Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5423-5450, 2025, DOI:10.32604/cmc.2025.067952 - 23 October 2025

Abstract This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double… More >

Nurul A. Asram¹, Eran S. S. Md Sadek^2,*

Revue Internationale de Géomatique, Vol.34, pp. 619-637, 2025, DOI:10.32604/rig.2025.065667 - 06 August 2025

Abstract Landslides are a frequent geomorphological hazard in tropical regions, particularly where steep terrain and high precipitation coincide. This study evaluates landslide susceptibility in the Jelapang area of Perak, Malaysia, using Shannon Entropy-weighted bivariate models (i.e., Frequency Ratio, Information Value, and Weight of Evidence), in comparison with Logistic Regression. Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity: slope gradient, slope aspect, curvature, vegetation cover, lineament density, terrain ruggedness index, and flow accumulation. Each model generated susceptibility maps, which were validated using Receiver Operating Characteristic curves and Area Under the Curve… More >

Naveen Chandra¹, Himadri Vaidya^2,3, Suraj Sawant⁴, Shilpa Gite^5,6, Biswajeet Pradhan^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3351-3375, 2025, DOI:10.32604/cmes.2025.064395 - 30 June 2025

Abstract Landslide hazard detection is a prevalent problem in remote sensing studies, particularly with the technological advancement of computer vision. With the continuous and exceptional growth of the computational environment, the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning. Furthermore, attention models, driven by human visual procedures, have become vital in natural hazard-related studies. Hence, this paper proposes an enhanced YOLOv5 (You Only Look Once version 5) network for improved satellite-based landslide detection, embedded with two popular attention modules: CBAM (Convolutional Block Attention Module) More >

Duc-Dam Nguyen¹, Nguyen Viet Tiep^2,*, Quynh-Anh Thi Bui¹, Hiep Van Le¹, Indra Prakash³, Romulus Costache^4,5,6,7, Manish Pandey^8,9, Binh Thai Pham¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 467-500, 2025, DOI:10.32604/cmes.2024.056576 - 17 December 2024

Abstract This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand, India, using advanced ensemble models that combined Radial Basis Function Networks (RBFN) with three ensemble learning techniques: DAGGING (DG), MULTIBOOST (MB), and ADABOOST (AB). This combination resulted in three distinct ensemble models: DG-RBFN, MB-RBFN, and AB-RBFN. Additionally, a traditional weighted method, Information Value (IV), and a benchmark machine learning (ML) model, Multilayer Perceptron Neural Network (MLP), were employed for comparison and validation. The models were developed using ten landslide conditioning factors, which included slope, aspect, elevation, curvature, land cover, geomorphology,… More >

Maan Habib^1,*, Ahed Habib², Mohammad Abboud³

Revue Internationale de Géomatique, Vol.33, pp. 247-271, 2024, DOI:10.32604/rig.2024.053857 - 07 August 2024

Abstract Digital elevation models (DEMs) are essential tools in environmental science, particularly for hazard assessments and landscape analyses. However, their application across multiple environmental hazards simultaneously remains in need for a multi-aspect critical assessment to promote their effectiveness in comprehensive risk management. This paper aims to review and critically assess the application of DEMs in mapping and managing specific environmental hazards, namely floods, landslides, and coastal erosion. In this regard, it seeks to promote their utility of hazard maps as key tools in disaster risk reduction and environmental planning by employing high-resolution DEMs integrated with advanced More >

Shiwei Zhao^1,*, Hao Chen², Jidong Zhao¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09965

Abstract With global warming, thaw-induced landslides occur more frequently in permafrost, which not only threaten the safety of infrastructures as general geohazards but also worsen global warming due to carbon release. This work presents a novel computational framework to model thaw-induced landslides from a multiscale perspective. The proposed approach can capture the thermal-mechanical (TM) response of frozen soils at the particulate scale by using discrete element method (DEM). The micromechanics-based TM model is superior to capturing the sudden crash of soil skeletons caused by thaw-induced cementation loss between soil grains. The DEM-simulated TM response is then More >

Can Huang^1,*, Xiaoliang Wang¹, Qingquan Liu¹, Huaning Wang²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.09824

Abstract Soil‒water coupling is an important process in landslide-generated impulse waves (LGIW) problems, accompanied by large deformation of soil, strong interface coupling and three-dimensional effect. A meshless particle method, smooth particle hydrodynamics (SPH) has great advantages in dealing with complex interface and multiphase coupling problems. This study presents an improved soil‒water coupled model to simulate LGIW problems based on an open source code DualSPHysics (v4.0). Aiming to solve the low efficiency problem in modeling real large-scale LGIW problems, graphics processing unit (GPU) acceleration technology is implemented into this code. An experimental example, subaerial landslidegenerated water waves,… More >

G. Bhargavi^*, J. Arunnehru

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1257-1272, 2023, DOI:10.32604/iasc.2023.034335 - 05 January 2023

Abstract Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala’s Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and… More >

Shaojie Feng^*, Leipeng Liu, Chen Gao, Hang Hu

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.5, pp. 1189-1201, 2023, DOI:10.32604/fdmp.2023.023637 - 30 November 2022

Abstract The present study considers the damage mechanisms and the rainfall infiltration process responsible for landslide phenomena which originate from accumulation slopes. Accordingly, a physical test model is developed for different slopes and different rainfall conditions. Moreover, a three-dimensional laser scanner and a camera are used to monitor the slope erosion and the landslide dynamic evolution. Using this approach, the time variation curves of volumetric water content, pore water pressure, soil pressure, slope deformation, and damage are determined. The results show that under similar conditions, similar trends of the pore water pressure are achieved for different More > Graphic Abstract

Binghui Cui^1,*, Liaojun Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 357-376, 2023, DOI:10.32604/cmes.2022.022309 - 29 September 2022

Abstract Flow-type landslide is one type of landslide that generally exhibits characteristics of high flow velocities, long jump distances, and poor predictability. Simulation of its propagation process can provide solutions for risk assessment and mitigation design. The smoothed particle hydrodynamics (SPH) method has been successfully applied to the simulation of two-dimensional (2D) and three-dimensional (3D) flow-like landslides. However, the influence of boundary resistance on the whole process of landslide failure is rarely discussed. In this study, a boundary condition considering friction is proposed and integrated into the SPH method, and its accuracy is verified. Moreover, the… More > Graphic Abstract