Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning

Liu, Yuan-Yuan; Lei, Li; Liu, Yesen; Chan, Pak Wai; Zhang, Wenhai

doi:10.1016/j.atmosres.2020.104861

Cited by 45 publications

(22 citation statements)

References 20 publications

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“…It is located in the coastal area, and has a subtropical maritime climate with concentrated precipitation and frequent short-term rainstorms. It has an advanced economy and large population but immature drainage systems [34]. Due to the poor geographic factors and drainage infrastructure, Gongming suffers severe flooding in the rainy season every year and the urban resilience requires much improvement [35,36].…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth

Chen

Yang

et al. 2020

Sustainability

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Urban flooding now occurs frequently and low impact development (LID) has been widely implemented as an effective resilience strategy to improve storm water management. This study constructed the inundation curve to dynamically simulate the disaster, and established an inundation severity indicator (ISI) and cost-effectiveness indicator (CEI) to quantify the severity and cost effectiveness at each site. The study set 10 different density scenarios using a zonal approach. The results showed that LID could reduce the overall ISI value, but as the construction increased, the CEI exhibited a downward trend, showing that there is a marginal utility problem in LID. However, the performance of CEI differed slightly in areas of different severity. In the vulnerable resilience zone, the CEI increased initially and then decreased, and the optimal cost–benefit combination was 60% permeable pavement +20% green roof +50% vegetative swale. The mutual effects of LID measures in different zones led to synergistic or antagonistic effects on LID. This study explored the tradeoff between the resilience enhancement effect and strategy transformation cost, and determined the optimal combination of the LID strategy, thereby providing a new analytical perspective for the sustainable development of sponge cities.

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Section: Study Area and Data Collectionmentioning

confidence: 99%

Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth

Chen

Yang

et al. 2020

Sustainability

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“…Case study area of Shenzhen city in China. The typical rainy city of Shenzhen in China, with 4 urban rivers, is selected as the study area 29 , this city has complete hydrological infrastructure and observation information systems.Shenzhen, located in the southeast coast of China, is the rapidest urbanization city in China with high urban flood risk caused by extreme rainfall. Shenzhen city ranks fifth among the 136 costal cites in the world in terms of future flood loss risk 30 .…”

Section: Datamentioning

confidence: 99%

The impact of RMD on urban runoff cannot be ignored in urban hydrologic management

Liu¹,

Huang²,

Liu³

2021

Preprint

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Urban floods have been exacerbated globally, associated with increasing spatial-temporal variations of rainfall. However, compared with rainfall variabilities of intensity and duration, the effect of RMD is always ignored. Based on 1313 rainfall scenarios with different combinations of rainfall intensity(RI) and RMD in a typical rainy city of Shenzhen in China, we investigate the urban runoff response in relation to RMD and the spatial feature of rivers. Our results show that the effect of RMD on the peak runoff may reach up to 20%, which will decrease to less than 5% under heavy RI conditions. In addition, we find that the impact of RMD is almost symmetrical and is associated with the direction of the river. The closer RMD is to the Linear Directional Mean of upstream, the larger peak runoff of section is. Our results reveal that RMD is significant to urban peak runoff in the downstream reaches, which should be considered in urban hydrologic model.

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“…Hence, various data-driven intelligence models, such as artificial neural networks such as the Elman neural networks [15,16] and the nonlinear autoregressive network with exogenous inputs (NARX) network [17], support vector machines (SVM) [18] and deep learning involving the Random Forest [19,20] and gradient-boosting decision tree (GBDT) [21] have been widely applied for urban flood risk assessment and prediction in recent years with satisfactory results. Urban flood prediction mainly includes three flood characteristics-the duration of the flood, the area affected by flooding, and the depth of the flood [22]. In actual urban flood management, we can actively respond to emergencies and greatly reduce the negative impact of flooding if we can determine the maximum depth of urban flooding during a rainfall event.…”

Section: Introductionmentioning

confidence: 99%

Using Multi-Factor Analysis to Predict Urban Flood Depth Based on Naive Bayes

Wang

et al. 2021

Water

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With global warming, the number of extreme weather events will increase. This scenario, combined with accelerating urbanization, increases the likelihood of urban flooding. Therefore, it is necessary to predict the characteristics of flooded areas caused by rainstorms, especially the flood depth. We applied the Naive Bayes theory to construct a model (NB model) to predict urban flood depth here in Zhengzhou. The model used 11 factors that affect the extent of flooding—rainfall, duration of rainfall, peak rainfall, the proportion of roads, woodlands, grasslands, water bodies and building, permeability, catchment area, and slope. The forecast depth of flooding from the NB model under different rainfall conditions was used to draw an urban inundation map by ArcGIS software. The results show that the probability and degree of urban flooding in Zhengzhou increases significantly after a return period of once every two years, and the flooded areas mainly occurred in older urban areas. The average root mean square error of prediction results was 0.062, which verifies the applicability and validity of our model in the depth prediction of urban floods. Our findings suggest the NB model as a feasible approach to predict urban flood depth.

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Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning

Cited by 45 publications

References 20 publications

Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth

Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth

The impact of RMD on urban runoff cannot be ignored in urban hydrologic management

Using Multi-Factor Analysis to Predict Urban Flood Depth Based on Naive Bayes

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