Predicting Lake Erie wave heights and periods using XGBoost and LSTM

H Hu, AJ van der Westhuysen, P Chu… - Ocean Modelling, 2021‏ - Elsevier
Waves in large lakes put coastal communities and vessels under threat, and accurate wave
predictions are needed for early warnings. While physics-based numerical wave models …

Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid …

M Abbaszadeh, S Soltani-Mohammadi… - Computers & …, 2022‏ - Elsevier
The support vector classifier (SVC) is one of the most powerful machine learning algorithms.
This algorithm has been accepted as an effective method in three-dimensional geological …

A Comparison of machine learning algorithms in predicting lithofacies: Case studies from Norway and Kazakhstan

T Merembayev, D Kurmangaliyev, B Bekbauov… - Energies, 2021‏ - mdpi.com
Defining distinctive areas of the physical properties of rocks plays an important role in
reservoir evaluation and hydrocarbon production as core data are challenging to obtain from …

[HTML][HTML] Classification of logging data using machine learning algorithms

R Mukhamediev, Y Kuchin, N Yunicheva… - Applied Sciences, 2024‏ - mdpi.com
A log data analysis plays an important role in the uranium mining process. Automating this
analysis using machine learning methods improves the results and reduces the influence of …

Topological graph representation of stratigraphic properties of spatial-geological characteristics and compression modulus prediction by mechanism-driven learning

M Wang, E Wang, X Liu, C Wang - Computers and Geotechnics, 2023‏ - Elsevier
The soil's compression modulus (Es) is one of the most critical mechanical parameters for
studying land subsidence in urban strata. Meanwhile, the vertical heterogeneity and lateral …

[HTML][HTML] Application of machine learning methods to assess filtration properties of host rocks of uranium deposits in Kazakhstan

Y Kuchin, R Mukhamediev, N Yunicheva… - Applied Sciences, 2023‏ - mdpi.com
The uranium required for power plants is mainly extracted by two methods in roughly equal
amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by …

Estimation of filtration properties of host rocks in sandstone-type uranium deposits using machine learning methods

RI Mukhamediev, Y Kuchin, Y Amirgaliyev… - IEEE …, 2022‏ - ieeexplore.ieee.org
The nuclear decay of uranium is one of the cleanest ways to meet the growing energy
demand. The uranium needed for power plants is mainly extracted by two methods in …

A machine-learning based approach to predict facies associations and improve local and regional stratigraphic correlations

FMW Tognoli, AF Spaniol, ME de Mello… - Marine and Petroleum …, 2024‏ - Elsevier
The geological record has challenged stratigraphers through time. Many depositional,
tectonic and paleobiological events require stratigraphic positioning to determine temporal …

[HTML][HTML] Determination of reservoir oxidation zone formation in uranium wells using ensemble machine learning methods

RI Mukhamediev, Y Kuchin, Y Popova, N Yunicheva… - Mathematics, 2023‏ - mdpi.com
Approximately 50% of the world's uranium is mined in a closed way using underground well
leaching. In the process of uranium mining at formation-infiltration deposits, an important …

Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry …

M Abbaszadeh, V Khosravi, AB Pour - Earth Science Informatics, 2024‏ - Springer
Geological domaining is an essential aspect of mineral resource evaluation. Various explicit
and implicit modeling approaches have been developed for this purpose, but most of them …