Synthetic Hydrology Dataset

该数据集是一个综合性的数据集，旨在根据观察到的环境条件，如温度和降水，来预测河流流量，并专注于建立因果关系模型。该数据集包含10个与水文循环相关的节点，重点捕捉它们之间的因果关系。实验采用深度学习方法，尤其是自动编码器，来处理复杂的相互关系和时间动态。该数据集在处理高维特征表征方面具有规模优势，其任务是利用环境条件来预测河流流量。

This is a comprehensive dataset designed to predict river flow based on observed environmental conditions such as temperature and precipitation, with a primary focus on modeling causal relationships. The dataset encompasses 10 nodes associated with the hydrological cycle, prioritizing the capture of causal correlations among these nodes. For experiments, deep learning approaches—especially autoencoders—are utilized to address complex interrelationships and temporal dynamics. This dataset offers scalability advantages in processing high-dimensional feature representations, with its core task being to predict river flow using environmental conditions.