An application of a new extreme value distribution to air pollution data

Hurairah, Ahmed; Ibrahim, Noor Akma; Daud, Isa; Haron, Kassim

doi:10.1108/14777830510574317

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…Extreme values may also be the result of sampling from a highly skewed distribution as observed for example in the case of air pollution in urban areas (Sharma et al 1999;Hurairah et al 2005). Various geostatistical approaches have been developed to deal with these distributions (see e.g.…”

Section: Handling Outliers In the Detection Of Emergenciesmentioning

confidence: 98%

Towards generic real-time mapping algorithms for environmental monitoring and emergency detection

Brenning

Dubois

2007

Stoch Environ Res Risk Assess

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Real-time analysis of data reported by environmental monitoring networks poses a number of challenges, one of which is the conversion of point measurements of phenomena that display some spatial dependence into maps. This is the case for the many variables that cannot be monitored efficiently over large regions by satellites. Environmental pollutants, radiation levels, rainfall fields and seismic activity are but a few of these variables that are usually interpolated for the production of maps. These maps will then further serve as an essential support for decision-making. Ideally, in order to allow real-time assessments and minimize human intervention in case of hazards and emergencies that are frequently linked to the above mentioned variables (e.g. air pollution peaks, nuclear accidents, flash-floods, earthquakes), these maps should be established in near real time and thus automatically. The ability of real-time mapping systems running in the routine mode to be able to cope with extreme events is not straightforward, and few systems are today used automatically for both monitoring the environment and triggering early warnings in case of necessity. Alternatively, adopting a decision-centered view of environmental monitoring and mapping systems allows us to re-formulate their final objective as a classification problem that consists of discriminating routine against emergency conditions, or background information against outliers. It is the purpose of this paper to give an overview of the main challenges for developing and evaluating automatic mapping systems for critical environmental variables, as well as to discuss steps toward the development of generic realtime mapping algorithms.

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Section: Handling Outliers In the Detection Of Emergenciesmentioning

confidence: 98%

Towards generic real-time mapping algorithms for environmental monitoring and emergency detection

Brenning

Dubois

2007

Stoch Environ Res Risk Assess

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“…As it has been widely deployed in many research areas, the EVD is used to represent the distributions of various observations. ese include wind speed and energy data [4,[8][9][10][11][12], wave data prediction [13], data on air pollution [14][15][16][17][18], information and communication technology [19], data on flooding [20], financial risk [3,21], temperature [22], food drying technology [23], and rainfall [24]. It has also been implemented in public health and medical sciences [25,26].…”

Section: Introductionmentioning

confidence: 99%

Extreme Value Distributions: An Overview of Estimation and Simulation

Abdulali

Bakar

Ibrahim

et al. 2022

Journal of Probability and Statistics

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The generalized extreme value distribution (GEVD) and various extreme value distributions are commonly applied in air pollution, telecommunications, operational risk management, finance, insurance, material sciences, economics, and hydrology, among many other industries that deal with extreme events. Extreme value distributions (EVDs) typically limit the distribution of maximum and minimum values for many random observations drawn from the same arbitrary distribution. Besides that, it is a crucial method for forecasting future events and emerged as critical method for predicting future events. As a result, prior research is required to select the best estimation method to obtain a reliable value for the parameters of extreme value distributions. This study provides an overview of three-parameter estimation methods based on goodness-of-fit statistics and root mean square error (RMSE). This paper reviewed and compared three estimation methods used to approximate values of parameters for simulated observations taken from the EVD and GEVD. The method of moments (MOMs), maximum likelihood estimator (MLE), and maximum product of spacing (MPS) were the methods investigated in this study. Our findings indicated that the MPS performed better based on the mean square errors (MSEs); meanwhile, the MPS had similar goodness-of-fit statistic values compared to the MLE.

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“…Coles (2001) provide the necessary theoretical references for EVT as well as various application examples. At present, only a small number of literatures working on distribution fitting with consideration on extreme air quality concentration conducted in Malaysia (Hurairah et al 2005;Yusof et al 2011). Although there is no prior reason to make assumption of the probability distribution of air pollutants concentration, the choice of appropriate statistical distribution models is extremely significant (Jiang et al 2011).…”

Section: Introductionmentioning

confidence: 99%