Abstract
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.
| Original language | English |
|---|---|
| Article number | 8576150 |
| Journal | Advances in Meteorology |
| Volume | 2017 |
| DOIs | |
| Publication status | Published - 2017 |
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Geospatial Analysis of Extreme Weather Events in Nigeria (1985-2015) Using Self-Organizing Maps. / Akande, Adeoluwa; Costa, Ana Cristina; Mateu, Jorge; Henriques, Roberto.
In: Advances in Meteorology, Vol. 2017, 8576150, 2017.Research output: Contribution to journal › Article
TY - JOUR
T1 - Geospatial Analysis of Extreme Weather Events in Nigeria (1985-2015) Using Self-Organizing Maps
AU - Akande, Adeoluwa
AU - Costa, Ana Cristina
AU - Mateu, Jorge
AU - Henriques, Roberto
N1 - Akande, A., Costa, A. C., Mateu, J., & Henriques, R. (2017). Geospatial Analysis of Extreme Weather Events in Nigeria (1985-2015) Using Self-Organizing Maps. Advances in Meteorology, 2017, [8576150]. https://doi.org/10.1155/2017/8576150
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85028411166&partnerID=8YFLogxK
U2 - 10.1155/2017/8576150
DO - 10.1155/2017/8576150
M3 - Article
VL - 2017
JO - Advances in Meteorology
JF - Advances in Meteorology
SN - 1687-9309
M1 - 8576150
ER -