Ditapis dengan
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. The response variable is often subject to detection limits due to limitations of the measuring instrument or the sampling protocol used. Consequently, the response variable's observations are censored …
- 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 exceeded 3,000, keeping the stable gas supply in the decade. Thus, the daily production prediction of gas wells is significant for monitoring production and for implementing and evaluating stimulation …
- 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 though these methods exhibit competitive spatial prediction performance, they do not exactly honor the categorical target variable's observed values at sampling locations by construction. On the other side…
- 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
Exact Conditioning of Regression Random Forest for Spatial Prediction
Regression random forest is becoming a widely-used machine learning technique for spatial prediction that shows competitive prediction performance in various geoscience fields. Like other popular machine learning methods for spatial prediction, regression random forest does not exactly honor the response variable’s measured values at sampled locations. However, competitor methods such as regr…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 6.201 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Developing ground motion prediction models for West Java: A machine learning …
Indonesia, one of the most earthquake-prone countries in the world, is currently developing an Earthquake Early Warning (EEW) system. A key component of this system, the Regional EEW, relies on Ground Motion Prediction models (GMPMs) to issue end-user alerts. However, in West Java, one of the pilot regions for this project, there is a lack of region-specific GMPMs essential for accurate early w…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 10,819 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Current progress in subseasonal-to-decadal prediction based on machine learning
The application of machine learning (ML) techniques to climate science has received significant attention, particularly in the field of climate predictions, ranging from sub-seasonal to decadal time scales. This paper reviews recent progress of ML techniques employed in climate phenomena prediction and the enhancement of dynamic forecast models, which provide valuable insights into the great po…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 5.666 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Long-term temperature prediction with hybrid autoencoder algorithms
This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomalies and provide a final temperature prediction. The second proposed approach involves training an AE and then using the resulting latent space as i…
- Edisi
- Vol.23, September 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 13 hlm PDF, 1.864 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Machine learning technique in the north zagros earthquake prediction
Studying the changes in seismicity, and the potential of the occurrences of large earthquakes in a seismic zone is not only extremely important from the aspect of seismological research, but it is additionally significant in the decisions of crisis management. Since, nowadays Machine learning techniques have proven the high ability for analyzing information, and discovering the relations among …
- Edisi
- Vol.22, June 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 2.915 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
GeoCoDA: Recognizing and validating structural processes in geochemical data.…
Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted to the real non-negative number space with constant-sum constraint, that is, the simplex. Geochemistry can be considered a proxy for mineralogy, comprised of atomically ordered structures that define the placement and abundance of elements in the mineral lattice struct…
- Edisi
- Vol.22, June 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 15 hlm PDF, 1.985 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
AnnRG - An artificial neural network solute geothermometer
Solute artificial neural network geothermometers offer the possibility to overcome the complexity given by the solute-mineral composition. Herein, we present a new concept, trained from high-quality hydrochemical data and verified by in-situ temperature measurements with a total of 208 data pairs of geochemical input parameters (Na+, K+, Ca2+, Mg2+, Cl−, SiO2, and pH) and reservoir temperatur…
- Edisi
- Vol.20, December 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 4.961 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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