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

Balahaha Hadi Ziyad Sami,Balahaha Fadi Ziyad Sami,Sarmad Dashti Latif,Muhammad Ary Murti,Asep Suhendi,Ming Fai Chow,Ahmed El-Shafie,Ahmed H Birima,Ali Najah Ahmed,Jee Khai Wong

doi:10.1038/s41598-022-06969-z

Abstract

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.

Highlights

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
One of the main characteristics of defining artificial neural network models is choosing the number of neurons for the hidden layer
The study focuses on predicting dissolved oxygen concentration as crucial water quality parameters in the FeiTsui reservoir in Taiwan using an artificial neural network model with simple architecture

Summary

Introduction

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. 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. Among all the water quality parameters, the Dissolved Oxygen (D.O.) is considered the most representative parameter that showed the class’s water quality status, especially in surface water This is due to the fact that D.O. is vital for the aquatic organisms and fish in the water bodies. Inspired by the robust performance of ML in capturing the nonlinearity patterns in most of the engineering systems, different algorithms of ML have been adopted to predict the water quality parameters. These drawbacks are that it is not very accurate and cannot find the best parameters; it is prone to get stuck in a local minimum, contributing to its lack of prediction abilities

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Conclusion