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预测和元特征估计沿海地区的废水和气候变化影响,使用多重学习
E B Priyanka1, S Vivek2, S Thangavel1
1Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, 638060, India.
Environmental research
|October 20, 2023
概括
气候变化导致南亚海平面上升和沿海洪水. 一个新的预测平台使用数字化和多重学习来预测气候和废水的影响,在减少不确定性方面达到91.45%的准确性.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 水资源管理 水资源管理
背景情况:
- 南亚的海岸线人口密且经济重要,面临气候变化带来的重大威胁,包括海平面上升和沿海洪水增加.
- 河口水质受季节性和年度流量变化的影响,需要先进的评估工具.
- 气候变化对沿海社区的影响需要数字化分析平台,以减少风险和有效地规划废物管理.
研究的目的:
- 开发一个预测平台,用于评估气候变化和废水对印度南部沿海地区的影响.
- 加强对沿海脆弱性未来变化的预测,并为废物规划决策提供信息.
- 整合多模型组合和多元学习,以改善不确定性评估.
主要方法:
- 开发一个数字化分析平台,结合多模型组合和多重学习.
- 实施统计偏差校正和元特征估计,以解决多重学习分析偏差的问题.
- 分析气候-水文模型链,以预测水资源变化.
主要成果:
- 该研究通过数字化和多重学习实现了23%的不确定性概率率,在预防措施下被认为是可以忽略不计的.
- 偏差校正和元特征估计提高了多重学习分析在废水管理中的适用性.
- 气候-水文学建模表明,不同地点的水资源存在显著差异.
结论:
- 开发的预测平台有效评估了沿海地区的气候变化和废水影响.
- 91.45%的预测准确率被认为是减少气候变化和废水管理的不确定性最佳的.
- 数字化分析平台对于缓解脆弱沿海地区气候变化风险至关重要.
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