预测伊斯坦布尔的医疗废物使用一种新的非线性灰色伯努利模型,由火虫算法优化
Aziz Kemal Konyalıoğlu1,2, Tuncay Ozcan2, Ilke Bereketli3
1Hunter Centre for Entrepreneurship, Strathclyde Business School, University of Strathclyde, Glasgow, UK.
概括
在伊斯坦布尔,准确的医疗废物预测对于公共卫生和环境安全至关重要. 一个新的混合模型,FA-NGBM{1,1),显著提高了医疗废物管理的预测准确性.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 全球废物管理至关重要,COVID-19相关的医疗废物问题加剧了这一问题.
- 伊斯坦布尔在管理不断增加的医疗废物量方面面临重大挑战.
- 准确的医疗废物量估计对于有效的资源规划至关重要.
研究的目的:
- 通过一种新的混合模型,估计伊斯坦布尔的医疗废物量.
- 引入和验证火算法优化的非线性灰色伯努利模型 (FA-NGBM(1,1)).
- 将拟议模型的性能与现有的预测算法进行比较.
主要方法:
- 开发FA-NGBM的混合型号,包括滚动机制和参数优化.
- 与经典GM{1,1},FA-GM{1,1},FA-FGM{1,1}和线性回归模型进行比较分析.
- 使用测试和验证数据集进行验证,以评估预测准确性.
主要成果:
- 在FA-NGBM(1,1) 模型中,测试数据的平均绝对百分比误差 (MAPE) 为3.47%,验证数据为2.57%.
- 拟议的混合模型与所有其他评估的预测算法相比,显示出更高的准确性.
- 预测显示,在未来三年内,伊斯坦布尔的医疗废物将大幅增加.
结论:
- 该FA-NGBM(1,1) 模型提供了一个非常准确和可靠的方法来估计医疗废物量.
- 该研究强调,伊斯坦布尔迫切需要加强废物管理政策.
- 建议决策者采取主动措施,以应对医疗废物预计的增加.
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