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Journal articles on the topic 'Data environmental analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Kulla, L., and R. Marušák. "Environmental risk assessment based on semi-quantitative analysis of forest management data." Journal of Forest Science 57, No. 3 (March 21, 2011): 89–95. http://dx.doi.org/10.17221/35/2010-jfs.

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The paper deals with environmental risk assessment in prevailingly unnatural spruce (Picea abies [L.] Karst.) forests in three regions with different patterns of forest damage in the Slovak part of the West Carpathians. Logistic regression was used to estimate the effect of 7 site-related, 5 stand-related and 2 anthropogenic factors on the probability that critical forest damage will occur. The results show that regression models can describe cause-effect relationships in regions with different regimes of forest decline. Stand age, proportion of spruce, and distance from the focus of biotic agent activity predicted decline in two regions with generally lower elevation in northern Slovakia (Kysuce and Orava). In a mountain region (Low Tatras), the importance of factors contributing to the static stability of trees and position towards dangerous winds increased significantly. The quality of the derived models and prospects for their usefulness in risk assessment are discussed.

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2

Rozental, O. M., and L. N. Alexandrovskaya. "Data Analysis in Environmental Management." Ecology and Industry of Russia 22, no. 3 (April 4, 2018): 56–59. http://dx.doi.org/10.18412/1816-0395-2018-3-56-59.

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3

Lesser, Virginia M., and Wayne R. Ott. "Environmental Statistics and Data Analysis." American Statistician 49, no. 4 (November 1995): 398. http://dx.doi.org/10.2307/2684587.

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4

Rathbun, Stephen L., and Wayne Ott. "Environmental Statistics and Data Analysis." Journal of the American Statistical Association 93, no. 441 (March 1998): 402. http://dx.doi.org/10.2307/2669643.

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5

Hamilton, Martin A. "Environmental Statistics and Data Analysis." Technometrics 38, no. 3 (August 1996): 292–93. http://dx.doi.org/10.1080/00401706.1996.10484518.

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6

O'Brien, Robert. "Environmental Statistics and Data Analysis." Journal of Quality Technology 30, no. 1 (January 1998): 105–6. http://dx.doi.org/10.1080/00224065.1998.11979827.

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7

Sayers, William T. "Environmental statistics and data analysis." Atmospheric Environment 30, no. 20 (October 1996): 3551. http://dx.doi.org/10.1016/1352-2310(96)00053-2.

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8

G., Kaziyeva, Esekeeva M., Sagnayevа S., Sembina G., and Abzhanova A. "Environmental Monitoring Data Analysis OLAP Toolplatform." BULLETIN of L.N. Gumilyov Eurasian National University. Technical Science and Technology Series 131, no. 2 (2020): 66–77. http://dx.doi.org/10.32523/2616-68-36-2020-131-2-66-77.

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9

Yuan, Lina, Huajun Chen, and Jing Gong. "An analysis of environmental data transmission." IOP Conference Series: Earth and Environmental Science 64 (May 2017): 012058. http://dx.doi.org/10.1088/1755-1315/64/1/012058.

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10

Esterby, S. R. "Trend analysis methods for environmental data." Environmetrics 4, no. 4 (December 1993): 459–81. http://dx.doi.org/10.1002/env.3170040407.

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11

Zafar, Mohammad. "Computer applications in environmental data analysis." Environmental Quality Management 13, no. 4 (2004): 71–81. http://dx.doi.org/10.1002/tqem.20017.

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12

HejcÌ, M., and J. HrÌebÃcÌek. "Data uncertainty analysis in environmental management." PAMM 7, no. 1 (December 2007): 2010025–26. http://dx.doi.org/10.1002/pamm.200700892.

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13

Hasselblad, Vic. "Meta-analysis of environmental health data." Science of The Total Environment 160-161 (January 1995): 545–58. http://dx.doi.org/10.1016/0048-9697(95)04389-i.

