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Zhou, P., Sang, H., Jin, L. and Lee, W.J. (2017) Application of Statistical Methods to Predict Production from Liquid-Rich Shale Reservoirs. Proceedings of the 5th Unconventional Resources Technology Conference, Austin, 24-26 July 2017, 2999-3017.
https://doi.org/10.15530/urtec-2017-2694668

has been cited by the following article:

• TITLE: Unsupervised Classification of Sea Surface Temperature (SST) in the Tropical Atlantic Using Spatial and Functional Data Analysis

  AUTHORS: Ogoudjobi François Adjibode

  KEYWORDS: Clustering, Unsupervised Classification, Functional Data, Spatio-Temporal Data, Climate Change, SST

  JOURNAL NAME: Applied Mathematics, Vol.16 No.6, June 26, 2025

  ABSTRACT: In this study, we employ a spatial unsupervised classification technique to analyze the spatio-temporal variability of Sea Surface Temperature (SST) in the tropical African zone. The methodology we propose considers both the spatial dimensions of the data and their functional characteristics, distinguishing it from conventional approaches. The results demonstrate noteworthy fluctuations in SST across spatial and temporal scales. This variability signifies a detected anomaly in SST within the study area, which can be attributed to the impacts of climate change.