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Thailand Raw Water Quality Dataset Analysis and Evaluation

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jaturapith Krohkaew & Pongpon Nilaphruek & Niti Witthayawiroj & Sakchai Uapipatanakul & Yamin Thwe & Padma Nyoman Crisnapati, 2023. "Thailand Raw Water Quality Dataset Analysis and Evaluation," Data, MDPI, vol. 8(9), pages 1-18, September.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:9:p:141-:d:1232825
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    References listed on IDEAS

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    1. Li, Fan & Zhou, Tao, 2020. "Effects of objective and subjective environmental pollution on well-being in urban China: A structural equation model approach," Social Science & Medicine, Elsevier, vol. 249(C).
    2. Lucas Pereira & Vitor Aguiar & Fábio Vasconcelos, 2021. "FIKWater: A Water Consumption Dataset from Three Restaurant Kitchens in Portugal," Data, MDPI, vol. 6(3), pages 1-10, March.
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    Cited by:

    1. Silvia Bossi & Luciano Blasi & Giacomo Cupertino & Ramiro dell’Erba & Angelo Cipollini & Saverio De Vito & Marco Santoro & Girolamo Di Francia & Giuseppe Marco Tina, 2024. "Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies," Sustainability, MDPI, vol. 16(19), pages 1-26, September.

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