Thailand Raw Water Quality Dataset Analysis and Evaluation

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.

Developing a smart system for infant incubators using the internet of things and artificial intelligence

“…Evaluation techniques used in this study include root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R 2 ). RMSE measures the difference between the predicted value generated by the model and the actual value, as shown in (10) [42], [43]. Meanwhile, MAE is similar to RMSE, but the difference lies in its tolerance for outliers, which is stated in (11) [42], [43].…”

“…LSTM are a particular type of recurrent neural networks (RNN) designed to learn and understand long-term dependencies in data. Through the analysis performed by the LSTM model, certain patterns in the data can be identified and predicted [9], [10]. Therefore, this study uses LSTM to analyze data from various sensors in the incubator collected in a database.…”

Enhancing Neonatal Incubator Energy Management and Monitoring through IoT-Enabled CNN-LSTM Combination Predictive Model

“…The IoT has been widely adopted in various aspects of life to enhance daily living, even within the industrial sector [2]. The IoT comprises hardware, networking, and software components [3,4]. In this research, IoT technology was applied in healthcare, specifically in neonatal incubators.…”