Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 2947 - 2947

Published: Aug. 12, 2024

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution research publication trends, (3) progress of algorithms, (4) application techniques models for landslide susceptibility mapping, detections, prediction, inventory deformation monitoring, assessment, extraction management. selections were based on keyword searches using title/abstract keywords from Web Science Scopus. A total 186 articles published between 2011 2024 critically reviewed to provide answers questions related the recent advances use technologies combined with artificial intelligence (AI), machine (ML), deep (DL) algorithms. review revealed that these methods have high efficiency detection, hazard mapping. few current issues also identified discussed.

Language: Английский

---

Bridging Macro–Microscopic Parameters: Correspondence and Calibration Approaches

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 707 - 707

Published: Jan. 13, 2025

Clarifying the correlation between macro–microscopic granular parameters and establishing effective parameter calibration methods for determining microparameters is of great importance numerical simulations flows. Currently, there a gap in systematic study small particles, using sandy soil as an example, development rational methods. Through inter-particle friction experiments, particle–wall uniaxial compression experiments simulations, we studied relationship macroscopic microscopic parameters. Based on these studies, appropriate empirical formulas were proposed, based parameters, method calibrating was presented. This approach uses combination with constraints imposed by stiffness coefficient to calibrate provides process framework standardizing improving both efficiency quality.

Language: Английский

---

Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 1843 - 1843

Published: Feb. 11, 2025

As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing have often ignored the impact of similarities geographical attributes, restricting their feasibility regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data can isolate region-specific features, thus overcoming this challenge. Consequently, method was developed by integrating information value (IV) GOS model. Huangshan City Anhui Province, China, selected as study region. This research used 11 remote sensing feature factors 657 historical points, combined IV model, to construct dataset prediction using findings indicate that, compared conventional such random forest, logistic regression, radial basis function classifier, enhances area under curve (AUC) 2.81% 8.92%, reaching 0.846. demonstrates superior performance confirms effectiveness accuracy assessment. Furthermore, basic-configuration-similarity (BCS) increases AUC 9.64%, achieving approach substantially diminishes effects accuracy, revealing upgraded applicability evaluations. Landslides are primarily influenced rainfall vegetation cover. High-susceptibility zones predominantly located areas high precipitation low In contrast, low-susceptible non-susceptible found flat cover farther from fault lines. majority region lies landslide-prone zones, comprising only 12.43% total area. Historical landslides largely concentrated moderate- high-susceptibility accounting 92.24% all occurrences. Landslide density level, 0.15 per square kilometre zones. brings forward reliable strategy establishing spatial relationship between attribute susceptibility, bolstering method’s adaptability across various regions.

Language: Английский

---

Transfer learning-based deep learning models for flood and erosion detection in coastal area of Algeria

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 21, 2025

Language: Английский

---

Debris flow in indian himalaya: A threat to emerging infrastructure

Bulletin of Engineering Geology and the Environment, Journal Year: 2024, Volume and Issue: 83(11)

Published: Oct. 4, 2024

Language: Английский

---

Comparative Analysis of Machine Learning, Statistical, and MCDA Methods for Rainfall-Induced Landslide Susceptibility Mapping in the Eco-Sensitive Koyna River Basin of India

Indian geotechnical journal, Journal Year: 2024, Volume and Issue: unknown

Published: April 25, 2024

Language: Английский

---

Evaluating landslide hazard, vulnerability, and risk using machine learning; A case study from the Alaknanda Valley, NW Himalaya

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Language: Английский

---

Understanding the role of topography, climate, and sediment transport dynamics in flash flood hazards along the Dhauli Ganga River of northwest Himalaya, India

Journal of Earth System Science, Journal Year: 2024, Volume and Issue: 133(4)

Published: Nov. 25, 2024

Language: Английский

---