The time series technique for aerosol retrievals over land from MODIS

Author(s):  
Alexei Lyapustin ◽  
Yujie Wang
Keyword(s):  
2022 ◽  
pp. 29-38
Author(s):  
Abd El-Moneim A. M. Teamah ◽  
Hasnaa M. Faied ◽  
Mohammed H. El-Menshawy

Author(s):  
Hesham A. Ali ◽  
Neville A. Parker

Analysis of the seasonal monitoring program data of the long term pavement performance program indicated that some pavement structural properties often follow predictable seasonal patterns. Time series is a statistical technique that may be used to develop periodic functions to predict the values of such properties as a function of time. The application of time series technique in characterizing the seasonal variations of pavement structural properties as simulated functions is presented. In addition, the incorporation of such variations in both empirical and mechanistic-empirical methods of flexible pavement design is demonstrated. To this end, a computer program, seasonal variation in pavement design, was written to carry out the required calculations and to facilitate the comparison between empirical and mechanistic-empirical design methods.


Author(s):  
Alhassan Mohammed ◽  
Muhammad Bashir Mu’azu ◽  
Yusuf Abubakar Sha’aban ◽  
Shehu Mohammed Yusuf ◽  
Salawudeen Ahmed Tijani ◽  
...  

2018 ◽  
Vol 19 (2) ◽  
pp. 151-170
Author(s):  
Abd El-Moneim A. M. Teamah ◽  
◽  
Hasnaa M. Faied ◽  
Mohammed H. El-Menshawy ◽  
◽  
...  

Author(s):  
Sai Manoj Cheruvu

Abstract: Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant


Author(s):  
P. A. Euillades ◽  
L. E. Euillades ◽  
P. Rosell ◽  
Y. Roa

Abstract. The city of Maceió has been historically affected by cracks and sink events in buildings and city infrastructure. Availability of a consistent Sentinel 1 Mission dataset between 2014 and 2019 allows characterizing the undergoing crustal deformation process that provokes such effects. We processed a dataset of 81 SAR scenes using the DInSAR-SBAS time-series technique, which allowed us to obtain mean velocity of deformation and deformation time series. Detected displacement patterns show subsidence concentrated in the Mundaú lagoon coast in front of Mutange, Pinheiro and Levada neighbourhoods. Inversion of the results, using analytical models, locates a sill-like source at ∼400 m depth and with a radius of ∼0.8 km. Its depth would be compatible with re-activation of the Mutange fault system, possibly related to salt mining operations in the area. Further investigation is needed to better constrain the deformation source and to identify if the observed process was active before the analysed time span.


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