平衡水产养殖和河口生态系统:基于机器学习的水质指数,用于有效管理
Sri Bala Gottumukkala1,2, Vamsi Nagaraju Thotakura3,4, Srinivasa Rao Gvr5
1Department of Civil Engineering, S.R.K.R Engineering College, Bhimavaram, India.
概括
这项研究评估了内陆水产养殖对南印度河口水质的影响. 像多线性回归 (MLR) 和支持向量回归器 (SVR) 这样的机器学习模型准确地预测了水质指数,突出了生物化学氧气需求 (BOD) 作为关键指标.
科学领域:
- 环境科学 环境科学
- 水生生态学 水生生态学
- 水资源管理 水资源管理
背景情况:
- 南印度的内陆水产养殖对河口生态系统产生重大影响.
- 在这些密集耕种的地区,水质恶化是一个主要问题.
研究的目的:
- 评估水产养殖对河口水质的环境影响.
- 评估高斯消除方法 (GEM),多线性回归 (MLR) 和支持矢量回归器 (SVR) 对水质指数 (WQI) 的预测精度.
主要方法:
- 收集了南印度水产养殖区四条河口河流的水质数据.
- 应用GEM,MLR和SVR模型来预测WQI.
- 使用确定系数 (R2) 和平均平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 与GEM相比,MLR和SVR模型显示出更高的预测效率.
- 将关键的水参数作为输入,大大提高了机器学习模型的性能.
- 生物化学氧气需求 (BOD) 被确定为MLR和SVR对WQI预测的关键参数.
结论:
- 在受水产养殖影响的河口系统中,MLR和SVR是预测WQI的有效工具.
- 建议在主动水质监测中优先考虑MLR和SVR与BOD等关键参数.
- 这种方法可以促进及时干预,以保护水产养殖区的河口健康.
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