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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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基于鱼优化算法 (OOA) 的湖泊污染物预测研究.

Yinshan Yu1, Xu Tang2, Mingjian Ding2

  • 1Jiangsu Engineering Research Center of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian, China. yuyinshan@163.com.

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本研究引入了一种 Osprey 优化算法 (OOA),以增强高斯过程回归 (GPR) 以准确预测湖泊污染物. 对OOA优化的模型显著提高了对水质参数的预测准确度.

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科学领域:

  • 环境科学 环境科学
  • 数据科学数据科学数据科学
  • 算法优化的算法优化

背景情况:

  • 准确测量湖水质量参数对于环境监测至关重要.
  • 在测量参数和污染物度之间存在复杂的非线性关系.
  • 现有的预测模型可能会受到低于最佳准确性的影响.

研究的目的:

  • 开发一种使用优化算法预测湖泊污染物的增强方法.
  • 提高高斯过程回归 (GPR) 的准确性和适应性,用于污染物预测.
  • 完善实时评估水质的方法方法.

主要方法:

  • 使用光谱光度测量测量六个水质参数 (COD,总,总).
  • 用高斯过程回归 (GPR) 来预测污染物度的应用.
  • 使用Osprey优化算法 (OOA) 优化GPR超参数.

主要成果:

  • 经过OOA增强的GPR模型实现了高预测准确度.
  • 在训练和测试数据集中,相关系数 (R^2) 超过了0.9.
  • 平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE) 的指标接近零.

结论:

  • Osprey优化算法有效地改进了用于污染物预测的GPR模型.
  • 改进后的模型在湖泊污染物预测方面表现出卓越的性能.
  • 这种方法为复杂的水质参数测量挑战提供了强大的解决方案.