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14

Harter, Thomas. "Nondetects and Data Analysis: Statistics for Censored Environmental Data." Vadose Zone Journal 5, no. 1 (January 2006): 508–9. http://dx.doi.org/10.2136/vzj2005.0106br.

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15

Kumar, Vinay, S. S. Maitra, and Rohit Nandan Shukla. "Environmental Metagenomics: The Data Assembly and Data Analysis Perspectives." Journal of The Institution of Engineers (India): Series A 96, no. 1 (January 10, 2015): 71–83. http://dx.doi.org/10.1007/s40030-014-0102-y.

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16

Dixon, Philip. "Nondetects and Data Analysis: Statistics for Censored Environmental Data." Journal of the American Statistical Association 101, no. 473 (March 2006): 401. http://dx.doi.org/10.1198/jasa.2006.s85.

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17

Liu, Shiping, Jye-Chyi Lu, Dana W. Kolpin, and William Q. Meeker. "Analysis of Environmental Data with Censored Observations." Environmental Science & Technology 31, no. 12 (December 1997): 3358–62. http://dx.doi.org/10.1021/es960695x.

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18

Gross, Deborah S., Robert Atlas, Jeffrey Rzeszotarski, Emma Turetsky, Janara Christensen, Sami Benzaid, Jamie Olson, Thomas Smith, Leah Steinberg, and Jon Sulman. "Environmental chemistry through intelligent atmospheric data analysis." Environmental Modelling & Software 25, no. 6 (June 2010): 760–69. http://dx.doi.org/10.1016/j.envsoft.2009.12.001.

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19

Sain, Stephan R. "Analysis and Modelling of Spatial Environmental Data." Journal of the American Statistical Association 101, no. 475 (September 2006): 1312. http://dx.doi.org/10.1198/jasa.2006.s125.

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20

Pooler, Penelope S. "Handling Missing Data: Applications to Environmental Analysis." Journal of the American Statistical Association 101, no. 473 (March 2006): 400–401. http://dx.doi.org/10.1198/jasa.2006.s84.

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21

WAKUDA, Hidehiko, Masatoshi ICHIKAWA, and Ichiro TAKADA. "Computer aided analysis of working environmental data." Bunseki kagaku 36, no. 3 (1987): T35—T40. http://dx.doi.org/10.2116/bunsekikagaku.36.3_t35.

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22

M. M. R., Manu, Rajib Saha, Azizul Hoque S. N. M., and Aminul Hoque. "Remote Environmental Data Analysis Using Sounding Rocket." International Journal of Engineering and Technology 11, no. 6 (December 31, 2019): 1209–21. http://dx.doi.org/10.21817/ijet/2019/v11i6/191106028.

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23

Ross, D. Lauren. "Multivariate Statistical Analysis of Environmental Monitoring Data." Ground Water 35, no. 6 (November 1997): 1050–57. http://dx.doi.org/10.1111/j.1745-6584.1997.tb00177.x.

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24

Villaseñor-Alva, José A., Elizabeth González-Estrada, and María Guzmán-Martínez. "Extreme value modeling for environmental data analysis." Boletín de la Sociedad Matemática Mexicana 20, no. 1 (March 25, 2014): 137–45. http://dx.doi.org/10.1007/s40590-014-0011-0.

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25

Haas, Timothy C. "Analysis and Modeling of Spatial Environmental Data." Mathematical Geology 37, no. 8 (November 2005): 943–46. http://dx.doi.org/10.1007/s11004-005-9232-0.

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26

Singh, D., M. S. P. Meirelles, G. A. Costa, I. Herlin, J. P. Berroir, and E. F. Silva. "Environmental degradation analysis using NOAA/AVHRR data." Advances in Space Research 37, no. 4 (January 2006): 720–27. http://dx.doi.org/10.1016/j.asr.2004.12.052.

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27

Manap, Norpadzlihatun, and Nikolaos Voulvoulis. "Data analysis for environmental impact of dredging." Journal of Cleaner Production 137 (November 2016): 394–404. http://dx.doi.org/10.1016/j.jclepro.2016.07.109.

