Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach
“… Fig. 1 a Location of Klang River basin and b The stations of water quality monitoring (Rezaie-Balf et al 2020 ) …”
Section: Methodsmentioning
confidence: 99%
“…Water is the key natural factor for human survival and social growth as well as for ecological (i.e., natural, biological, and environmental) health (Rezaie-Balf et al 2020 ). Whether drinking or personal hygiene, water is the fundamental element for manufacturing, agricultural, and biotransformation purposes.…”
Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.
“… Fig. 1 a Location of Klang River basin and b The stations of water quality monitoring (Rezaie-Balf et al 2020 ) …”
Section: Methodsmentioning
confidence: 99%
“…Water is the key natural factor for human survival and social growth as well as for ecological (i.e., natural, biological, and environmental) health (Rezaie-Balf et al 2020 ). Whether drinking or personal hygiene, water is the fundamental element for manufacturing, agricultural, and biotransformation purposes.…”
Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.
“…For example, devastating flooding occurred in the Federal Capital of Kuala Lumpur in 1971, the worst flooding event in Malaysia in recent times. The Klang Gates Dam is an example of a dam built to supply water and as a flood mitigation dam [28]. Figure 1 shows the Klang Gates Dam and Klang River location, and Data on the water demand/supply of Klang Dam; iii.…”
Section: Study Area and Data Acquisitionmentioning
In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.
“…Predicting the class Water Quality Index (W.Q.I.) has been carried out using different ML algorithms by many researchers 22 – 24 . Artificial Neural Network (ANN) has been used to predict total nitrogen and phosphorus in the United States (U.S.) lakes 25 .…”
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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