Unveiling the Illusion of Successful Water Quality Governance Using Deep Learning
Year: 2025
Type: article
Abstract: This dataset contains explanatory variables and river water quality observations used in the study “Unveiling the Illusion of Successful Water Quality Governance Using Deep Learning.” Explanatory variables (Folder 01): Daily time series (2010–2023) of meteorological data (rainfall, temperature), socio-economic indicators (GDP, population density), and land-use categories (farmland, forest, grassland, water, settlements, unused land) for 86 sub-watersheds in the Pearl River Basin, China. Water quality data (Folder 02): Daily monitoring records (2010–2023) of dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), and total nitrogen (TN) from 86 sub-watersheds, harmonized and quality-controlled. All data are provided as CSV files with clear variable names and units, supporting reproducibility of the deep learning models and analyses presented in the manuscript.
Author: zheng, Hang
Institution: Dongguan University of Technology
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7081572246
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