Assoc. Prof. Ir. Ts. Gs. Dr. Chow Ming FaiAssoc. Prof.
School of Engineering Monash University Malaysia Ir. Ts. Gs. Dr. Chow Ming Fai is an Associate Professor in the Civil Engineering Discipline of the School of Engineering, Monash University Malaysia. Dr. Chow completed his bachelor degree of civil engineering and PhD degree from University Technology Malaysia (UTM) in 2007 and 2012, respectively. Since then, he has worked in Academia Sinica, Taiwan and Universiti Tenaga Nasional (UNITEN). His research interests are mainly focused on Sustainable urban stormwater management and flood simulation. He had obtained the professional engineer (Ir.), chartered engineer (CEng.), professional technologist (Ts) and professional geospatialist recognitions. He has been involved in many consultancy projects with clients from TNB, TNBR, Department of Irrigation and Drainage Malaysia (DID) and Lembaga Kemajuan Pertanian Muda (MADA).
|
|
Topic for the conference…
"An efficient strategy for predicting dissolved oxygen concentration in reservoir: application of artificial neural network model"
Abstract. Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the drinking water supply reservoir. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the 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.
Keywords: Artificial neural network; dissolved oxygen; machine learning model; water supply reservoir
Keywords: Artificial neural network; dissolved oxygen; machine learning model; water supply reservoir
