Field data analysis and risk assessment of gas kick during industrial deepwater drilling process based on supervised learning algorithm

2021 ◽  
Vol 146 ◽  
pp. 312-328
Author(s):  
Qishuai Yin ◽  
Jin Yang ◽  
Mayank Tyagi ◽  
Xu Zhou ◽  
Xinxin Hou ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Data Analysis ◽  
Supervised Learning ◽  
Field Data ◽  
Learning Algorithm ◽  
Drilling Process ◽  
Deepwater Drilling ◽  
Gas Kick ◽  
Field Data Analysis

Navigational risk assessment of Three Gorges ship lock: Field data analysis using intelligent expert system

2020 ◽  
Vol 38 (2) ◽  
pp. 1197-1202
Author(s):  
Wei He ◽  
Xiumin Chu ◽  
Yumeng Zhou ◽  
Zhonglian Jiang ◽  
Zhen Yu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Data Analysis ◽  
Expert System ◽  
Field Data ◽  
Three Gorges ◽  
Field Data Analysis ◽  
Ship Lock

Effects of Flow Depth on Water Flow and Solute Transport in Furrow Irrigation: Field Data Analysis

2003 ◽  
Vol 129 (4) ◽  
pp. 237-246 ◽  
Author(s):  
Fariborz Abbasi ◽  
Floyd J. Adamsen ◽  
Douglas J. Hunsaker ◽  
Jan Feyen ◽  
Peter Shouse ◽  
...  
Keyword(s):  
Data Analysis ◽  
Solute Transport ◽  
Water Flow ◽  
Field Data ◽  
Furrow Irrigation ◽  
Flow Depth ◽  
Field Data Analysis

Field-data analysis and hydromechanical modeling of CO2 storage at In Salah, Algeria

2018 ◽  
Vol 79 ◽  
pp. 61-72 ◽  
Author(s):  
Tore Ingvald Bjørnarå ◽  
Bahman Bohloli ◽  
Joonsang Park
Keyword(s):  
Data Analysis ◽  
Field Data ◽  
Co2 Storage ◽  
Field Data Analysis

Spatial Algal Bloom Characterization by Landsat 8-Oli and Field Data Analysis

2018 ◽  
Author(s):  
Andrea Guachalla Alarcon ◽  
Alba German ◽  
Alejandro Aleksinko ◽  
Maria Fernanda Garcia Ferreyra ◽  
Carlos Marcelo Scavuzzo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Field Data ◽  
Algal Bloom ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Field Data Analysis

scholarly journals The Effects of Unbalanced Data on Field Data Analysis

1989 ◽  
Vol 60 (4) ◽  
pp. 364-371
Author(s):  
Takuro OIKAWA ◽  
Yukio YAMADA
Keyword(s):  
Data Analysis ◽  
Field Data ◽  
Unbalanced Data ◽  
Field Data Analysis

scholarly journals Machine Learning-Based Field Data Analysis and Modeling for Drone Communications

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 79127-79135 ◽  
Author(s):  
Lin Shan ◽  
Ryu Miura ◽  
Toshinori Kagawa ◽  
Fumie Ono ◽  
Huan-Bang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Field Data ◽  
Field Data Analysis

LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning

2020 ◽  
Vol 19 (01) ◽  
pp. 283-316 ◽  
Author(s):  
Luis Morales ◽  
José Aguilar ◽  
Danilo Chávez ◽  
Claudia Isaza
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
New Approach ◽  
Supervised And Unsupervised Learning ◽  
Training Stage

This paper proposes a new approach to improve the performance of Learning Algorithm for Multivariable Data Analysis (LAMDA). This algorithm can be used for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class, through the contributions of all its descriptors. LAMDA has the capability of creating new classes after the training stage. If an individual does not have enough similarity to the preexisting classes, it is evaluated with respect to a threshold called the Non-Informative Class (NIC), this being the novelty of the algorithm. However, LAMDA has problems making good classifications, either because the NIC is constant for all classes, or because the GAD calculation is unreliable. In this work, its efficiency is improved by two strategies, the first one, by the calculation of adaptable NICs for each class, which prevents that correctly classified individuals create new classes; and the second one, by computing the Higher Adequacy Degree (HAD), which grants more robustness to the algorithm. LAMDA-HAD is validated by applying it in different benchmarks and comparing it with LAMDA and other classifiers, through a statistical analysis to determinate the cases in which our algorithm presents a better performance.

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