Multi-source Rainfall Data Analysis Based on OLAP and Visualization Techniques: a Practical Approach

• Lorenna Christ'na Nascimento UFF
• Lucas Knust UFF
• Ramon Santos UFF
• Bruno Cunha Sá UFF
• Gustavo Muller Moreira UFF
• Fabiola de Souza Freitas Secretaria Municipal de Defesa Civil e Geotecnia de Niterói
• Nathalia Moura Secretaria Municipal de Defesa Civil e Geotecnia de Niterói
• Marcos Lage UFF
• Daniel de Oliveira UFF

DOI: https://doi.org/10.5753/wcama.2021.15731

Abstract

Climate studies have gained relevance due to the increase in climatic events with severe impacts observed in the last decade, especially in urban areas. For example, large volumes of precipitation can cause floods and landslides, impacting city traffic and even costing citizens' lives. In order to make it possible to monitor rainfall volumes, pluviometers are installed throughout the country. However, such stations are controlled by multiple organizations and produce data in different space/time resolutions and formats. This paper proposes TEMPO, a system that uses OLAP (Online Analytical Processing) techniques to propose efficient storage, query, and analysis mechanisms to handle pluviometers data. To evaluate the tool, we present a case study showing the integration and analysis of data from CEMADEN and Alerta Rio.

Keywords: OLAP, Visualization, Rainfall Data

References

CEMADEN (2021). Centro nacional de monitoramento e alertas de desastres naturais - http://www.cemaden.gov.br/apresentacao/.

Chan, W. W.-Y. (2006). A survey on multivariate data visualization. Department of Computer Science and Engineering. Hong Kong University of Science and Technology, 8(6):1–29.

Cuzzocrea, A. (2015). Data warehousing and OLAP over big data: a survey of the stateof-the-art, open problems and future challenges. Int. J. Bus. Process. Integr. Manag., 7(4):372–377.

Diehl, A., Pelorosso, L., Delrieux, C., Saulo, C., Ruiz, J., Groller, M. E., and Bruckner, S. (2015). Visual analysis of spatio-temporal data: Applications in weather forecasting. In Computer Graphics Forum, number 3 in 34, pages 381–390.

Esplugues, F. B., Gramaje, M. d. C. P., and García-Haro, F. J. (2013). Tecnicas de minería de datos para el analisis de periodos de sequía en españa. Revista Tiempo y Clima, 5(30).

Han, J., Kamber, M., and Pei, J. (2012). Data mining concepts and techniques. Elsevier

Inmon, W., Welch, J., and Glassey, K. (2005). Building the Data Warehouse. Sons Inc, New York.

Kimball, R. and Ross, M. (2002). The Data Warehouse Toolkit: The complete guide to dimensional modeling. Wiley, New York.

Liu, Z. and Heer, J. (2014). The effects of interactive latency on exploratory visual analysis. IEEE transactions on visualization and computer graphics, 20(12):2122–2131.

Lu, G. Y. and Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & geosciences, 34(9):1044–1055.

Milanesi, M., Rozolen, R., and Galvani, E. (2017). Comparativo entre instrumentos pluviométricos experimentais e automáticos. In XVII Simpósio Brasileiro de Geografia Física Aplicada, pages 2251–2261.

Mizutori, M. and Guha-Sapir, D. (2020). Human cost of disasters 2000-2019. Technical report, United Nations Office for Disaster Risk Reduction.

Morais, L. d. and Ferreira, N. C. (2015). Banco de dados pluviométricos integrados por dados do sensor trmm e estações pluviométricas no estado de goiás. Anais Eletrônicos, 17.

Salas, D., Liang, X., Navarro, M., Liang, Y., and Luna, D. (2020). An open-data openmodel framework for hydrological models’ integration, evaluation and application. Environ. Model. Softw., 126:104622.

Thorndahl, S. and Willems, P. (2008). Probabilistic modelling of overflow, surcharge and flooding in urban drainage using the first-order reliability method and parameterization of local rain series. Water Research, 42(1):455–466.