Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Jumin, Ellysia; Zaini, Nur’atiah; Ahmed, Ali Najah; Abdullah, Samsuri; Ismail, Marzuki; Sherif, Mohsen; Sefelnasr, Ahmed; El-Shafie, Ahmed

doi:10.1080/19942060.2020.1758792

Cited by 63 publications

(52 citation statements)

References 31 publications

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“…In this study, four different machine learning algorithms, namely multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and boosted decision tree (BDT), have been proposed to predict the changes in water quality parameters. More details about these models can be seen in (Choi et al 2018 ; Lai et al 2019 ; Jumin et al 2020 ; Muslim et al 2020 ). After developing the above-mentioned techniques, the performance of these techniques was evaluated based on comparing the actual and the predicted data from each technique and based on the relative error percent, which can measure the discrepancy between the predicted and the observed data (Fiyadh et al 2019 ): …”

Section: Methodsmentioning

confidence: 99%

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Najah

Teo

Fai

et al. 2021

Int. J. Environ. Sci. Technol.

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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.

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Section: Methodsmentioning

confidence: 99%

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Najah

Teo

Fai

et al. 2021

Int. J. Environ. Sci. Technol.

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“…Figure 2. The architecture approach and process of neural network regression [22] Neural network simulators are computer programs that model neural network behavior in artificial or biological ways based on one or a few specific types of neural networks. Typically, they are independent and are not intended to build a general neural network that can be integrated into other applications.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

Latif¹,

Azmi²,

Ahmed³

et al. 2020

IJDNE

Self Cite

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Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

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“…The approaches considered were the sparse model for linear regression estimation (Predictor 1) and Random Forest Decision Tree (Predictor 2) due to their various benefits mentioned in numerous researches. [15][16][17] Predictor 1-Linear regression is a statistical approach where a target function is linear and depends on features as the input. All input attributes as real numbers and represented as:…”

Section: Mathematical Modeling Of the Machine Learning Algorithmmentioning

confidence: 99%

Machine learning predictor for micro gas turbine performance evaluation

Jayachandran¹,

Omar²,

Tkachenko³

et al. 2020

AAOAJ

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Due to the increased trends and innovation with reliable IoT sensors, high-speed networks, and supercomputers the amount of data solving has improved in predicting performance with greater accuracy. Machine learning-based diagnostics and health monitoring for large gas turbines are available but lack application in Micro gas turbine component design and performance prediction. In this paper to predict the optimized net efficiency using a machine learning-based model was developed. Micro gas turbine power plant mathematical model (MGTPPMM) was developed that could generate an electric power of 6kW which was validated using the operational conditions of Jet Cat PHT3. The resulting data was used to optimize and predict the net efficiency of gas turbines and also its compressor and turbine characteristics using a Machine learning algorithm model (MLAM). The MGTPPMM has an accuracy of 92% in simulating various operating conditions while two MLAM was developed Predictor 1 and Predictor 2. Predictor 1 has an accuracy of 76% and Predictor 2 has an accuracy of just less than1 % error at sea level operating conditions. However at higher altitudes greater than 17 Km operating condition the accuracy of the MGTPPMM is 88% while that of the MLAM -Predictor 2 has an error of less than 6% this is due to the lack of sufficient validated results data required for training sets. The proposed models may help to design more efficient micro gas turbines at the component level and also could predict performance characteristics to monitor the health of the system virtually by implementing a degradation monitor system.

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Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Cited by 63 publications

References 31 publications

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

Machine learning predictor for micro gas turbine performance evaluation

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