Where Will the Next Oil Spill Incident in the Niger Delta Region of Nigeria Occur?

Vremudia Onyeayana Wekpe, Malcolm Whitworth, Brian Baily
{"title":"Where Will the Next Oil Spill Incident in the Niger Delta Region of Nigeria Occur?","authors":"Vremudia Onyeayana Wekpe, Malcolm Whitworth, Brian Baily","doi":"10.1088/2515-7620/ad29b5","DOIUrl":null,"url":null,"abstract":"\n Oil spill incidents are almost a daily occurrence within the Niger Delta region of Nigeria with far reaching environmental, economic and social consequences. This study aimed at understanding the spatial and temporal context of the problem as a panacea for forecasting likely locations of oil spill incidents within the region. About 76.77% of crude oil spilt in the Niger Delta is lost to the environment with only about 23% of the crude oil recovered from the environment, this represents a very worrying statistic in terms of the known and unknown negative impacts of oil spills. Space Time Pattern Mining (STPM) tools were adapted to explore and interrogate historical spill data. Time series forecasting was then used for forecasting possible locations of future oil spills within the region. Results show that there is a pattern to oil spill occurrences in the Niger Delta with statistically significant hotspots identified in Rivers State, Bayelsa State and Delta State. Forecast root mean square error (RMSE) and forecast validation RMSE are -1.016328 and 1.035992 respectively. This suggests an ability of the model to fairly predict likely locations of future oil spills. This was further verified by counting the number of spills that occur within any area based on the predicted likelihood of spill occurrence. This study has shown that STPM tools can be deployed in understanding the occurrence and prediction of oil spill incidents. This will ultimately aid in the deployment of scarce management resources to where they are most needed.","PeriodicalId":505267,"journal":{"name":"Environmental Research Communications","volume":"89 21","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Research Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2515-7620/ad29b5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Oil spill incidents are almost a daily occurrence within the Niger Delta region of Nigeria with far reaching environmental, economic and social consequences. This study aimed at understanding the spatial and temporal context of the problem as a panacea for forecasting likely locations of oil spill incidents within the region. About 76.77% of crude oil spilt in the Niger Delta is lost to the environment with only about 23% of the crude oil recovered from the environment, this represents a very worrying statistic in terms of the known and unknown negative impacts of oil spills. Space Time Pattern Mining (STPM) tools were adapted to explore and interrogate historical spill data. Time series forecasting was then used for forecasting possible locations of future oil spills within the region. Results show that there is a pattern to oil spill occurrences in the Niger Delta with statistically significant hotspots identified in Rivers State, Bayelsa State and Delta State. Forecast root mean square error (RMSE) and forecast validation RMSE are -1.016328 and 1.035992 respectively. This suggests an ability of the model to fairly predict likely locations of future oil spills. This was further verified by counting the number of spills that occur within any area based on the predicted likelihood of spill occurrence. This study has shown that STPM tools can be deployed in understanding the occurrence and prediction of oil spill incidents. This will ultimately aid in the deployment of scarce management resources to where they are most needed.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
尼日利亚尼日尔三角洲地区的下一次漏油事件会在哪里发生?
尼日利亚尼日尔三角洲地区几乎每天都会发生漏油事件,对环境、经济和社会造成深远影响。本研究旨在了解这一问题的时空背景,以此作为预测该地区可能发生溢油事故地点的灵丹妙药。在尼日尔三角洲泄漏的原油中,约有 76.77% 流失到环境中,只有约 23% 的原油从环境中回收,就已知和未知的溢油负面影响而言,这是一个非常令人担忧的统计数据。时空模式挖掘(STPM)工具用于探索和查询历史溢油数据。然后使用时间序列预测法预测该地区未来可能的漏油地点。结果表明,尼日尔河三角洲的漏油事件有一定的规律可循,在统计意义上,河流州、巴耶尔萨州和三角洲州是漏油事件的热点地区。预测均方根误差 (RMSE) 和预测验证均方根误差分别为 -1.016328 和 1.035992。这表明该模型有能力公平地预测未来石油泄漏的可能地点。根据预测的溢油发生可能性,通过统计任何区域内发生的溢油数量,进一步验证了这一点。这项研究表明,STPM 工具可用于了解溢油事故的发生和预测。这最终将有助于将稀缺的管理资源调配到最需要的地方。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
What factors influence individuals' willingness to pay for environmental protection: Evidence from CGSS2021 Unlocking bioremediation potential: harnessing an indigenous bacterial consortium from effluent treatment plants for industrial wastewater treatment Household Air Pollution Disparities Between Socioeconomic Groups in Chicago An integrated expert recommender system approach to environmental service priorities in renewable energy Climatic characteristics of centennial and extreme precipitation in Hangzhou, China
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1