flood Events

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