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Open research questions in Landslides and related hazards

257 unresolved questions extracted from the limitations and future-work sections of 846 Landslides and related hazards papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The intrinsic challenges of geological data collection, including the difficulty of accessing remote areas and the complexity of geological systems. The need to integrate multiple datasets and techniques to investigate landslide evolution. The challenge of reconstructing the slope morphology and kinematics throughout different failure stages.

    Analysis of the May 2019 Joffre Peak landslides, British Columbia, Canada: The importance of considering changes in slope kinematics · 2026 · DOI

  • further investigation of the role of changes in kinematics on slope instability, - analysis of the effect of permafrost degradation and glacial retreat on landslide evolution, - study of the interaction between successive landslide stages

    Analysis of the May 2019 Joffre Peak landslides, British Columbia, Canada: The importance of considering changes in slope kinematics · 2026 · DOI

  • the complexity of sinkhole formation mechanisms - the limited availability of detailed subsurface data - the need for a robust and accurate susceptibility model for natural sinkholes

    Natural sinkholes in Umbria region (Central Italy): a regional-scale inventory and susceptibility assessment · 2026 · DOI

  • Operator-dependent subjectivity when detailed subsurface data are lacking, - The classification is based on an integrated interpretation of morphological, stratigraphic and hydrogeological features, - Validation relied on morphological and environmental criteria

    Natural sinkholes in Umbria region (Central Italy): a regional-scale inventory and susceptibility assessment · 2026 · DOI

  • The gap is not explicitly stated in the provided text, but it can be inferred that there is a need to analyze the impacts of coal mining on land transformations, landslide susceptibility, and tribal displacement. The study aims to fill this gap by focusing on the Lekhapani Range of Dehing Patkai Rainforest, India.

    Coal Mining-Induced Land Transformations, Landslide Susceptibility, and Tribal Displacement in the Lekhapani Range of Dehing Patkai Rainforest, India · 2026 · DOI

  • Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them.

    Overcoming the data limitations in landslide susceptibility modeling · 2025 · DOI

  • As our morphometric model only requires elevation data, it overcomes the major limitations of data-driven models and facilitates the creation of effective susceptibility models in areas where it was previously unfeasible.

    Overcoming the data limitations in landslide susceptibility modeling · 2025 · DOI

  • The conditions controlling when and where these flows bulk are not well understood, making their hazard unpredictable.

    The hazard of large debris flows · 2025 · DOI

  • Despite increasing research focus on this issue, systematic mapping of LUCC-landslide interdependencies and their cascading impacts remains lacking.

    From hazard mapping to risk governance: 20-year trajectory of land use/cover change impacts on landslide susceptibility via multi-modal scientometrics · 2025 · DOI

  • However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce.

    Surface Multi-Hazard Effects of Underground Coal Mining in Mountainous Regions · 2025 · DOI

  • However, the impact disaster mechanism of debris flow on bridge structures remains unclear.

    Numerical Analysis of the Dynamic Response of Concrete Bridge Piers under the Impact of Rock Debris Flow · 2024 · DOI

  • Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning.

    Time Series Prediction of Reservoir Bank Slope Deformation Based on Informer and InSAR: A Case Study of Dawanzi Landslide in the Baihetan Reservoir Area, China · 2024 · DOI

  • The spatial inventory lacks the information to describe landslide temporal distribution; there are insufficient samples in the temporal inventory to represent landslide spatial distribution.

    A Novel Framework for Spatiotemporal Susceptibility Prediction of Rainfall-Induced Landslides: A Case Study in Western Pennsylvania · 2024 · DOI

  • The improvement of 8.4% in coseismic landslide susceptibility prediction using multiple parameters compared to USGS PGA ShakeMap suggests further optimization of parameter combinations is needed.

    Exploratory relationships between selected ground motion parameters and coseismic landslides: A case study of the 2017 Jiuzhaigou MW6.5 earthquake · 2026 · DOI

  • The study focuses on a single earthquake event (2017 Jiuzhaigou MW6.5); generalization to other seismic contexts and earthquake magnitudes requires validation across multiple case studies.

    Exploratory relationships between selected ground motion parameters and coseismic landslides: A case study of the 2017 Jiuzhaigou MW6.5 earthquake · 2026 · DOI

  • Complex tectonic settings. Limited understanding of landslide behavior. Difficulty in assessing the long-term performance and stability of mitigation works.

