Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan

• Published in: Scientific reports Vol. 12; no. 1; p. 3649
• Main Authors: Ziyad Sami, Balahaha Fadi, Latif, Sarmad Dashti, Ahmed, Ali Najah, Chow, Ming Fai, Murti, Muhammad Ary, Suhendi, Asep, Ziyad Sami, Balahaha Hadi, Wong, Jee Khai, Birima, Ahmed H., El-Shafie, Ahmed
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.03.2022; Nature Portfolio
• Subjects: 704/242; 704/4111; Algorithms; Environmental Monitoring - methods; Humanities and Social Sciences; Machine Learning; multidisciplinary; Oxygen - analysis; Reproducibility of Results; Science; Science (multidisciplinary); Taiwan; Water Quality; Taiwan
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-06969-z

Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it’s the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs.