Raw Water Quality Dataset Thailand

Sustainable water quality data is important for understanding historical variability and trends in river regimes, as well as the impact of industrial waste on the health of aquatic ecosystems. Sustainable water management practices heavily depend on reliable and comprehensive data, prompting the need for accurate monitoring and assessment of water quality parameters. We describe a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. We used Internet of Things technology to reconstruct the water quality dataset for the period June 2022 – February 2023, with Turbidity, Optical Dissolved Oxygen, Dissolved Oxygen Saturation, Spatial Conductivity, Acidity/Basicity, Total Dissolved Solids, Salinity, Temperature, Chlorophyll, and Depth as the recorded parameters from six different stations. The presented dataset comprises a total of 211,322 data points, which are separated into six CSV files. The dataset is then evaluated using the Long Short-Term Memory (LSTM) algorithm. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices

可持续水质数据对于明晰河流径流的历史波动规律与演变趋势，以及工业废弃物对水生生态系统健康的影响均具有重要意义。可持续水资源管理实践高度依赖可靠且全面的数据集，这催生了对水质参数开展精准监测与评估的迫切需求。本研究介绍了一套重构的逐日水质数据集，用于补充泰国湄南河沿岸6个监测站点的稀缺历史观测资料。我们借助物联网（Internet of Things, IoT）技术，重构了2022年6月至2023年2月期间的水质数据集，共收录浊度、光学溶解氧、溶解氧饱和度、空间电导率、酸碱度、总溶解固体、盐度、温度、叶绿素含量以及水深共10项监测参数，数据来自6个独立监测站点。本数据集总计包含211322条有效数据点，被划分为6个逗号分隔值（Comma-Separated Values, CSV）文件。随后我们采用长短期记忆网络（Long Short-Term Memory, LSTM）算法对该数据集进行了评估。本数据集可为河流生态系统相关研究的科研人员提供宝贵参考，为科学决策与可持续水资源管理实践提供支撑。