Thailand Raw Water Quality Dataset Analysis and Evaluation

Jaturapith Krohkaew, Pongpon Nilaphruek (), Niti Witthayawiroj, Sakchai Uapipatanakul, Yamin Thwe and Padma Nyoman Crisnapati
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Jaturapith Krohkaew: Department of Big Data Management and Analytics, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand
Pongpon Nilaphruek: Department of Big Data Management and Analytics, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand
Niti Witthayawiroj: Department of Computer Science, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand
Sakchai Uapipatanakul: Kinetics Corporation Ltd., 388 Ratchadapisek Rd.32 Chandrakasem, Chatuchak, Bangkok 10900, Thailand
Yamin Thwe: Department of Data and Information Science, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand
Padma Nyoman Crisnapati: Department of Mechatronics Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand

Data, 2023, vol. 8, issue 9, 1-18

Abstract: Sustainable water quality data are 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. This research describes a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. Internet of Things technology and a Eureka water probe sensor is used to collect and reconstruct the water quality dataset for the period from 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 with a Mean Squared Error (MSE) of 0.0012256, and Root Mean Squared Error (RMSE) of 0.0350080. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices.

Keywords: water quality dataset; Internet of Things; real-time monitoring; metropolitan waterworks authority; Thailand (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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