Prediction of Carbon Monoxide (CO) Atmospheric Pollution Concentrations with Machine Learning and Time Series Analysis in Langkawi, Malaysia

Carbon monoxide (CO) is a non-irritant toxic and odourless gas produced from the incomplete combustion of fossil fuels. Long-term exposures to lower levels of carbon monoxide have wide implications for human health. Thus, an early warning system for CO atmospheric concentration with an accurate and reliable forecasting method is crucial. Studies for predicting CO atmospheric concentration are still limited in Malaysia especially using data science approaches. This study aims to develop and predict future CO concentration for the next few hours by using the statistical time series approach and machine learning approach. The data used for the project is the air quality data of the monitoring station in Langkawi, Malaysia. The data mining tool used for this project is RapidMiner Studio. Based on the results, it showed that Time Series analysis with deep learning gave a reasonably good CO concentration prediction for the next 3 hours with a relative error of approximate 10%. The model developed in this project can be used by authorities as public health’s protection measure to provide an early alarm for alerting the Malaysian populations on the air pollution issue.

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A Machine Learning Approach to Biodiversity Time Series Analysis

SSRN Electronic Journal ◽

10.2139/ssrn.3520735 ◽

2020 ◽

Author(s):

Rajarshi Paul ◽

Th. Shanta Kumar

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Analysis ◽

Learning Approach ◽

Series Analysis ◽

Machine Learning Approach

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Time Series-Analysis Based Engineering of High-Dimensional Wide-Area Stability Indices for Machine Learning

IEEE Access ◽

10.1109/access.2021.3099459 ◽

2021 ◽

pp. 1-1

Author(s):

Raoult Teukam Dabou ◽

Innocent Kamwa ◽

C. Y. Chung ◽

C. F. Mugombozi

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Analysis ◽

Wide Area ◽

High Dimensional ◽

Series Analysis ◽

Stability Indices

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Predicting the New Cases of Coronavirus [COVID-19] in India by Using Time Series Analysis as Machine Learning Model in Python

Journal of The Institution of Engineers (India) Series B ◽

10.1007/s40031-021-00546-0 ◽

2021 ◽

Author(s):

Vikas Kulshreshtha ◽

N. K. Garg

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Analysis ◽

Learning Model ◽

Series Analysis ◽

Machine Learning Model

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Machine Learning Methods for Improved Understanding of a Pumping Test in Heterogeneous Aquifers

Water ◽

10.3390/w12051342 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1342 ◽

Cited By ~ 1

Author(s):

Yong Fan ◽

Litang Hu ◽

Hongliang Wang ◽

Xin Liu

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Analysis ◽

Analytical Solutions ◽

Pumping Test ◽

Learning Methods ◽

Pumping Tests ◽

Machine Learning Methods ◽

Series Analysis ◽

Pumping Wells

Pumping tests are very important means for investigating aquifer properties; however, interpreting the data using common analytical solutions become invalid in complex aquifer systems. The paper aims to explore the potential of machine learning methods in retrieving the pumping tests information in a field site in the Democratic Republic of Congo. A newly planned mining site with a pumping test of three pumping wells and 28 observation wells over one month was chosen to analyze the significance of machine learning methods in the pumping test analysis. Widely used machine learning methods, including correlation, cluster, time-series analysis, artificial neural network (ANN), support vector machine (SVR), random forest (RF) method, and linear regression, are all used in this study. Correlation and cluster analyses among wells provide visual pictures of possible hydraulic connections. The pathway with the best permeability ranges from the depth of 250 m to 350 m. Time-series analysis perfectly captured changes of drawdowns within the three pumping wells. The RF method is found to have the higher accuracy and the lower sensitivity to model parameters than ANN and SVR methods. The coupling of the linear regressive model and analytical solutions is applied to estimate hydraulic conductivities. The results found that ML methods can significantly and effectively improve our understanding of pumping tests by revealing inherent information hidden in those tests.

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