Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques

Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center. Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. The R script(s) for machine learning models can also be found at Code for machine learning classification model.R.本数据集包含来自长江（Yangtze River, YR）汉口站与汉江（Han River, HR）汉川站两处水文站的水文数据，具体包括日水位、流速与径流量。上述数据均由湖北省水文水资源中心提供。 此外，汉江流域的水质数据（包括总氮（TOTN）、总磷（TOTP）与水温）以及白河嘴、秦渡口、宗关三个断面的藻类密度数据，均由长江流域生态环境监督管理局采集提供。 本数据集还配套提供了机器学习模型所用的R脚本，相关代码存于《机器学习分类模型代码.R》文件中。