Data Mining of Meteorological-related Attributes from Smartphone Data

Main Article Content

Luiz Fernando Afra Brito
Marcelo Keese Albertini


This paper presents studies on using data mining techniques with data collected from mobile devices in order to verify the viability of usage on rainfall alert systems.

In our study, we have employed smartphones to gather meteorological-related data from telecommunication technologies, such as, Global System for Mobile Communications (GSM) and Global Positioning System (GPS).
In order to evaluate the capability of monitoring rain with data from smartphones, we used a simplified rainfall simulator to conduct studies in controlled scenarios.

We used classification algorithms such as k-Nearest Neighbors, Support Vector Machine and Decision Tree to identify rainfall types (no rain, light rain and heavy rain). The classification results were promising and showed area under ROC curve of 0.95 with the k-Nearest Neighbors algorithm and 0.80 with Support Vector Machine.
Additionally we have conducted preliminary and promising experiments in a real world scenario which motivates further research on data collection, preprocessing and specialized classification for alarm systems.

Article Details

How to Cite
Brito, L. F. A., & Albertini, M. K. (1). Data Mining of Meteorological-related Attributes from Smartphone Data. INFOCOMP Journal of Computer Science, 15(2), 1-9. Retrieved from


Arruda, B. A. Estudo comparativo das tecnicas para calculo de atenuacao devido a chuva. Master’s thesis, Federal University of Uberlandia, Brazil, 2008.

Atlas, D. and Ulbrich, C. W. Path- and area-integrated rainfall measurement by microwave attenuation in the 1–3 cm band. Journal of Applied Meteorology, 16(12):1322–1331, Dec. 1977.

Box, G. E. and Wilson, K. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B (Methodological), 13(1):1–45, 1951.

Chang, C.-C. and Lin, C.-J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):1–27, Apr. 2011.

Ebert, E. E., Janowiak, J. E., and Kidd, C. Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bulletin of the American Meteorological Society, 88(1):47–64, Jan. 2007.

Fong, B., Rapajic, P. B., Hong, G. Y., and Fong, A. C. M. Factors causing uncertainties in outdoorwireless wearable communications. IEEE Pervasive Computing, 2(2):16–20, Apr. 2003.

Griffith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., Stout, J., and Sikdar, D. N. Rain estimation from geosynchronous satellite imagery—visible and infrared studies. Monthly Weather Review, 106(8):1153–1171, Aut. 1978.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. The WEKA data mining software: An update. SIGKDD Explor. Newsl., 11(1):10–18, Nov. 2009.

Madaus, L. E., Hakim, G. J., and Mass, C. F. Utility of dense pressure observations for improving mesoscale analyses and forecasts. Monthly. Weathear Review, 142(7):2398–2413, Jul. 2014.

Mass, C. F. and Madaus, L. E. Surface pressure observations from smartphones: A potential revolution for high-resolution weather prediction? Bulletin of the American Meteorological Society, 95(9):1343–1349, Sep. 2014.

Meischner, P., editor. Weather radar: principles and advanced applications. Springer Berlin Heidelberg, 2004.

Messer, H. Rainfall monitoring using cellular networks [in the spotlight]. IEEE Signal Processing Magazine, 24(3):144–142, May 2007.

Michalski, R. S., Carbonell, J. G., and Mitchell, T. M., editors. Machine learning: An artificial intelligence approach. Springer Science and Business Media, 1983.

Minda, H. and Nakamura, K. High temporal resolution path-average rain gauge with 50-GHz band microwave. Journal of Atmospheric and Oceanic Technology, 22(2):165–179, Feb. 2005.

Niforatos, E., Campos, P., Vourvopoulos, A., Doria, A., and Langheinrich, M. Atmos: a hybrid crowdsourcing approach to weather estimation. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp ’14 Adjunct, pages 135–138. Association for Computing Machinery (ACM), 2014.

OpenSignal. Opensignal: 3G and 4G LTE cell coverage map., 2014. [Online; accessed 16-November-2014].

Otero, J., Yalamanchili, P., and Braun, H.-W. High performance wireless networking and weather. High Performance Wireless Research and Education Network

, 2001.

Overeem, A., Robinson, J. C. R., Leijnse, H., Steeneveld, G. J., Horn, B. K. P., and Uijlenhoet, R. Crowdsourcing urban air temperatures from smartphone battery temperatures.

Geophysical Research Letters, 40(15):4081–4085, Aug.

pressureNet. The weather’s future., 2014. [Online; accessed 16-November-2014].

Ramirez, M. C. V., de Campos Velho, H. F., and Ferreira, N. J. Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. Journal of Hydrology, 301(1-4):146–162, Jan. 2005.

Rincon, R. F. and Lang, R. H. Microwave link dual-wavelength measurements of path-average attenuation for the estimation of drop size distributions and rainfall. IEEE Transactions - Geoscience and Remote Sensing, 40(4):760–770, Apr. 2002.

Wheatley, D. M. and Stensrud, D. J. The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale ensemble system. Monthly Weather Review, 138(5):1673–1694, May 2010.

Yi, W.-J., Jia, W., and Saniie, J. Mobile sensor data collector using Android smartphone. In 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), pages 956–959. Institute of Electrical & Electronics Engineers (IEEE), Aug. 2012.

Zinevich, A., Messer, H., and Alpert, P. Prediction of rainfall intensity measurement errors using commercial microwave communication links. Atmospheric Measurement Techniques, 3(5):1385–1402, Oct. 2010.