使用物联网设备的养鱼池水的实时数据集
1Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh.
Data in brief
|December 11, 2023
概括
这项研究使用物联网传感器创建了用于养鱼的实时水质数据集. 机器学习确定了三个合适的池,随机森林是鱼类生存的最佳预测.
科学领域:
- 环境科学 环境科学
- 农业技术 农业技术
- 数据科学数据科学数据科学
背景情况:
- 水生环境监测对于可持续的养殖鱼类至关重要.
- 实时数据收集增强了对池动态的理解.
- 现有的数据集可能缺乏全面的实时水质参数.
研究的目的:
- 为渔业养殖引入一个新的实时水质数据集.
- 为数据采集建立物联网 (IoT) 框架.
- 评估用于预测鱼类适合性的机器学习算法.
主要方法:
- 部署了一个带有传感器 (pH,温度,度) 和Arduino微控制器的物联网框架.
- 数据来自孟加拉国贾马尔普尔区的五个鱼池.
- 通过使用10个机器学习算法 (包括Random Forest) 分析了40,280行数据集.
主要成果:
- 在5个池中,只有3个被确定为适合养鱼的池.
- 随机森林算法在预测方面展示了最高的性能.
- 该数据集包括11个不同的鱼类类别和关键的水质参数.
结论:
- 开发的物联网系统和数据集为养鱼管理提供了宝贵的见解.
- 机器学习,特别是随机森林,可以有效地预测池的适用性.
- 这种资源有助于研究人员和农民优化鱼类生存率.
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