渔民专家对斐济鱼类中毒原因的看法:通过数据挖掘技术进行调查

J. Nahar, Neeraj Anand Sharma, Kunal Kumar, A. Prasad, Anal Kumar
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引用次数: 1

摘要

鱼类中毒是斐济人类疾病的常见原因。有毒物质最初可以被鱼类摄入,它们通常以海洋绿色植物、溢出的石油、脏水和淤泥垃圾为食。人类食用这些受感染的鱼会生病。鉴于鱼是大多数斐济人的主食,这一点尤其令人关切。本研究介绍了渔民对斐济不同地区鱼类中毒的重要原因和危险因素的看法:Rakiraki, Tavua, Sonaisaili岛,Gua岛,Nandi, Lautoka, Suva。该研究使用了基于计算智能(CI)的数据挖掘方法来阐明渔民的观点。然后使用关联规则挖掘(ARM)来查找这些视图之间的相关性和关联,并确定感知到的鱼中毒的主要原因。这项研究的目的是探讨ARM方法(以及开发适当的数据库)在确定专家渔民对导致鱼类中毒的原因和风险因素的共同看法方面的功效。结果表明,渔民普遍认为各种环境因素与鱼类中毒之间存在因果关系。这些因素包括受污染的鱼类通道、海草区、水上交通工具污染、水温、脏水和受污染的水、暴雨和洪水、夏季、藻华、深海环境和不负责任的人类活动。可以想象,这项研究的结果可能有助于设计鱼中毒诊断系统(包括鱼中毒的早期诊断),并在第一时间减少或预防预测因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fishermen’S Expert Views on the Causes of Fish Poisoning in Fiji: An Investigation through Data Mining Technique
Fish poisoning is a common cause of human illness in Fiji. Poisonous substances can be initially ingested by fish that typically feast upon marine green growth, spilt oil, dirty water and silted rubbish. Humans can fall ill when eating these infected fish. This is of particular concern given that fish is the staple diet of most Fijians. This research presents fishermen’s views on the significant causes and risk factors of fish poisoning in different regions of Fiji: Rakiraki, Tavua, Sonaisaili Island, Gua Island, Nandi, Lautoka, Suva. The study used a computational intelligence-based (CI), data mining approach to elucidate the fishermen’s views. Association rule mining (ARM) was then utilised to find correlations and associations between these views and to determine the perceived main causes of fish poisoning. The aim of this research was to explore the efficacy of an ARM approach (and the development of an appropriate database) in determining the common views of expert fishermen on the causes and risk factors that contribute to fish poisoning. The results indicated that the fishermen held common beliefs of causal relationships between various environmental factors and fish poisoning. These included contaminated passageways of fish, velella areas, water vehicle pollution, water temperatures, dirty and polluted water, heavy rains and flooded waters, the summer season, algal blooms, deep-sea environments and irresponsible human activities. Conceivably, the outcomes of this study may aid designing fish poisoning diagnostic systems (including early diagnosis of poisoning in fish) and through reducing or preventing predicating factors in the first instance.
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