Analysis of typhoon wind hazard in Shenzhen City by Monte-Carlo Simulation

Guo, Yunxia; Hou, Yijun; Qi, Peipei

doi:10.1007/s00343-019-8231-9

Cited by 11 publications

(19 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jérôme et al used the MC method to find a high-performance working fluid with an uncertain property [26]. Guo et al obtained, by the MC, a great deal of expanded typhoon data for analyzing wind hazards and optimizing wind field models [27]. Therefore, this paper proposes the ELM combined with the MC method to predict and analyze dyeing time with a small number of samples.…”

Section: Related Workmentioning

confidence: 99%

Seepage Time Soft Sensor Model of Nonwoven Fabric Based on the Extreme Learning Machine Integrating Monte Carlo

Zhang

Fan

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

Nonwoven fiber materials are materials with multifunctional purposes, and are widely used to make masks for preventing the new Coronavirus Disease 2019. Because of the complexity and particularity of their structure, it becomes difficult to model the penetration and flow characteristics of liquid in nonwoven fiber materials. In this paper, a novel seepage time soft sensor model of nonwoven fabric, based on Monte Carlo (MC), integrating extreme learning machine (ELM) (MCELM) is proposed. The Monte Carlo method is used to expand data samples. Then, an ELM method is used to establish the prediction model of the dyeing time of the nonwoven fiber material overlaps with the porous medium, as well as the insertion degree and height of the different quantity of hides. Compared with the back propagation (BP) neural network and radial basis function (RBF) neural network, the results show that the prediction model based on the MCELM method has significant power in terms of accuracy and prediction speed, which is conducive to the precise and rapid manufacture of nonwoven fiber materials in practical applications between liquid seepage characteristics and structural characteristics of porous media. Furthermore, the relationship between the proposed models has certain value for predicting the behavior and use of nonwoven fiber materials with different structural characteristics and related research processes.

show abstract

Section: Related Workmentioning

confidence: 99%

Seepage Time Soft Sensor Model of Nonwoven Fabric Based on the Extreme Learning Machine Integrating Monte Carlo

Zhang

Fan

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…They statistically determined the average and standard deviation of the maximum storm wind speed radius and validated the impact on storm surge deviation along the Japanese coast by comparing it with observed values. For example, in analyzing the maximum storm wind speed radius (R m ) and Holland-B parameters, [8] employed a typhoon model to examine the maximum wind speed radius and Holland-B parameters, validating the analysis with observational data to explore the optimal parameter values for Shenzhen City. Reference [9] focused on the Zhoushan Archipelago, compiling previous studies on the maximum storm wind speed radius and establishing a typhoon model.…”

Section: Introductionmentioning

confidence: 99%

“…q m = 0.9 3.802 p 0 + ∆p 0 3.7 e 17.67Ts 243.5+Ts (15) where R = 286.9 J (kg • K) is the gas constant of dry air, T vs is the virtual surface temperature, T s is the surface air temperature, q m is the vapor pressure at 90% relative humidity, and φ is the absolute latitude value. This study refers to the research results of [8,9], which use 9 types of R m and 7 kinds of Holland-B parameters; a total of 63 different combinations were analyzed. These combinations are described below.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Study on the Application of Typhoon Experience Parameter Analysis in Taiwan’s Offshore Wind Farms

Fang

Hsu

Wang³

2023

Water

View full text Add to dashboard Cite

Due to the rapid development of computers, researchers have made efforts since the 1990s to develop typhoon forecasting models and stochastic typhoon simulation models to assess typhoon disasters and risks. Typhoon forecasting models are primarily used to predict and track the movement of typhoons and provide warning information to the general public before landfall. Stochastic typhoon simulation models can assess extreme wind speeds and compensate for the limitations of current observations and simulation data length. Taiwan experiences approximately three to four typhoons yearly, of varying intensities and paths. Whether the marine meteorological data includes events of strong typhoon centers passing through will affect the results of frequency analysis. The development of offshore wind power in Taiwan is closely related to the unique marine meteorological conditions throughout the lifecycle stages, including wind farm site selection, feasibility studies, planning and design, construction and installation, operation and maintenance, and decommissioning. This study references relevant research and analyzes sixty-three scenarios using nine types of maximum storm wind speed radii and seven Holland-B parameters. The data from Japan Meteorological Agency Best Track Data (JMA BTD) is utilized, explicitly selecting 20 typhoon events after 2000 for wind speed simulation using a typhoon wind speed model. After validating the typhoon wind speeds with observation data from the Central Weather Bureau (CWB) in Hsinchu and the Longdong buoy, the technique of Monte Carlo simulation is utilized to generate synthetic typhoons randomly. The average of the relative absolute errors for the simulated maximum wind speeds is calculated, and through comprehensive evaluation, optimal parameter combinations (Rm, B) are obtained.

show abstract

“…Qian et al (2019) used hourly real-time ocean station observation data to investigate quality control techniques and methods for determining the most suitable quality control approach for the data collected at the Xiaomaidao observation station in Qingdao, China. The typhoon wind hazard risk for the city of Shenzhen was analyzed using a Monte-Carlo simulation and the YM wind fi eld model (Guo et al, 2019). Liu et al (2019b) investigated the distribution and zoning of risks from sea level rise across Shandong Province and conducted an assessment of those risks.…”

mentioning

confidence: 99%