Predicting air pollution using fuzzy genetic linear membership kriging in GIS

Shad, Rouzbeh; Mesgari, Mehran; Abkar, Ali; Shad, Arefeh

doi:10.1016/j.compenvurbsys.2009.10.004

Cited by 68 publications

(36 citation statements)

References 16 publications

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“…This can be confirmed by the existing rules in Tehran to confront the air pollution problems, in this case, the Area Traffic Plan that exists for the central parts of Tehran. In addition, (Shad et al 2009) confirms that air pollutants concentrate in the central and eastern parts of Tehran. Second, regarding the temporal distribution, it can be seen that in the first time slots of the series, there are relatively few patterns.…”

Section: Discussionmentioning

confidence: 56%

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A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

2015

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Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting cooccurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus, it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance. Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting co-occurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance.

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Section: Discussionmentioning

confidence: 56%

“…Tehran to the eastern parts; however, due to the presence of mountains, they remain blocked in the eastern parts, increasing the air pollution in that area, as found by (Shad et al 2009). Repeatedly, this confirms the validity of this approach.…”

Section: Discussionmentioning

confidence: 89%

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

2015

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“…They constructed maps showing environmental characteristics, prevalence of S. mansoni and its snail host in order to study the spatial and temporal dynamics of the infection thereby providing an indication of the environmental factors that influence the distribution of schistosomiasis Geostatistical techniques such as Kriging (Isaaks and Srivastava, 1989) and MaxEnt (Elith et al, 2011) were initially applied in the field of mining and assessment of mineral resources, and later for studies of plant and animal ecology. Kriging is a spatial estimation technique that can be used to build probabilistic models for values not sampled, allowing inferences from observed values (Felgueiras, 1999;Shad et al, 2009). Indicator Kriging for categorical data takes into consideration the uncertainties associated with the data and methods involved in modelling, thus enabling creation of maps of snails spatial distributions along with the corresponding uncertainties (Felgueiras, 1999).…”

Section: Introductionmentioning

confidence: 99%

Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil

Barbosa¹,

Guimarães²,

Loyo³

et al. 2016

Geospat Health

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The occurrence of schistosomiasis is directly linked to the presence of its snail intermediate host Biomphalaria spp. Knowledge of geographical distribution, habitats and behaviour of these snails in relation to the climate is essential for guiding measures for disease prevention and control. This study aims to model the distribution of B. glabrata and B. straminea in schistosomiasis non-endemic areas of the metropolitan region of Recife (MRR) based on environmental data and estimates of snail distributions in endemic and neighbouring areas. We applied Kriging with the aim of determining the spatial distribution of these two snail species and MaxEnt for modelling their ecological behaviour. Kriging showed that the North and the Centre of the MRR were generally either snail-free or contained only B. straminea, while both snail species could be found in the South. MaxEnt supported our observation that the northern and southern coastal regions were favoured by B. glabrata and diurnal mean temperature variation; July rainfall and November rainfall were the three variables favouring Biomphalaria breeding sites that contributed the most in the predictive model we developed. The study showed the location of areas suitable to Biomphalaria spp. and therefore at potential risk, first for invasion of these snails and later for the development of new schistosomiasis-endemic areas. This information should be useful, not only to estimate expansion possibilities of this disease in the MRR, but also to point out the climatic variables that would contribute to this expansion, thereby allowing timely application of prevention and control measures.

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“…The statistical forecast method is based on the relationship between a set of meteorological predictors and the values of the concentrations at different monitoring stations (Vautard et al, 2001). Statistical forecasts use a variety of mathematical models such as linear or nonlinear regression (Cobourn and Hubbard, 1999;Cobourn, 2007), artificial neural network (Abdul-Wahab et al, 2002;Nejadkoorki et al, 2012;Antanasijevic et al, 2013), and fuzzy logic (Shad et al, 2009). Yetilmezsoy et al (2012) proposed a prognostic approach, which is based on a fuzzylogic model, to estimate suspended dust concentrations related to PM 10 in a specific residential area in Kuwait with high traffic and industrial influences.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach to Forecast Air Quality during High-PM Concentration Pollution Period

Chen

et al. 2015

Aerosol Air Qual. Res.

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In this study, a hybrid approach of combining numerical prediction with statistical analysis was proposed to forecast high-PM 10 (aerosol particle with aerodynamic diameter less than 10 μm) concentration events in Beijing, China. This approach was used to forecast the daily PM 10 in Beijing from January 1 to December 30, 2013. The WRF-CMAQ modeling system was also applied to simulate Beijing's PM 10 in the same period. The performance of the two methods was then assessed according to the mean bias (MB), normalized mean bias (NMB), normalized mean gross error (NME), mean normalized bias (MNB), mean normalized gross error (MNE), and root mean square error (RMSE). The results demonstrate that both methods perform well during low-PM 10 concentration periods (PM 10 concentration < 250 μg/m 3 ), the MB, NMB, NME, MNB, MNE and RMSE for hybrid approach during low-PM 10 concentration periods were 26.15, 24.88%, 41.94%, 43.23%, 56.35% and 61.67, respectively. The MB, NMB, NME, MNB, MNE and RMSE for CMAQ during low-PM 10 concentration periods were -6.04, 57.47%, 41.49%, 21.52%, 55.64% and 60.11, respectively. While the MB, NMB, NME, MNB, MNE and RMSE for CMAQ during high-PM 10 concentration periods (PM 10 concentration ≥ 250 μg/m 3 ) were -162.87, -50.37%, 50.37%, -49.86%, 49.86% and 175.93, respectively. The MB, NMB, NME, MNB, MNE and RMSE for hybrid approach during high-PM 10 concentration periods were -30.3, -9.37%, 23.21%, -8.21%, 24.25% and 97.37, respectively. The hybrid approach shows significant improvement in accuracy during high-PM 10 concentration periods.

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Predicting air pollution using fuzzy genetic linear membership kriging in GIS

Cited by 68 publications

References 16 publications

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil

A Hybrid Approach to Forecast Air Quality during High-PM Concentration Pollution Period

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