Accounting of Intensity-Duration-Frequency Equations Available in Brazil

Dias, Éverton Costa; Penner, Giovanni Chaves

doi:10.11137/2019_1_209_216

Cited by 4 publications

(3 citation statements)

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“…RadioML [236] Synthetic simulated channel effects and over-the-air recordings CRAWDAD [237] Real-time position data reported by buses SNDZoo [238] Virtualised Network Functions MDC [239] Continuous data pertaining to the behaviour of individuals and social networks QOS-FOG [240] QOS Requirements for FOG Computing Applications TOPO4MEC [241] Network Topologies description Infrastructure VEDAI [242] Detection of engineering vehicles CrackTree [243] Road pavement images CRKWH100 [244] Road pavement images CrackLS315 [244] Road pavement images Stone331 [244] Images of stone surface ZJU SYG crack [245] Crack data set for object detection Surface Crack Detection [246] Images of concrete surfaces with and without crack…”

Section: Communicationmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.INDEX TERMS unmanned aerial vehicle, machine learning, literature review, UAV applications, neural networks.

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Section: Communicationmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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“…In the literature, there is consensus that the determination of the IDF equations should be carried out based on the rainfall sub-daily resolution rain data (pluviographic data) (Abreu, 2018;Nunes et al, 2021;Abreu et al, 2022a). However, sub-daily time series of rainfall data is generally scarce or has short periods of observations (less than 30 years as recommended by the World Meteorological Organization [WMO]), and times with no data records (Teodoro et al, 2014;Dias and Penner, 2019;Abreu et al, 2022b). Furthermore, there is a large amount of records to be tabulated and analyzed for each pluviogram (before using the automatized rain gauge), and it needs a structured process to calculate the series with the maximum precipitation values, associated with the different durations (Abreu, 2018).…”

Section: Introductionmentioning

confidence: 99%

Methodology for IDF equation based on reduced pluviograph records

Penner,

Wendland,

Gonçalves

et al. 2023

Rev. Bras. Ciênc. Ambient.

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In the climate change scenario, extreme rainfall events are increasing in significance and frequency. It is essential to estimate the maximum precipitation intensity for designing hydraulic-hydrological structures, such as macrodrainage. Thus, this study makes a comparison between disaggregation coefficients and forms of the intense rainfall equation to determine an Intensity, Duration and Frequency (IDF) equation for Barcarena-PA. The rainfall historical series available in the Hidroweb database extends between 1981 and 2018. The Gumbel distribution presents the best fit in the return periods: 2, 5, 10, 50, 100, 200 and 1000 years, by the following tests: Filliben, Variance and Kolmogorov-Smirnov. The disaggregation of 1-day precipitation into shorter durations was done in two ways: using disaggregation coefficients recommended by the literature, as well as local disaggregation coefficients. For the construction of the IDF equation, two frequently used representations were considered: the first based on the determination of the coefficients: K, a, b and c; and the second, described in the Pluviometric Atlas of Brazil (APB), determines the coefficients: A, B, C, D and . The results indicated that the use of local disaggregation coefficients, in this case DCBarcarena, with adjustment coefficient R2=0.9945, together with the use of the equation described in the APB, provides the best fit, R2=0.9998, to historical data. When compared with other IDF equations from Barcarena-PA, the previous finding is clear in terms of underestimating the intensity values. Thus, the methodology presented here can be extended to locations with reduced sub-daily rainfall records associated with large annual maximum daily rainfall records.

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“…Intensity-duration-frequency equations are determined using traditional methods based on data from rainfall stations (Martins et al, 2017;Tfwala et al, 2017). In the absence or scarcity of long data series, information from rainfall stations is gathered, and the maximum 1-day rainfall is clipped to shorter rainfall events, thus allowing the fitting of equations (Penner and Lima, 2016;Rangel and Hartwig, 2017;Dias and Penner, 2019). Some studies assess the possibility of using satellite or radar observations to obtain IDF equations (Marra et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Extreme rainfall and IDF equations for Alagoas State, Brazil

2020

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Intensity-duration-frequency (IDF) equations have important applications in several engineering areas such as urban drainage designs, hydrological modeling, and soil conservation projects. This study analyzes the annual maximum series and fits IDF equations for 44 rainfall stations in Alagoas State, Brazil. We adjusted parameters of the Gumbel distribution (GD) and the Generalized Extreme Value (GEV) distribution. The fitting of the observed data to the probability distributions, as well as the selection of the best distribution, were based on the Kolmogorov-Smirnov and Anderson-Darling tests at a 5% significance level. The GEV distribution with parameters obtained by the L-moments method was considered the best in 73% of rainfall stations. The estimated IDF equations showed a good fit, with determination coefficients above 0.991. The maximum rainfall intensities have spatial variation following the climatic zones of the state. The fitted equations allow estimating rainfall intensities from 5 minutes to 24 hours with a return period of 2 to 100 years, and standard error of less than 6.83 mm h-1.

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Accounting of Intensity-Duration-Frequency Equations Available in Brazil

Cited by 4 publications

References 0 publications

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Methodology for IDF equation based on reduced pluviograph records

Extreme rainfall and IDF equations for Alagoas State, Brazil

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