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28

Givoni, Baruch. "Problematics of data analysis in environmental research." Mathematical Social Sciences 13, no. 3 (June 1987): 300–301. http://dx.doi.org/10.1016/0165-4896(87)90043-6.

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29

Shih, Shou Hsing, and Chris P. Tsokos. "A new forecasting model for nonstationary environmental data." Nonlinear Analysis: Theory, Methods & Applications 71, no. 12 (December 2009): e1209-e1214. http://dx.doi.org/10.1016/j.na.2009.01.242.

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30

Wang, Sujing, and Christoph F. Eick. "A data mining framework for environmental and geo-spatial data analysis." International Journal of Data Science and Analytics 5, no. 2-3 (September 30, 2017): 83–98. http://dx.doi.org/10.1007/s41060-017-0075-9.

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31

Horowitz, Joel L., and Neil Wrigley. "Categorical Data Analysis for Geographers and Environmental Scientists." Economic Geography 62, no. 2 (April 1986): 192. http://dx.doi.org/10.2307/144098.

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32

HASS, J. RONALD, YVES TONDEUR, JAKAL AMIN, EDWIN A. MARTI, and HANI KARAM. "DATA QUALITY AND THE ANALYSIS OF ENVIRONMENTAL SAMPLES." Analytical Sciences 7, Supple (1991): 1139–45. http://dx.doi.org/10.2116/analsci.7.supple_1139.

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33

Longnecker, Krista, Joe Futrelle, Elizabeth Coburn, Melissa C. Kido Soule, and Elizabeth B. Kujawinski. "Environmental metabolomics: Databases and tools for data analysis." Marine Chemistry 177 (December 2015): 366–73. http://dx.doi.org/10.1016/j.marchem.2015.06.012.

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34

Prentice, Ross L., and Duncan Thomas. "Methodologic Research Needs in Environmental Epidemiology: Data Analysis." Environmental Health Perspectives 101 (December 1993): 39. http://dx.doi.org/10.2307/3431658.

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35

Lovett, Andrew, and Neil Wrigley. "Categorical Data Analysis for Geographers and Environmental Scientists." Transactions of the Institute of British Geographers 11, no. 1 (1986): 124. http://dx.doi.org/10.2307/622079.

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36

Gotway, Carol A. "Spatial Data Analysis in the Social and Environmental." Technometrics 34, no. 4 (November 1992): 499–500. http://dx.doi.org/10.1080/00401706.1992.10484976.

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37

Filzmoser, Peter, Karel Hron, and Clemens Reimann. "The bivariate statistical analysis of environmental (compositional) data." Science of The Total Environment 408, no. 19 (September 1, 2010): 4230–38. http://dx.doi.org/10.1016/j.scitotenv.2010.05.011.

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38

Petrisor, Alexandru-Ionut, Mircea Grigorovschi, Vasile Meita, and Cezar-Petre Simion-Melinte. "LONG-TERM ENVIRONMENTAL CHANGES ANALYSIS USING CORINE DATA." Environmental Engineering and Management Journal 13, no. 4 (2014): 847–60. http://dx.doi.org/10.30638/eemj.2014.089.

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39

Prentice, R. L., and D. Thomas. "Methodologic research needs in environmental epidemiology: data analysis." Environmental Health Perspectives 101, suppl 4 (December 1993): 39–48. http://dx.doi.org/10.1289/ehp.93101s439.

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40

Rong, Yue. "Statistical Methods and Pitfalls in Environmental Data Analysis." Environmental Forensics 1, no. 4 (January 2000): 213–20. http://dx.doi.org/10.1006/enfo.2000.0022.

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41

SCHIPPER, MARIA, JOLANDA DEN HARTOG, and EVERT MEELIS. "SEQUENTIAL ANALYSIS OF ENVIRONMENTAL MONITORING DATA: OPTIMAL SPRTs." Environmetrics 8, no. 1 (January 1997): 29–41. http://dx.doi.org/10.1002/(sici)1099-095x(199701)8:1<29::aid-env236>3.0.co;2-3.

