AI 中文总结
本文以黑河流域中游为研究区，提出六项负责任AI原则，开发LSTM与Transformer模型并验证，发现Transformer表现更优，证明负责任AI原则可用于地下水预测以支撑水资源可持续管理。
AI 中文摘要
人工智能（AI）的快速发展与广泛应用，引发了关于如何部署符合人类价值观和伦理标准的负责任AI系统的激烈讨论。与医疗、能源或金融等领域相比，AI在地下水领域的应用相对有限，而负责任AI的研究更为匮乏。本文以黑河流域中游为研究区域，提出六项负责任AI原则：透明性、技术鲁棒性、隐私治理、公平性、问责制与可持续性。利用多源水文气象数据开发LSTM与Transformer时间序列模型，通过事后可解释性分析、蒙特卡洛模拟及情景分析进行验证。结果显示，Transformer在准确性、鲁棒性与可解释性方面优于LSTM，证明负责任AI原则在地下水预测中具有可操作性与实用价值，可支持气候变化和人类活动下的水资源可持续管理。
英文摘要
The rapid development and widespread application of artificial intelligence (AI) have sparked intense discussions on how to deploy responsible AI systems in a manner aligned with human values and ethical standards. Compared to fields like healthcare, energy, or finance, the application of AI in groundwater is relatively limited, and research on responsible AI is even more scarce. Taking the middle reaches of the Heihe River Basin as the study area, this paper proposes six Responsible AI principles: transparency, technical robustness, privacy governance, fairness, accountability, and sustainability. LSTM and Transformer time-series models are developed using multi-source hydrometeorological data, and validated via post-hoc interpretability, Monte Carlo simulation, and scenario analysis. The results show that Transformer outperforms LSTM in accuracy, robustness, and interpretability, demonstrating the operability and practical value of Responsible AI principles in groundwater prediction to support sustainable water management under climate change and human activities.
