Application of oracle 7.3 system for the development of an environmental database

Kumar, Ashok; Sud, A. M.; Heydinger, Andrew G.

doi:10.1002/ep.670170308

Cited by 10 publications

(9 citation statements)

References 2 publications

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“…Initially, 90% of the given data is selected as the training data and the remaining 10% as the validation data. In this study, the training and validation data have been transformed to [21,11] in order to avoid asymptotic effect arising from the sigmoid activation function used in the ANN model [42]. The number of hidden neurons in the conventional ANN model is fixed at 10.…”

Section: Resultsmentioning

confidence: 99%

Correction Model‐BasedANNModeling Approach for the Estimation of Radon Concentrations inOhio

Yerrabolu

Mareddy

Bhatt

et al. 2012

Env Prog and Sustain Energy

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According to National Cancer Institute, radon is one of the major causes for lung cancer related deaths after smoking in US. To prevent deaths due to radon inhalation there is a need to determine the level of radon concentration in each locality, for example, zip code and this would help ease the identification of areas with high radon concentration thereby allowing the necessary preventive measures to be taken. However, factors like inapproachability hinder the process of estimating radon concentration in some places. In such places it is a common practice to estimate the radon concentrations using several interpolation techniques. In this article, a new approach that improves the accuracy of the neural model with the help of sensitivity‐based correction model for modeling and estimating radon concentrations in Ohio is proposed. The results are compared with commonly used techniques such as kriging, radial basis function (RBF), inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), and the recently developed conventional ANN modeling approach. Further, model accuracies of all the above models are evaluated based on Willmott's Index and the ranked performance measures criteria with emphasis on the extreme‐end (peak‐end, low‐end), and mid‐range radon concentrations. The results demonstrate the effectiveness of the proposed approach in estimating the radon concentrations with the percentage improvement of 70–80% prediction accuracy, compared to the other techniques. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 1223–1233, 2013

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Section: Resultsmentioning

confidence: 99%

Correction Model‐BasedANNModeling Approach for the Estimation of Radon Concentrations inOhio

Yerrabolu

Mareddy

Bhatt

et al. 2012

Env Prog and Sustain Energy

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“…The "Homes" section, the most extensive of all the sections of the databases, began as an ORACLE/MS Access database [10]. It is currently handled by SQL Server 7.0/MS Access, as MS Access on its own is incapable of handling such a large database [11].…”

Section: Radon Gas Databasesmentioning

confidence: 99%

Management of Radon Data in the State of Ohio, U.S.A.

Kumar¹

2011

TOEBMJ

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Abstract:The objective of this research paper is to create a public awareness in understanding the radon gas problem, discuss efficient data management methods and analyses of indoor radon gas concentrations, and mitigate the indoor radon gas concentrations to below acceptable level in the state of Ohio. The Ohio Radon Information System (ORIS) is a compendium of five databases or modules, viz. the "Homes" database, the "Water" database, the "Schools" database, the "Mitigation" database, and the "Tester" database. Over the years, the radon database (made available by testing laboratories, Ohio Department of Health (ODH), Ohio Environmental Protection Agency (EPA), and universities) has been expanded to 145,849 radon gas observations for homes; 1,283 radon gas observations for drinking water; 1,147 radon gas observations for schools; 26,374 radon gas observations for mitigation systems; and 76,727 radon gas observations by testers. Analysis of the homes data shows that "Licking County" is the only county having a geometric mean (GM = 8.41 pCi/l) of radon gas concentration greater than 8 pCi/l in Ohio. Zip code 43930, in "Jefferson County," accounts for a maximum GM radon gas concentration of 39 pCi/l. Of the 216 public and 1,067 private water supply systems, only 2 public and 65 private water supply systems have shown radon gas concentrations to exceed 1000 pCi/l. Analysis of the schools data reveals that a school in "Belmont County" to show a maximum radon gas concentration of 85.5 pCi/l, with 11 rooms out of the 39 school rooms tested greater than 4 pCi/l. The performance analysis of the mitigation systems identified the Sub Slab Depressurization (SSD) systems to be the best performers in mitigating the radon gas concentration to below 4 pCi/l in Ohio. Analysis of the tester's data showed two counties: Harrison (GM = 10.25 pCi/l); and Perry (GM = 8.65 pCi/l) to have a GM of radon gas concentration greater than 8 pCi/l in Ohio.

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“…u t o l e d o . e d / ~ a p rg / o r i s /) [6][7][8][9]. ORIS draws its i n f o rmation from a database, compiled in Oracle, that includes indoor radon levels for almost 93,875 buildings across Ohio, most of which are private houses.…”

Section: The Radon Dat a B A S Ementioning

confidence: 99%

Experience in integrating geographical information systems (GIS) to an indoor radon database

2001

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Application of oracle 7.3 system for the development of an environmental database

Cited by 10 publications

References 2 publications

Correction Model‐BasedANNModeling Approach for the Estimation of Radon Concentrations inOhio

Correction Model‐BasedANNModeling Approach for the Estimation of Radon Concentrations inOhio

Management of Radon Data in the State of Ohio, U.S.A.

Experience in integrating geographical information systems (GIS) to an indoor radon database

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