Comparison of estimation techniques for generalised extreme value (GEV) distribution parameters: a case study with Tasmanian rainfall

Hossain, Iqbal; Khastagir, Anirban; Aktar, M. N.; Imteaz, Monzur Alam; Huda, Durul; Rasel, H. M.

doi:10.1007/s13762-021-03693-5

Cited by 5 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vicente-Serrano compared the fitting effects of the Log-logistic, Pearson, Log-normal, and Generalized Extreme Value on the series. The results showed that the Log-logistic distribution has the best fitting effect on the series, and the estimation method of the fitting parameters uses the linear moment method [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…Vicente-Serrano compared the fitting effects of the Log-logistic, Pearson, Log-normal, and Generalized Extreme Value on the D k n series. The results showed that the Log-logistic distribution has the best fitting effect on the D k n series, and the estimation method of the fitting parameters uses the linear moment method [35]. Using the three-parameter log-logistic probability distribution to normalize the D i data series, calculate the SPEI index corresponding to each value:…”

Section: Plos Onementioning

confidence: 99%

See 1 more Smart Citation

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Yao,

Wu,

Liu

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Yao,

Wu,

Liu

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The importance of hydro-climate estimates is demonstrated in the large number of scientific papers that have been published recently aimed at the improvement of probabilistic estimates [2][3][4][5]. Comparative studies of parameter estimation methods have been widely used to analyse hydro-climate extremes.…”

Section: Introductionmentioning

confidence: 99%

Maximum Extreme Flow Estimations in Historical Hydrological Series under the Influence of Decadal Variations

et al. 2022

View full text Add to dashboard Cite

The hypothesis of stationarity is a fundamental condition for the application of the statistical theory of extreme values, especially for climate variables. Decadal-scale fluctuations commonly affect maximum and minimum river discharges. Thus, the probability estimates of extreme events need to be considered to enable the selection of most appropriate time series. The current study proposed a methodology to detect the fluctuation of long wet and dry periods. The study was carried out at the gauging station 4C-001 in Pardo River, State of São Paulo, Brazil. The Spearman, Mann–Kendall and Pettitt’s non-parametric tests were also performed to verify the existence of a temporal trend in the maximum annual daily flows. The graph achieved from the Pettitt’s statistical variable allowed for the identification and separation of the longest dry period (1941 to 1975) and the longest wet period (1976 to 2011), decreasing again in 2012. Analysing the series separately, it was observed that both mean and standard deviation were higher than those corresponding to the dry period. The probable maximum flows for the corrected series showed estimates 10% higher than those estimated for the uncorrected historical series. The proposed methodology provided more realistic estimates for the extreme maximum flows.

show abstract

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data

Singh,

Ali,

Haque

2024

Accident Analysis & Prevention

View full text Add to dashboard Cite

Comparison of estimation techniques for generalised extreme value (GEV) distribution parameters: a case study with Tasmanian rainfall

Cited by 5 publications

References 45 publications

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Maximum Extreme Flow Estimations in Historical Hydrological Series under the Influence of Decadal Variations

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data

Contact Info

Product

Resources

About