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42

Haining, R. P. "Categorial data analysis for geographers and environmental scientists." Applied Geography 6, no. 4 (October 1986): 360. http://dx.doi.org/10.1016/0143-6228(86)90037-8.

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43

Worsfold, P. J. "Methods of Environmental Data Analysis, Elsevier Applied Science." Analytica Chimica Acta 279, no. 2 (July 1993): 343. http://dx.doi.org/10.1016/0003-2670(93)80339-m.

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44

Whitelegg, John. "Categorical data analysis for geographers and environmental scientists." Land Use Policy 4, no. 1 (January 1987): 85. http://dx.doi.org/10.1016/0264-8377(87)90018-4.

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45

Paksy, L�szl�. "Statistical design of data handling in environmental analysis." Mikrochimica Acta 123, no. 1-4 (March 1996): 197–205. http://dx.doi.org/10.1007/bf01244393.

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46

Qian, Xiaodong, Zhen Li, and Zhixin Li. "A thermal environmental analysis method for data centers." International Journal of Heat and Mass Transfer 62 (July 2013): 579–85. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2013.03.037.

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47

Dbouk, Talib, and Dimitris Drikakis. "Correcting pandemic data analysis through environmental fluid dynamics." Physics of Fluids 33, no. 6 (June 2021): 067116. http://dx.doi.org/10.1063/5.0055299.

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48

Kim, Yoon-Sung, Hae-Chang Rim, and Do-Gil Lee. "Business environmental analysis for textual data using data mining and sentence-level classification." Industrial Management & Data Systems 119, no. 1 (February 4, 2019): 69–88. http://dx.doi.org/10.1108/imds-07-2017-0317.

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Abstract:

Purpose The purpose of this paper is to propose a methodology to analyze a large amount of unstructured textual data into categories of business environmental analysis frameworks. Design/methodology/approach This paper uses machine learning to classify a vast amount of unstructured textual data by category of business environmental analysis framework. Generally, it is difficult to produce high quality and massive training data for machine-learning-based system in terms of cost. Semi-supervised learning techniques are used to improve the classification performance. Additionally, the lack of feature problem that traditional classification systems have suffered is resolved by applying semantic features by utilizing word embedding, a new technique in text mining. Findings The proposed methodology can be used for various business environmental analyses and the system is fully automated in both the training and classifying phases. Semi-supervised learning can solve the problems with insufficient training data. The proposed semantic features can be helpful for improving traditional classification systems. Research limitations/implications This paper focuses on classifying sentences that contain the information of business environmental analysis in large amount of documents. However, the proposed methodology has a limitation on the advanced analyses which can directly help managers establish strategies, since it does not summarize the environmental variables that are implied in the classified sentences. Using the advanced summarization and recommendation techniques could extract the environmental variables among the sentences, and they can assist managers to establish effective strategies. Originality/value The feature selection technique developed in this paper has not been used in traditional systems for business and industry, so that the whole process can be fully automated. It also demonstrates practicality so that it can be applied to various business environmental analysis frameworks. In addition, the system is more economical than traditional systems because of semi-supervised learning, and can resolve the lack of feature problem that traditional systems suffer. This work is valuable for analyzing environmental factors and establishing strategies for companies.

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49

Kihn, E. A., M. Zhizhin, R. Siquig, and R. Redmon. "The Environmental Scenario Generator (ESG): a distributed environmental data archive analysis tool." Data Science Journal 3 (2004): 10–28. http://dx.doi.org/10.2481/dsj.3.10.

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50

Kuosmanen, Timo, and Mika Kortelainen. "Valuing environmental factors in cost–benefit analysis using data envelopment analysis." Ecological Economics 62, no. 1 (April 2007): 56–65. http://dx.doi.org/10.1016/j.ecolecon.2007.01.004.

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