Water quality time series datasets for multi-step dissolved oxygen forecasting at two tributary monitoring stations in the Yangtze River Basin

Description

This dataset supports the manuscript entitled “Multi-step dissolved oxygen prediction based on seasonal-trend decomposition and dual-branch global temporal modelling”. The dataset contains water quality time series from two automatic water quality monitoring stations in the middle and lower reaches of the Yangtze River Basin, China. Dataset A was collected from the Hannan Village site on the Han River, and Dataset B was obtained from the Liangtian Village site on the Jin River. The records cover the period from January 2021 to April 2024 at 4-hour intervals. The monitored variables include water temperature (WT), pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH4+-N), total phosphorus (TP), total nitrogen (TN), electrical conductivity (EC), and turbidity (NTU). These datasets were used to evaluate multi-step dissolved oxygen forecasting under distinct water quality dynamics.

Steps to reproduce

The two CSV files contain preprocessed water quality time series from two automatic monitoring stations. Dataset A corresponds to the Hannan Village site on the Han River, and Dataset B corresponds to the Liangtian Village site on the Jin River. The records cover the period from January 2021 to April 2024 at 4-hour intervals. To reproduce the data used in the manuscript, download the CSV files and read them using any standard data analysis software. The dissolved oxygen (DO) column is the forecasting target, while the remaining physicochemical parameters are used as input variables. The monitored variables include water temperature, pH, DO, chemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, electrical conductivity, and turbidity. The data were compiled from the automatic water quality monitoring system of the China National Environmental Monitoring Centre and preprocessed for multi-step dissolved oxygen forecasting experiments. Users should follow the data split and forecasting horizons described in the manuscript when reproducing the model evaluation.

Institutions

• Central South University

  Hunan, Changsha

Categories

Funders

• Department of Science and Technology of Hunan Province

  Hunan, Changsha

  Grant ID: No. 2024JJ5447

Licence