    Reactivation of ancient landslides in complex tectonic settings: Insights from the long-term monitoring of Jiangdingya landslides on the northeastern Tibetan Plateau · 2026 · DOI

  • The study identifies a gap in previous research, which has not integrated spatial and temporal information effectively. The gap is addressed by proposing a hybrid CNN-LSTM framework.

    Remote sensing-based landslide prediction and risk assessment using a hybrid CNN–LSTM deep learning model · 2026 · DOI

  • Despite its strong performance, several limitations should be noted. First, model accuracy depends on the quality and spatial resolution of remote sensing data, which may be affected by cloud cover, sensor noise, or DEM limitations. Second, the temporal component included only rainfall and reservoir-level fluctuations. The absence of additional time-series variables, such as soil moisture, ground deformation, or seismic activity, may limit representation of all triggering mechanisms. Third, the landslide inventory may be incomplete or biased toward Scientific Reports | (2026) 16:10687 | https://doi.org/10.1038/s41598-026-43927-5 14 www.nature.com/scientificreports/ Fig. 10. Landslide susceptibility maps provided based on deep learning: (a) CNN-only, (b) LSTM-only (the maps were generated using ArcGIS software version 4.10.1, Esri, https://www.esri.com/arcgis).‎ accessible areas, leading to higher uncertainty in regions with sparse records. The framework was evaluated only in Kerman Province, and its transferability to regions with different environmental conditions remains untested. In addition, statistical uncertainty analysis and repeated cross-validation were not performed; therefore, performance differences should be interpreted as indicative rather than statistically conclusive. Finally, random dataset splitting may introduce spatial autocorrelation between subsets, potentially resulting in optimistic estimates. Future studies should apply spatially independent validation strategies, test the framework in multiple regions, and incorporate additional spatiotemporal variables to enhance robustness and generalizability.

    Remote sensing-based landslide prediction and risk assessment using a hybrid CNN–LSTM deep learning model · 2026 · DOI

  • Future research can focus on refining the multi-parametric analytical framework to improve the accuracy of landslide susceptibility assessment. The study suggests the need for further research on the integration of landuse change analysis and landslide susceptibility assessment.

    Earthquake Induced Landslide Identification to Support Landuse Planning in Rapid Growing Settlements Area of Imogiri and Dlingo Sub-District, Yogyakarta · 2026 · DOI

  • The study identifies a gap in the use of a multi-parametric analytical framework to assess earthquake-induced landslide susceptibility. The integration of landuse change analysis is needed to predict the future exposure of elements at risk.

    Earthquake Induced Landslide Identification to Support Landuse Planning in Rapid Growing Settlements Area of Imogiri and Dlingo Sub-District, Yogyakarta · 2026 · DOI

  • The complicated nonlinear interplay among geological, hydrological, and mechanical factors. The need for accurate risk assessment with early warnings. The requirement for computationally efficient data-driven components for operational prediction and warning functions.

    Application of machine learning and numerical simulation for monitoring and early warning systems of landslides and rockfalls in geohazard-prone regions · 2026 · DOI

  • Further evaluation of the proposed approach in various domains. Investigation of the application of the approach to other types of natural hazards. Development of more advanced machine learning algorithms and numerical simulations for monitoring and early warning systems.

    Application of machine learning and numerical simulation for monitoring and early warning systems of landslides and rockfalls in geohazard-prone regions · 2026 · DOI

  • The complexity of rainfall infiltration and shear strength parameter degradation processes. The spatial variability of geotechnical materials. The need for efficient and accurate reliability analysis methods.

    A machine learning-aided surrogate model for time-dependent reliability analysis of Baishuihe landslide under rainfall considering spatially variable soils · 2026 · DOI

  • The coupled effects of rainfall infiltration, soil spatial variability, and shear strength parameter degradation on reservoir slope stability remain ambiguous. Traditional slope stability analyses usually employ deterministic geotechnical parameters to compute the factor of safety.

    A machine learning-aided surrogate model for time-dependent reliability analysis of Baishuihe landslide under rainfall considering spatially variable soils · 2026 · DOI

  • There is a need to understand the spatial distribution, controlling factors, and mobility characteristics of landslides triggered by extreme rainfall events. There is a lack of comprehensive analysis of the mechanisms underlying landslide occurrence and mobility.

    Lithological controls on clustered landslides: a case study of landslides triggered by Typhoon Gaemi (2024) in Zixing, Hunan Province, China · 2026 · DOI

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257 open questions have been extracted from the limitations and future-work passages of 846 Landslides and related hazards papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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