Study on Effect of Open Space in Reducing Heat lsland by Presuming the Temperature

Irie, Teruaki

doi:10.5632/jila.66.889

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Many studies on mitigating UHIP have been studied using relationship between intensity of UHIP and particular meteorological condition. Bio-greening (Ichinose et al, 2006;Irie, 2003;Kagawa et al, 1998;Lin et al, 2008;Narita et al, 2004a;Solecki et al, 2005;Spronken-Smith et al, 2000;Takebayashi and Moriyama, 2007;Wong et al, 2007;Wong and Yu, 2005) can be an effective method for reducing UHIP and cooling loads because plants absorb large amounts of heat for evapotranspiration during the daytime and inhibit temperature increase of the surrounding air.…”

Section: Introductionmentioning

confidence: 99%

Optimal Environmental Condition for Moss production in plant factory system

Park

Murase

2011

IFAC Proceedings Volumes

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Section: Introductionmentioning

confidence: 99%

Optimal Environmental Condition for Moss production in plant factory system

Park

Murase

2011

IFAC Proceedings Volumes

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“…However, the degree of influence should be supposed changeable and dependent on the geographical situation, as has been discussed in a previous research (Irie, 2003). Thus, building a model that can accommodate the changeability of the parameters and coefficients in relation to the geographic situation is necessary.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Evaluating Influences of Anthropogenic Factors on Temperature Formation of Summer Seasons Considering their Randomness -Verification Through the Hierarchical Bayesian Model-

Kyakuno

2016

Journal of Asian Architecture and Building Engineering

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A new regression model that considers random nature is proposed for describing summer air temperatures in urban areas through the concept of an empirical hierarchical Bayesian model. In this model, the regression coefficients of explanatory variables are regarded as having random effects because of their locations, whereas ordinal regression models do not allow such randomness. Data of air temperature, the climatic situation, and geographically related factors in the Kinki area were collected and compiled by GIS, and then applied to both the new model and the standard regression models. The performances of the models were compared and their differences were discussed in relation to the geographic or climatic situations. It was established that the proposed model succeeded in accounting for the random nature and it predicted air temperature more accurately than the standard models based on the MCMC simulation. Furthermore, the confidence intervals of the values predicted by the new model became more rigid than for the ordinal regression models. This means that the new model is capable of presenting a more accurate picture without affecting the convenience of the ordinary regression models.

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