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解释生成对抗网络应用对深度学习模型性能对甲度预测在流使用可解释的人工智能的效果

Jungsu Park¹, Woo Hyoung Lee², Ilsuk Kang³

¹Department of Civil and Environmental Engineering, Hanbat National University, Dongseo-daero, Republic of Korea.

Water environment research : a research publication of the Water Environment Federation

|December 23, 2025

概括

像生成对抗网络 (GANs) 这样的生成人工智能 (AI) 模型可以创建合成数据以改进藻类繁殖预测模型. 这项研究表明,GAN生成的数据有意义地影响了模型性能,为更好的水质管理提供了潜力.

科学领域:

环境科学环境科学
人工智能的人工智能
水质管理水质管理

背景情况:

预测藻类开花对于水质管理至关重要.
数据驱动模型,特别是深度学习,显示出希望,但需要广泛的,高质量的数据.
收集真实环境数据往往是昂贵和耗时的.

研究的目的:

为了研究由时间序列生成的合成数据的影响生成的对抗网络 (GAN) 在长期短期记忆 (LSTM) 网络的性能,以预测藻类繁殖.
通过知识蒸,将仅在真实数据 (LSTM_REAL) 上训练的模型与包含GAN生成数据 (LSTM_GAN) 的模型进行比较.
评估不同输入序列长度对模型性能的影响.

主要方法:

使用时间序列GAN生成合成数据.
利用长短期记忆 (LSTM) 网络进行时间序列预测.
我们比较了两个场景:LSTM_REAL (只有真实数据) 和LSTM_GAN (真实+GAN数据).
分析了从3到18的输入序列长度.
应用沙普利值分析来量化GAN生成数据的重要性.

主要成果:

序列长度为6的LSTM_GAN模型获得了最佳性能 (NSE为0.802).
性能因序列长度而异;LSTM_GAN显示在3-12长度上比LSTM_REAL有所改善,但在15-18长度上有所恶化.
沙普利价值分析表明,GAN生成的数据对变量重要性贡献了14.5%-24.3%,表明其对模型推断的影响.
虽然GAN数据对整体性能的影响很小,但它对模型理解的贡献很大.

结论:

生成对抗网络 (GAN) 算法具有通过提供有价值的合成数据来增强藻类繁殖预测模型的潜力.
由GAN生成的数据可以有意义地影响深度学习模型的内部推理过程,即使整体性能增长很小.
对优化GAN数据集成和序列长度选择的进一步研究是有必要的,以通过先进的人工智能来改善水质管理.

关键词:

藻类的开花藻类的开花可解释的人工智能 (XAI)生成性对抗性网络 (GAN) 是一种产生性对抗性网络.知识的蒸知识的蒸.长时间的短期记忆 (LSTM)水的质量水的质量.

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