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Open research questions in Landslides and related hazards

44 unresolved questions extracted from the limitations and future-work sections of 643 Landslides and related hazards papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Future developments involve cloud–edge co-process- ing and low-power wide-area networks (LPWANs) for IoT. To scale up monitoring in mountainous regions, it is recommended to adopt edge intelligence for low-latency preprocessing (time sync, denoising, outlier removal, and baseline-shift mitigation) and on-device anomaly screening, while cloud platforms handle model train- ing, multi-sensor fusion, and data archiving. In addition, constructing digital twins of landslides based on physics- informed and data-assimilative models can bridge the gap between observational and computational agents, providing an intelligent, interpretable module for mecha- nistic explanation and operational decision-making.

    GNSS and multi-modal data integration methods for landslide displacement monitoring: a review · 2026 · DOI

  • 1 3Bulletin of Engineering Geology and the Environment (2026) 85:363 363 Page 18 of 21 ● Check on dataset completeness: the completeness of the CEDIT entire catalogue is influenced by factors such as partial inventories, especially for historical earthquakes, and the limited information on smaller magnitude events.

    Evaluating completeness and consistency in earthquake-induced ground effects inventorying: insights from the last release of the Italian CEDIT catalogue · 2026 · DOI

  • The primary limitation of this study arises from the inherent spatial variability of soil properties and the uncertainty associated with the model parameters. Furthermore, the analysis was conducted using a deterministic framework, assuming homogeneous soil conditions across the study area in the LS-RAPID simulations. The triggering mechanism was simulated by progressively increasing pore-water pressure to match field observations of documented landslide events, consistent with the approaches adopted by Sassa et al. (2010), Vadivel and Sennimalai (2019), and Ha et al. (2020). As the objective of this research was to evaluate the influence of shear rate on landslide mobility, the pore-pressure ratios used in the validated model of the Achanakkal and Madithorai cases were maintained constant while assessing variations in runout distance. Future investigations could enhance the robustness of the framework by systematically varying pore-pressure ratios and incorporating rainfall records, thereby enabling the development of riskindexed susceptibility maps that may better support early-warning strategies and land-use planning in rainfall-prone regions (Ha et al. 2020; Weerasinghe et al. 2024). Fig.

    Experimental and numerical study of shear rate effects on residual shear strength and landslide mobility · 2026 · DOI

  • The PSO-CB-SHAP model focuses on conducting LSM with effect and identifying the core influencing factors, thereby providing critical insights for preventing geological hazards. However, a common challenge in landslide susceptibility assessment is the limited number of geological disaster samples. Insufficient sample sizes can compromise the accuracy of the LSM model, resulting in discrepancies between predicted landslide susceptibility zones and actual conditions. To address this issue, two primary approaches can be considered: (1) Conducting comprehensive and detailed field surveys of geological disasters and establishing a real-time updated geological disaster database. A robust geological disaster database serves as a strong foundation for regional geological disaster susceptibility assessments and is a key focus for future work. (2) Enhancing samples through algorithms, which involves generating similar sample points to increase the sample size. Standard methods for this purpose include Generative Adversarial Networks (GANs) and Gaussian Mixture Models (GMMs). However, the impact of such generated similar sample points on the performance of the LSM model warrants further investigation. Sang et al. Geoenvironmental Disasters (2026) 13:38 Page 18 of 21 Fig. 11 Single-factor dependence plots. a Elevation; b Slope; c EGP; d Distance to road; e Land use; f Profile curvature Sang et al.

    Landslide susceptibility mapping based on stratified non-landslides sampling from engineering geological petrofabric and optimized boosting models: a case of Maiji District, China · 2026 · DOI

  • Future research will investigate the integration of additional geospatial variables such as soil moisture, lithology, land cover, and SAR- or LiDAR-derived structural or deformation metrics.

    Learning to detect landslides from multi-source remote sensing data with super-resolution reconstruction and deep feature fusion · 2026 · DOI

  • Future research should focus on: (1) integrating TRIGRS with distributed hydrological Bulletin of Engineering Geology and the Environment (2026) 85:346 1 3 models to better simulate lateral flow and surface ponding; (2) conducting extensive field investigations, particularly refined studies on input parameters such as soil thickness, to further reduce prediction uncertainty and improve the reli- ability of the model for early warning systems.

    Dynamic simulation of slope stability based on TRIGRS model: a case study of cluster landslides in Jiangwan Town, Shaoguan, Guangdong, China · 2026 · DOI

  • In certain cases in which the sliding mass is laterally delimited by the hydro- graphic network and thus the sliding mass free surface has a pronounced convex shape, like in the case of the Armou landslide, the basal slip surface can be assumed to emerge directly on the ground surface along the lateral boundaries (e.

