Research Article
Towards Automated Environmental Monitoring Using YOLOv8 Variants
Nnanna Ekedebe*
Issue:
Volume 14, Issue 5, October 2026
Pages:
160-166
Received:
13 July 2026
Accepted:
24 July 2026
Published:
18 September 2026
Abstract: Oil spills in the Niger Delta pose a severe environmental threat, yet existing detection methods remain slow, manual and unsuitable for large-scale continuous monitoring. While existing studies have applied individual YOLOv8 variants to oil spill detection, no prior study has systematically benchmarked YOLOv8n, YOLOv8s and YOLOv8m under identical conditions framed around Niger Delta deployment constraints. This study benchmarks three YOLOv8 variants nano, small and medium trained and evaluated under identical conditions. The dataset used is a publicly available Oil Spill Detection dataset from Roboflow Universe, comprising 2,567 images across four classes: object, rainbow, sheen and truecolor. The models were evaluated on a held-out test set of 72 images using mAP@50, mAP@50-95, Precision, Recall and Inference Speed. It was found that YOLOv8m achieved the highest accuracy at 81.95% mAP@50, while YOLOv8n achieved the fastest inference at 6.2ms with 68.66% mAP@50, and YOLOv8s offered no clear advantage over nano. YOLOv8n is recommended for resource-constrained deployment given its speed advantage, while YOLOv8m is recommended where computational resources allow, given its superior accuracy. These findings show that deep learning based detection is a viable and practical approach for automated environmental monitoring in the Niger Delta region, offering a better pathway toward localized, scalable oil spill surveillance in a region that has lacked such tools, helping guide faster cleanup response.
Abstract: Oil spills in the Niger Delta pose a severe environmental threat, yet existing detection methods remain slow, manual and unsuitable for large-scale continuous monitoring. While existing studies have applied individual YOLOv8 variants to oil spill detection, no prior study has systematically benchmarked YOLOv8n, YOLOv8s and YOLOv8m under identical c...
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Research Article
Climate Variability Along the Northern Coast of Senegal: Analysis of Trends, Anomalies and Breaks at the Stations of Thies, Louga and Saint-louis (1994-2024)
Issue:
Volume 14, Issue 5, October 2026
Pages:
167-177
Received:
2 September 2026
Accepted:
15 September 2026
Published:
20 October 2026
DOI:
10.11648/j.ijema.20261405.13
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Abstract: The northern coast of Senegal, located on the western edge of the Sahel, is characterized by high climate variability, the repercussions of which directly affect the region's socio-economic activities, particularly market gardening in the Niayes area. This study aims to analyze the variability of rainfall and temperatures at the Thies, Louga, and Saint-Louis weather stations over the period 1994-2024, through the study of temporal trends, interannual anomalies, and breaks in the climate series. The data, provided by the National Civil Aviation and Meteorology Agency (ANACIM), were processed using the Mann-Kendall test, the Sen slope test, and the Pettitt test. The results reveal a rainfall gradient decreasing from south to north, with average rainfall decreasing from 474.3 mm in Thies to 289.0 mm in Saint-Louis, and a reverse temperature gradient, with the Louga station recording the highest temperatures. Trend analysis reveals a significant increase in rainfall and temperatures in Louga, a significant but more limited warming in Thies (minimum and average temperatures), and the absence of a significant trend in Saint-Louis. The Pettitt test reveals primarily thermal breaks, concentrated between 2009 and 2016 in Louga and Thies, with no break detected in Saint-Louis. These results reflect a marked spatial differentiation of climate change along the northern Senegalese coast, likely linked to the moderating effect of the Atlantic Ocean, and underscore the need for continuous climate monitoring to anticipate impacts on water resources and agricultural activities in the region.
Abstract: The northern coast of Senegal, located on the western edge of the Sahel, is characterized by high climate variability, the repercussions of which directly affect the region's socio-economic activities, particularly market gardening in the Niayes area. This study aims to analyze the variability of rainfall and temperatures at the Thies, Louga, and S...
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