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Advances in Meteorology
Volume 2017 (2017), Article ID 8576150, 11 pages
Research Article

Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

1NOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
2Department of Mathematics, Universitat Jaume I, 12071 Castellon, Spain

Correspondence should be addressed to Adeoluwa Akande

Received 18 January 2017; Revised 29 June 2017; Accepted 9 July 2017; Published 9 August 2017

Academic Editor: Efthymios I. Nikolopoulos

Copyright © 2017 Adeoluwa Akande et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.