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Geosciences
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Intelligent Landslide Early Warning: From Multi-Source Sensing to AI-Driven Forecasting
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Intelligent Landslide Early Warning: From Multi-Source Sensing to AI-Driven Forecasting

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2433

Special Issue Editors

Dr. Ting Xiao

E-Mail Website
Guest Editor
School of Earth Sciences and Information Physics, Central South University, Changsha, China
Interests: geological disaster mechanism and monitoring and early warning; landslide risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Linwei Li

E-Mail Website
Guest Editor
College of Resources and Environmental Engineering, Guizhou University, Guiyang, China
Interests: landslide geological hazard prediction and forecasting; geotechnical stability assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Jizhixian Liu

E-Mail Website
Guest Editor Assistant
School of Civil Engineering, Hubei Engineering University, Xiaogan, China
Interests: geological disaster mechanism; landslide–tsunami; numerical simulation

Special Issue Information

Dear Colleagues,

Landslides pose significant risks to infrastructure and communities worldwide, often triggering a cascade of secondary hazards such as landslide-dammed lakes, debris flows, and landslide-induced waves, which can amplify disaster impacts. Understanding their mechanisms and improving early warning systems requires integrating multi-source monitoring technologies, physical model tests, and artificial intelligence, and recent advances in remote sensing, sensor networks, high-performance computing, and AI-driven data analysis have revolutionized landslide and secondary hazard investigations, enabling more accurate predictions and dynamic risk assessments. This Special Issue seeks to showcase cutting-edge research and innovative methodologies in landslide and secondary disaster monitoring, physical and numerical modeling, and AI applications.

We invite contributions on experimental studies, case studies, and novel technological approaches that enhance our understanding of landslide processes and improve hazard mitigation strategies, and we particularly encourage interdisciplinary contributions bridging geosciences, data science, and engineering domains.

Dr. Ting Xiao
Dr. Linwei Li
Guest Editors

Dr. Jizhixian Liu
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • landslide monitoring
  • multi-source sensing
  • model test
  • artificial intelligence
  • early warning system
  • risk assessment

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Published Papers (3 papers)

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Research

28 pages, 12703 KB  
Article
Multi-Scale Attention Network for Landslide Susceptibility Assessment
by Zhao Zhan, Shanxiong Chen, Min Zhang, Wenzhong Shi, Yangjie Sun and Hongbo Luo
Geosciences 2026, 16(5), 188; https://doi.org/10.3390/geosciences16050188 - 7 May 2026
Viewed by 223
Abstract
Landslide susceptibility assessment (LSA) is crucial for regional landslide risk evaluation and mitigation strategy formulation. Previous studies mostly adopted single-scale features, while landslide formation is influenced by multi-scale factors, making multi-scale information extraction more appropriate for assessment. This study proposes a deep learning [...] Read more.
Landslide susceptibility assessment (LSA) is crucial for regional landslide risk evaluation and mitigation strategy formulation. Previous studies mostly adopted single-scale features, while landslide formation is influenced by multi-scale factors, making multi-scale information extraction more appropriate for assessment. This study proposes a deep learning framework integrating multi-scale and attention modules for object-based LSA. A multi-scale network extracts geo-environmental features at different scales, which are input into attention networks using multi-head attention and Squeeze-and-Excitation, termed MSMHA and MSSE, respectively, to enhance relevant features and suppress irrelevant ones. Finally, features are fused for classification and prediction. In a case study in Hong Kong, CNN-based and ML-based methods were compared using 9814 landslides and 11 influencing factors. Results show the proposed MSMHA (area under the curve, AUC 0.91) and MSSE (AUC 0.90) outperform conventional methods (e.g., random forest with AUC 0.86; multi-layer perceptron and support vector machine with AUC 0.85; DenseNet with AUC 0.86; CNN with AUC 0.88; VGG with AUC 0.87; GoogLeNet and ResNet with AUC 0.81). CNN-based methods outperformed ML-based ones, indicating that incorporating neighborhood information improves model performance. The rationality of the susceptibility map generated by MSMHA was verified via comparative analysis. Results confirm that the proposed multi-scale and attention-integrated framework outperforms traditional single-scale methods consistently. Equally importantly, the case study provides advanced CNN-based landslide susceptibility maps for Hong Kong, which can serve as a critical reference for regional landslide risk management and the formulation of targeted mitigation strategies. Full article
(This article belongs to the Special Issue Intelligent Landslide Early Warning: From Multi-Source Sensing to AI-Driven Forecasting)
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24 pages, 11712 KB  
Article
Reservoir Basin-Scale Landslide Susceptibility Assessment by Machine Learning Techniques: A Case Study of San Pietro Dam, Southern Italy
by Elias E. Chikalamo, Olga C. Mavrouli and Piernicola Lollino
Geosciences 2026, 16(4), 153; https://doi.org/10.3390/geosciences16040153 - 8 Apr 2026
Viewed by 558
Abstract
Research on landslides around reservoirs is necessitated to strengthen risk prevention and mitigation, as their occurrence has catastrophic consequences. For reservoir safety assessments, landslide susceptibility analysis is commonly concentrated on single reservoir bank slopes or individual landslides. However, focusing solely on bank slopes [...] Read more.
Research on landslides around reservoirs is necessitated to strengthen risk prevention and mitigation, as their occurrence has catastrophic consequences. For reservoir safety assessments, landslide susceptibility analysis is commonly concentrated on single reservoir bank slopes or individual landslides. However, focusing solely on bank slopes and individual landslides gives an incomplete picture of how safe the reservoir is from possible landslide related risks, since landslides from distant slopes can also adversely affect the reservoir in different ways. In this paper, landslide susceptibility assessment was conducted using machine learning models (Gradient Boosting Machine, XGBoost, Random Forest and Ensemble Stacking) in the area around the San Pietro Dam, an earth dam located in Southern Italy, in a region highly prone to landslide hazards. The landslide inventory for the area was used to generate landslide and non-landslide points for model training and testing. The area under curve (AUC) of a receiver operating characteristic (ROC) curve approach was used to evaluate, validate, and compare the performance of the four models. Results indicated that the ROC AUC values of the models ranged from 0.76 to 0.77, with the Random Forest, Gradient Boosting and Ensemble stacking models having AUC values of 0.77. All the models classified about 15–20% of the reservoir basin as highly susceptible to landslides. The generated basin-scale landslide susceptibility maps can be used to prioritize monitoring and maintenance in areas around the dam that have been identified as highly susceptible. Full article
(This article belongs to the Special Issue Intelligent Landslide Early Warning: From Multi-Source Sensing to AI-Driven Forecasting)
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33 pages, 18247 KB  
Article
Learning Debris Flow Dynamics with a Deep Learning Fourier Neural Operator: Application to the Rendinara–Morino Area
by Mauricio Secchi, Antonio Pasculli, Massimo Mangifesta and Nicola Sciarra
Geosciences 2026, 16(2), 55; https://doi.org/10.3390/geosciences16020055 - 24 Jan 2026
Cited by 3 | Viewed by 1067
Abstract
Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are [...] Read more.
Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the (x,t) domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to h(x,t) and u(x,t), thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative L2 errors of about 6–7% for flow depth and 10–15% for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately 36× with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments. Full article
(This article belongs to the Special Issue Intelligent Landslide Early Warning: From Multi-Source Sensing to AI-Driven Forecasting)
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