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基于具有自我注意神经网络的多场景气候模型,预测中国的自然排水情况

Naixin Hu¹, Kai Shu¹, Yuezheng Zhang¹

¹School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, PR China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, Harbin Institute of Technology, Harbin 150090, PR China.

|May 8, 2025

概括

气候变化影响了中国的自然排放,模型预测增加,特别是在高排放的情况下. 这项研究增强了水资源风险评估和适应策略.

科学领域:

水文和气候科学水文和气候科学
环境建模中的人工智能
水资源管理水资源管理

背景情况:

气候变化显著改变了全球的水循环,需要强大的模型来预测对地表自然排水的影响.
评估气候变化对水资源的影响对于提高对水相关风险的抵御能力至关重要.

研究的目的:

开发和应用一个自我注意的人工神经网络 (SAANN) 来量化气候变化对中国自然排水的影响.
提高水文模型的可解释性和可靠性,使用自我注意机制.
根据不同的排放情景,预测中国未来的自然排放趋势,并确定关键的驱动因素.

主要方法:

开发了一种自我注意人工神经网络 (SAANN),用于非顺序的水文建模.
训练了SAANN模型,使用来自中国的气候,底层表面和自然排水数据 (2000-2018年).
采用贝叶斯优化用于超参数调整,并将SAANN性能与标准人工神经网络 (ANN) 相比较.

主要成果:

与ANN相比,SAANN表现出优异的预测性能,平均平方误差 (MSE) 减少了26.9%,与ANN相比.
该模型可视化了关键输入变量相关性,提高了可解释性,并与物理原理保持一致.
未来的预测表明,在SSP245和SSP585两种情景下,中国的自然流水总体上升,SSP585显示出更高的速度.
预计秋季排水量将大幅增加,北方盆地预计将比南方盆地增加更多.

结论:

该SAANN模型为气候流失关系建模提供了更好的准确性和可解释性.
预计自然排水量增加,特别是在秋季和北部地区,为中国的农业和水利管理带来了新的适应性挑战.
这些发现强调了需要采取积极的适应战略,以减轻气候变化相关的潜在水资源风险.

关键词:

气候变化气候变化气候变化可以解释性解释性多场景预测多场景预测自然的下水是自然下水.自我注意的神经网络

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