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Rainfall Distribution Trend Analysis of the Philippine National Capital Region (2013-2016)

Topics: Big Data Algorithm, Methodology, Business Models and Challenges; Big Data as a Service (BDaaS) including Frameworks, Empirical Approaches and Data Processing Techniques; Data as a Service and Decision as a Service; Natural Science as a Service (NSaaS) including Weather Forecasting and Weather Data Visualization

Authors: Miguel Bobadilla ; Ryan Eugenio and Maria Pulido

Affiliation: Department of Physics, Mapúa University, Intramuros, Manila City, 1002 and Philippines

ISBN: 978-989-758-369-8

Keyword(s): Natural Science as a Service (NSaaS), Weather Forecasting, Decision as a Service, Big Data as a Service (BDaaS), Big Data Algorithm, Trend Analysis, Rainfall Distribution, Mann-Kendall Test.

Abstract: The Philippine archipelago is a tropical country that experiences only two major seasons annually: wet (June-November) and dry (December-May). Due to these conditions, the country is bound to experience significant amounts of rainfall, followed by drought. Hence, studying long-term rainfall trends is highly beneficial for the country’s livelihood and safety. In this work, we studied the rainfall distribution in the National Capital Region covering the period of 2013 to 2016, and analysed the data using the Mann-Kendall Test and the Bootstrap procedure. Using a monthly scale, we found a negative trend, signifying a decrease in rainfall amount over the four years of data. Interestingly, we found a positive trend using a yearly scale, showing an increase of rainfall overall. Therefore it is quite risky to generalize a certain region's rainfall condition just by looking at it annually, but must consider as well its seasonal and monthly phenomena for a more detailed analysis. We note also that the area being studied was considerably large and the rainfall data varied with the location of the weather station where it was obtained. This work demonstrates the potential of using Big Data and the Internet of Things to measure and predict weather trends using various sensors and processors. (More)

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Paper citation in several formats:
Bobadilla, M.; Eugenio, R. and Pulido, Maria Teresa R. (2019). Rainfall Distribution Trend Analysis of the Philippine National Capital Region (2013-2016).In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-369-8, pages 370-375. DOI: 10.5220/0007750303700375

@conference{iotbds19,
author={Miguel Aaron M. Bobadilla. and Ryan Gabriel A. Eugenio. and Pulido, Maria Teresa R.},
title={Rainfall Distribution Trend Analysis of the Philippine National Capital Region (2013-2016)},
booktitle={Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2019},
pages={370-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007750303700375},
isbn={978-989-758-369-8},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Rainfall Distribution Trend Analysis of the Philippine National Capital Region (2013-2016)
SN - 978-989-758-369-8
AU - Bobadilla, M.
AU - Eugenio, R.
AU - Pulido, Maria Teresa R.
PY - 2019
SP - 370
EP - 375
DO - 10.5220/0007750303700375

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