Application of Machine Learning Time Series Analysis for Prediction of Human Future in Covid-19

2022 ◽  
pp. 340-351
Author(s):  
Subasish Mohapatra ◽  
Aditya Bisoi ◽  
Subhadarshini Mohanty ◽  
Jijnasee Dash ◽  
Subhangi Choudhary
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Learning Time ◽  
Series Analysis

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

IEEE Access ◽  
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

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

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1342 ◽  
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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