Current hydrological modeling methods combine data-driven Machine Learning (ML) algorithms and traditional physics-based models to address their respective limitations incorrect parameter estimates from rigid physics-based models and the neglect of physical process constraints by ML algorithms. Despite the accuracy of ML in outcome prediction, the integration of scientific knowledge is crucial for reliable predictions. This study introduces a Physics Informed Machine Learning (PIML) model, which merges the process understanding of conceptual hydrological models with the predictive efficiency of ML algorithms. Applied to the Anandapur sub-catchment, the PIML model demonstrates superior performance in forecasting monthly streamflow and actual evapotranspiration over both standalone conceptual models and ML algorithms, ensuring physical consistency of the outputs. This study replicates the methodologies of Bhasme, P., Vagadiya, J., & Bhatia, U. (2022) from their pivotal work on Physics Informed Machine Learning for hydrological processes, utilizing their shared code and datasets to further explore the predictive capabilities in hydrological modeling.

翻译：当前水文建模方法结合了数据驱动的机器学习（ML）算法和传统物理模型，以弥补各自在刚性物理模型参数估计不准确与ML算法忽视物理过程约束等方面的局限性。尽管ML在结果预测方面具有准确性，但科学知识的整合对于可靠预测至关重要。本研究引入了一种物理信息机器学习（PIML）模型，该模型融合了概念性水文模型的过程理解与ML算法的预测效率。应用于Anandapur子流域时，PIML模型在预测月径流和实际蒸散发方面均优于独立的概念模型和ML算法，并确保了输出的物理一致性。本研究复现了Bhasme, P.、Vagadiya, J.与Bhatia, U.（2022）关于水文过程物理信息机器学习的开创性研究方法，利用其共享代码和数据集进一步探索了水文建模中的预测能力。