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A visual big data system for the prediction of weather-related variables: Jordan-Spain case study

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Abstract

The Meteorology is a field where huge amounts of data are generated, mainly collected by sensors at weather stations, where different variables can be measured. Those data have some particularities such as high volume and dimensionality, the frequent existence of missing values in some stations, and the high correlation between collected variables. In this regard, it is crucial to make use of Big Data and Data Mining techniques to deal with those data and extract useful knowledge from them that can be used, for instance, to predict weather phenomena. In this paper, we propose a visual big data system that is designed to deal with high amounts of weather-related data and lets the user analyze those data to perform predictive tasks over the considered variables (temperature and rainfall). The proposed system collects open data and loads them onto a local NoSQL database fusing them at different levels of temporal and spatial aggregation in order to perform a predictive analysis using univariate and multivariate approaches as well as forecasting based on training data from neighbor stations in cases with high rates of missing values. The system has been assessed in terms of usability and predictive performance, obtaining an overall normalized mean squared error value of 0.00013, and an overall directional symmetry value of nearly 0.84. Our system has been rated positively by a group of experts in the area (all aspects of the system except graphic desing were rated 3 or above in a 1–5 scale). The promising preliminary results obtained demonstrate the validity of our system and invite us to keep working on this area.

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Notes

  1. https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-ghcn

  2. Check ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt for further information on the M-,Q-, and S-Flag.

  3. All that information has been obtained from https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt

  4. There are some Spanish word in the figure whose meaning is: estación = station; fecha = date; valor_dato = datum_value

  5. https://weka.sourceforge.io/doc.packages/timeseriesForecasting/weka/classifiers/timeseries/WekaForecaster.html

  6. https://weka.sourceforge.io/doc.dev/weka/classifiers/meta/Bagging.html

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Acknowledgments

This paper was drafted as part of Juan A. Lara’s research stay during 2019-2020 at Jordan University of Science and Technology, JUST (Jordan), which partially sponsored this research. The authors would like to thank UDIMA’s and JUST’s students who took part in the design and implementation of the system, particularly Francisco Javier Moreno Hermosilla, Paulina Pyzel and Amnah Al-Abdi; and JUST’s experts for providing their feedback in order to assess this system.

Author information

Authors and Affiliations

  1. JUST, Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan

    Shadi Aljawarneh & Muneer Bani Yassein

  2. School of Computer Science, Madrid Open University, UDIMA, Carretera de La Coruña, KM. 38,500, Vía de Servicio, n° 15, 28400 Collado Villalba, Madrid, Spain

    Juan A. Lara

Authors
  1. Shadi Aljawarneh
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  3. Muneer Bani Yassein
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Correspondence to Juan A. Lara.

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APPENDIX

APPENDIX

1.1 I –. ARFF files generated by the system

1.2 A. Excerpt of a particular minable view created for “standard” analysis (file .arff)

figure b

@relation weather-project.

@attribute Date date ‘yyyy-MM-dd’.

@attribute raLnfall numeric.

@attribute tmLn numeric.

@attribute tmax numeric.

@data.

2016–1-C1,40.90490992906111,3.125,13.33111111111111.

2016–2-C1,34.753053538158774,5.157777777777778,18.84.

2016-3-01,48.504665419434346,7.76046511627907,21.05625.

2016-4-01,42.176677782541,12.59375,28.476829268292683.

2016-5-01,?,14.482608695652175,29.78192771084337.

2016-6-01,?,19.125555555555557,36.2038961038961.

2016-7-01,?,20.276767676767676,36.09493670886076.

2016-8-01,?,21.55056179775281,36.88076923076923.

2016-9-01,?,16.78426966292135,32.72894736842105.

2016-10-01,48.04021044733257,13.712903225806452,29.78170731707317.

2016-11-01,?f7.062637362637362,21.71772151898734.

2016-12-01,44.539838581248475,2.833707865168539,13.484210526315788.

2017-1-01,32.95836866004329,1.4148936170212765,13.410975609756099.

2017-2-01,36.37586159726386,1.1903225806451612,15.182894736842105.

2017-3-01,40.60443010546419,6.790425531914893,20.07.

2017-4-01,39.17010546939185,12.114285714285714,27.001785714285717.

2017-5-01,?,14.576842105263157,31.349.

2017-6-01,?,18.812222222222225,34.6.

2017-7-01,?,22.743478260869566,38.703947368421055.

2017-8-01,?,20.94123711340206,37.10253164556962.

2017-9-01,?,18.993269230769233,34.8725.

2017–10-01,0,14.519847328244273,27.939772727272725.

2017–11-01,34.965075614664805,8.31359223300971,21.822093023255814.

2017–12-01,40.16383020752389,5.935294117647059,19.084883720930232

1.3 B. Excerpt of a particular minable view created for “neighbour-based” analysis (file .arff)

figure c

@relation weather-project @attriblate year numem-ic @attribu-te month numeric @attribu-te rainfall numeric @attribu-te latitude numeric @attribu-te longitiade numem-ic @attribu-te altitaide numeric @data.

2016,1,3-258,096,528,021,482,325,390,381,950,6E16.

2016,2,3-8,213,641,296,489,095,325,390,381,950,686.

2016,2,5-299,971,020,274,537,325,390,381,950,686.

2016,1,2-4,849,066,497,880,004,325,390,381,950,686.

2016,5,2,325,390,381,950,686.

2016,6,2,325,390,381,950,686.

2016,7,2,325,390,381′950,686.

2016,8,2,325,390,381′950,686.

2016,9,2,325,390,381′950,686.

2016,10,?,325,390,381,950,686.

2016,11,?,325,390,381,950,686.

2016,12,3-349,904,087,274,605,325,390,381,950,6436.

2017,1,?,325,290,381,950,686.

2017,2,2,225,390,381,950,686.

2017,3,4-762,173,924,797,756,325,390,381,950,6436.

2017,4,4-269,697,449,699,962,325,390,381,950,6436.

2017,5,2,325,390,281,950,686.

2017,6,2,325,390,281,950,686.

2017,7,2,325,390,281,950,686.

2017,8,2,325,390,281,950,686.

2017,9,2,325,390,201,950,686.

2017,10,?,325,390,201,950,686.

2017,11,2-4,849,066,497,880,004,325,390,381,950,606.

2017,12,2-0149020205422647,325,390,381,950,606.

2016,1,2-7,950,615,700,918,397,321,610,371,490,677

1.4 C. Excerpt of. ARFF test file

figure d

@relation weather-project.

@attribute year numeric.

@attribute month numeric.

@attribute rainfall numeric.

@attribute latitude numeric.

@attribute longitude numeric.

@attribute altitude numeric.

data.

2018,1,?,325,390,381,950,686.

2018,2,?,325,390,381,950,686.

2018,3,?,325,390,381,950,686.

2018,4,?,325,390,381,950,686.

2018,5,?,325,390,381,950,686.

2018,6,?,325,390,381,950,686.

2018,7,?,325,390,381,950,686.

2018,8,?,325,390,381,950,686.

2018,9,?,325,390,381,950,686.

2018,10,?,325,390,381,950,686.

2018,11,?,325,390,381,950,686.

2018,12,?,325,390,381,950,686.

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Aljawarneh, S., Lara, J.A. & Yassein, M.B. A visual big data system for the prediction of weather-related variables: Jordan-Spain case study. Multimed Tools Appl 82, 13103–13139 (2023). https://doi.org/10.1007/s11042-020-09848-9

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