Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps
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 deserti...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/8576150 |
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| author | Adeoluwa Akande Ana Cristina Costa Jorge Mateu Roberto Henriques |
| author_facet | Adeoluwa Akande Ana Cristina Costa Jorge Mateu Roberto Henriques |
| author_sort | Adeoluwa Akande |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1ea5a4c55b0f4f919f643198e7f78a11 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-1ea5a4c55b0f4f919f643198e7f78a112025-08-20T03:54:57ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/85761508576150Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing MapsAdeoluwa Akande0Ana Cristina Costa1Jorge Mateu2Roberto Henriques3NOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalNOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalDepartment of Mathematics, Universitat Jaume I, 12071 Castellon, SpainNOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalThe 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.http://dx.doi.org/10.1155/2017/8576150 |
| spellingShingle | Adeoluwa Akande Ana Cristina Costa Jorge Mateu Roberto Henriques Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps Advances in Meteorology |
| title | Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps |
| title_full | Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps |
| title_fullStr | Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps |
| title_full_unstemmed | Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps |
| title_short | Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps |
| title_sort | geospatial analysis of extreme weather events in nigeria 1985 2015 using self organizing maps |
| url | http://dx.doi.org/10.1155/2017/8576150 |
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