The role of artificial intelligence and IoT in prediction of earthquakes: Review
Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment, lives, and properties. There has been an increasing interest in th…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 19 hlm PDF, 5.965 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 …
- 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
Enhanced permeability prediction in porous media using particle swarm optimiz…
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the e…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 9.874 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Optimization of shale gas fracturing parameters based on artificial intellige…
Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of …
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 13.443 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Determination of future land use changes using remote sensing imagery and art…
Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 6.696 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
ResGraphNet: GraphSAGE with embedded residual module for prediction of global…
Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering t…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 2.716 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Geostatistical semi-supervised learning for spatial prediction
Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms. Typically, the target variab…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 17 hlm PDF, 10.115 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Random forest for spatial prediction of censored response variables
The spatial prediction of a continuous response variable when spatially exhaustive predictor variables are available within the region under study has become ubiquitous in many geoscience fields. T…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 5.490 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Data-driven approaches for time series prediction of daily production in the …
The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has e…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.029 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Classification random forest with exact conditioning for spatial prediction o…
Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region. Even thoug…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 7.744 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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