灵敏度(控制系统)
水质
人工神经网络
可靠性(半导体)
计算机科学
质量(理念)
电流(流体)
算法
机器学习
数据挖掘
人工智能
工程类
电气工程
功率(物理)
哲学
物理
认识论
生物
量子力学
电子工程
生态学
作者
Balahaha Fadi Ziyad Sami,Sarmad Dashti Latif,Ali Najah Ahmed,Ming Fai Chow,Muhammad Ary Murti,Asep Suhendi,Balahaha Hadi Ziyad Sami,Jee Khai Wong,Ahmed H. Birima,Ahmed El‐Shafie
标识
DOI:10.1038/s41598-022-06969-z
摘要
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.
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