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
Geophysical-Geotechnical Characterization of Mud Volcanoes in Cartagena Colombia
In this research, the mud diapirism phenomenon in the Membrillal sector in Cartagena is characterized to analyze its spatiotemporal evolution. The goal is to geomorphologically, geotechnically, and geologically characterize the area to zone regions with the greatest susceptibility to geological hazards and provide an updated diagnosis of the phenomenon. This study is conducted due to the risks …
- Edisi
- Vol.15, Issue 3, March 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 34 hlm PDF, 7.640 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Benchmarking data handling strategies for landslide susceptibility modeling u…
Machine learning (ML) algorithms are frequently used in landslide susceptibility modeling. Different data handling strategies may generate variations in landslide susceptibility modeling, even when using the same ML algorithm. This research aims to compare the combinations of inventory data handling, cross validation (CV), and hyperparameter tuning strategies to generate landslide susceptibilit…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 33.426 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Ensemble hybrid machine learning methods for gully erosion susceptibility map…
Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random sub-space (RSS) and rotation forest (RTF) ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility (GES) in Hinglo river basin. 120 gullies were marked and grouped into four-f…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 7.028 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Advanced AI techniques for landslide susceptibility mapping and spatial predi…
Landslides, a global phenomenon, significantly impact economies and societies, especially in densely populated areas. Effective mitigation requires awareness of landslide risks, yet temporal links between occurrences are often neglected, challenging model performance due to non-stationary triggering and predisposing factors. This study presents a novel landslide susceptibility model that incorp…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 11 hlm PDF, 3.550 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A machine learning approach for mapping susceptibility to land subsidence cau…
Land subsidence is a worldwide threat that may cause irreversible damage to the environment and the infrastructures. Thus, identifying and mapping areas prone to land subsidence with accurate methods such as Land Subsidence Susceptibility Index (LSSI) mapping is crucial for mitigating the adverse impacts of this geohazard. Also, Machine Learning (ML) is now becoming a powerful tool to analyze v…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 15.318 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Flood susceptibility mapping: Integrating machine learning and GIS for enhanc…
Flooding presents a formidable challenge in the United States, endangering lives and causing substantial economic damage, averaging around $5 billion annually. Addressing this issue and improving community resilience is imperative. This project employed machine learning techniques and publicly available data to explore the factors influencing flooding and to develop flood susceptibility maps at…
- Edisi
- Vol.23, September 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 13 hlm PDF, 13.528 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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