Ditapis dengan
Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Te…
Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest I…
- Edisi
- Vol.15, Issue 3, March 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 28 hlm PDF, 4.459 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Optimized Random Forest Models for Rock Mass Classification in Tunnel Constru…
The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), and Bayesian optimization (BO) algorithms to classify the surrounding rock in real …
- Edisi
- Vol.15, Issue 2, February 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 26 hlm PDF, 3.524 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Lin…
The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the b…
- Edisi
- Vol.15, Issue 1, January 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 24 hlm PDF, 5.793 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coa…
This study aims to establish a scientific and methodological basis for predicting shoreline positions using modern data analysis and machine learning techniques. The focus area is a 5 km section of the Ural coast along Baydaratskaya Bay in the Kara Sea. This region was selected due to its diverse geomorphological features, varied lithological composition, and significant presence of permafrost …
- Edisi
- Vol.15, Issue 1, January 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 19 hlm PDF, 2.124 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Water resource forecasting with machine learning and deep learning: A sciento…
Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging themes, this study analyzed 876 articles published between 2015 and 2022, retrieved from the Web of Science database. Leveraging CiteSpace visualization software, bibliometric techniques, and li…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 4.935 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Reservoir evaluation using petrophysics informed machine learning: A case study
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical information can either be specific logging response equations or abstract relationships between logging data and reservoir parameters. We compare our method's performances using two datasets and evaluate…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 19.964 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Research on the prediction method for fluvial-phase sandbody connectivity bas…
The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield. Conventional connectivity studies often use methods such as seismic attribute fusion, while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes. Aiming at the above problem in the Bohai…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 6.809 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Reconstruction of lithofacies using a supervised Self-Organizing Map: Applica…
Recently, machine learning (ML) has been considered a powerful technological element of different society areas. To transform the computer into a decision maker, several sophisticated methods and algorithms are constantly created and analyzed. In geophysics, both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation. …
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 3.426 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Prediction of seismic-induced bending moment and lateral displacement in clos…
Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment, typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction. Despite recent advancements in machine learning techniques, there is a persistent need to establish data-driven models that can predict …
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 8.793 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Locally varying geostatistical machine learning for spatial prediction
Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these methods, the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area. This assumption, known as spatial stationarity, is very quest…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 9.301 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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