Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps
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Otros documentos de la autoría: Akande, Adeoluwa; Costa, Ana Cristina; Mateu, Jorge; Henriques, Roberto
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comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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Título
Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing MapsFecha de publicación
2017ISSN
1687-9309; 1687-9317Cita bibliográfica
AKANDE, Adeoluwa, et al. Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps. Advances in Meteorology, 2017Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.hindawi.com/journals/amete/2017/8576150/Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Advances in Meteorology, 2017Derechos de acceso
info:eu-repo/semantics/openAccess
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