    Investigation of a slow-moving landslide in Cretaceous bentonitic clay · 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

  • Settlement expansion projections to 2026 and 2032 assume continuation of 2013-2022 accessibility-driven growth patterns along road networks, but do not account for how post-seismic infrastructure damage, landslide-triggered road closures, or mandatory building code restrictions in high-susceptibility zones would alter the actual land development trajectory in Imogiri (western slopes near Baturagung Escarpment) and Dlingo.

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

  • The geological heterogeneity analysis contrasts the weak Semilir Formation (high-risk in Wukirsari) with the competent Wonosari Formation (lower vulnerability in Dlingo), but does not quantify how groundwater saturation and pore pressure changes from high-intensity rainfall (eastern sector) interact with formation-specific geotechnical properties to modulate earthquake-induced landslide susceptibility across formation boundaries.

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

  • The study uses Landsat-based classification for landuse change detection between 2013-2022, but does not specify the minimum mapping unit or validation accuracy achieved for distinguishing settlement encroachment into medium-susceptibility areas (64.08 km², 57% of study area). Finer-resolution multispectral or SAR data validation would be needed to refine settlement boundary delineation in steep terrain where shadow and slope distortion affect classification.

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

  • The susceptibility mapping identifies concentrated high-risk zones (1.7%) primarily in Wukirsari, yet the paper does not specify monitoring strategies or early warning system parameters tailored to the convergence of rainfall intensity, seismic forcing, and rapid tourism-driven settlement expansion along the Piyungan-Dlingo-Imogiri collector road where new tourism poles are emerging.

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

  • While the study identifies that vegetation cover loss due to settlement conversion reduces soil shear strength and slope stabilization, no quantitative threshold is established for the critical percentage of forest/agricultural land removal that would render mid-slope terrain (301-500 m/km²) transition from medium to high landslide susceptibility. This threshold needs empirical measurement for the specific root systems and soil types of the Baturagung Escarpment region.

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

  • The Ca-Markov settlement expansion model was calibrated on 2013-2022 transition patterns, but the study does not validate whether this longer-term calibration remains accurate for earthquake-induced landslide susceptibility when settlements encroach into the identified high-risk zones (1.93 km² in Wukirsari). Quantitative assessment of how settlement proximity to steep slopes (>800 m/km²) and weak Semilir Formation lithology affects actual landslide triggering during seismic events (238.222 gal, VII MMI) is missing.

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

  • However, actual landslide investigation data for the wider Kravarsko area (pilot area PA1) is relatively sparse and no landslide inventory or typical landslide model exists.

    Remote landslide mapping, field validation and model development – An example from Kravarsko, Croatia · 2022 · DOI

  • The rate of landslide advance and potential for sustained movement has previously been studied in the context of hydrological factors, such as dilative or contractive pore pressure response, but the sensitivity of realistic landslide geometry subject to toe erosion is not well quantified.

    Quantifying the Sensitivity of Progressive Landslide Movements to Failure Geometry, Undercutting Processes and Hydrological Changes · 2019 · DOI

  • Prediction with precision of precipitation patterns in mountainous areas is difficult due to lack of data and the complex effect of topography in rainfall, however, it is of major importance in order to identify vulnerable areas.

    MODELLING EXTREME PRECIPITATION IN HAZARDOUS MOUNTAINOUS AREAS. CONTRIBUTION TO LANDSCAPE PLANNING AND ENVIRONMENTAL MANAGEMENT · 2010 · DOI

  • Geospatial empirical and statistical models, mainly based on past storm events have predictive performance at scale but are limited by their lack of physical grounding.

    Hybrid landslide susceptibility modelling for rainfall-triggered landslides in forested slopes: a review for bridging physics-based and machine learning methods · 2026 · DOI

  • However, the phenomenon of pressure fluctuations in avalanches is rarely discussed in existing studies, and few studies have linked erosion with snow avalanche impact behavior so far.

    Experimental investigation on impact pressure fluctuations of snow avalanches over erodible beds · 2026 · DOI

  • " Therefore, individual localized engineering adaptations are insufficient; slope stabilization must be tackled via coordinated, community-wide municipal infrastructure rather than isolated household efforts.

    Precipitation dynamics and slope stability in the urban hills of baluwakhani, gangtok · 2026 · DOI

  • Future research may focus on incorporating time-dependent triggering factors such as rainfall intensity, seismic activity, and land-use dynamics to improve predictive accuracy.

    Landslide susceptibility zonation using the Information Value (IV) model in the High Wavy Mountains, Meghamalai, South India · 2026 · DOI

  • This case demon- strates that relying solely on construction-stage investigations is insufficient to address such long-term and concealed geohaz- ard risks.

    Progressive deformation and post-failure residual displacement of the 16 October 2025 Tongren loess landslide, Qinghai, China: insights for long-term extra-high voltage transmission line monitoring · 2026 · DOI

  • The findings are expected to provide a valuable reference for enhancing DFSM reliability in regions where unsurveyed areas are not clearly defined.

    Enhancing debris flow susceptibility models using negative samples from hazard-bearing body data · 2026 · DOI

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44 open questions have been extracted from the limitations and future-work passages of 643 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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