Many existing landslide inventories lack type-specific information

The gap

Evidence profile

Sourced from the limitations and stated research gap and future-work section and stated challenges and abstract of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 7 journals. Those papers have been cited 32 times in total.

Supporting evidence — 8 representative gaps

• Impact of climate change on future flood susceptibility using different climatic parameters and deep learning algorithms in eastern Himalayan region (2026) · Frontiers in Environmental Science · doi

  Frontiers in Environmental Science 17 frontiersin.org Paramanik et al. 10.3389/fenvs.2026.1729457 More dynamic models that account for changes in land use patterns could provide more accurate predictions. Additionally, this study does not critically incorporate socioeconomic factors in flood risk management. Factors such as population growth, urbanization, and infrastructure development could significantly influence flood susceptibility but were not explicitly considered this assessment. Future studies should aim to integrate socioeconomic projections with climate and environmental data to offer a more holistic view of flood risks. in 4.4 Limitations in machine learning-based flood risk evaluation and policy consequences Although there is considerable progress, machine learning algorithms for flood as well as landslide vulnerability continue to encounter substantial limits concerning data quality along with model generalizability. Class imbalance as well as biased training datasets frequently result in skewed predictions, wherein non-event conditions predominate, thereby diminishing model reliability and obstructing precise risk identification in future climate scenarios, particularly when conditioning parameters are selected inconsistently across areas (e.g., topography, climate variables), which limits transferability and extrapolation efficacy. The dependency on historical as well as remote sensing datasets, devoid of substantial real-time inputs, constrains operational prediction capabilities, as numerous places lack the continuous high-resolution imagery and hydrological observation infrastructure necessary for dynamic prediction (Han and Semnani, 2025). Third, the ability to interpret of intricate deep learning models poses a barrier; although achieving great accuracy, their “black-box” characteristics diminish transparency and hinder stakeholders’ comprehension of the physical links between predictors and dangers. Ultimately, the majority of current assessments incidents or certain study regions, concentrate on singular prompting apprehensions regarding the ability of trained models to consistently generalize to novel flood or landslide scenarios across varied geographies or in response to changing climate conditions. among emphasize framework.

  generallimitationsevidence 5/5
  Keywords: flood climate models risk learning well environmental dynamic predictions socioeconomic factors infrastructure future machine landslide
• A GeoAI framework for coastal flood risk assessment: integrating remote sensing and socioeconomic data (2026) · Frontiers in Climate · doi

  There is a lack of studies on flood risk assessment in Bangladesh using GeoAI-based approaches. Prior studies have used static hazard zonation and historical flood frequency to assess flood risk, which have limitations. There is a need for a framework that integrates remote sensing and socioeconomic data to predict flood risk.

  generalstated research gapevidence 5/5
  Keywords: there lack studies flood risk assessment bangladesh using
• Flood mapping approaches: a review of models, data accuracy, limitations, and future perspectives (2026) · Natural Hazards · doi

  The integration of artificial intelligence and climate change scenarios in flood mapping. The development of more accurate and effective flood mapping methods. The use of ensemble-based probabilistic flood maps in operational flood forecasting systems.

  generalfuture-work sectionevidence 5/5
  Keywords: integration artificial intelligence climate change scenarios flood mapping
• Evaluating the feasibility of scaling the FIER framework for large-scale flood inundation prediction (2026) · Hydrology and earth system sciences · cited 1× · doi

  Traditional flood forecasting methods often face challenges in terms of computational demands and data requirements, particularly when applied to large geographic areas. The lack of detailed bathymetry and friction coefficients information in many regions limits the application of traditional hydrodynamic models. The need for a data-driven approach that can provide accurate and timely flood forecasts in large geographic areas.

  generalstated research gapevidence 5/5
  Keywords: traditional flood forecasting methods often face challenges terms
• Integrated Flood Susceptibility and Multi-Temporal Flood Risk Prioritization in Pakistan Using Hydro-Climatic and Geospatial Indicators (2026) · Hydrology · doi

  National-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. There is a need for a unified geospatial assessment to address the gap between flood risk assessment and prioritization.

  generalstated research gapevidence 5/5
  Keywords: national-scale studies often lack comprehensive assessment captures both
• A data-driven framework for assessing climatic impact drivers in the context of food security (2025) · Natural hazards and earth system sciences · cited 4× · doi

  The paper notes the challenge of creating interpretable models, particularly in regions lacking data availability. The study highlights the difficulty of assessing agricultural production risk through extreme climate indices. The paper identifies the challenge of balancing model performance, interpretability, and practical applications in machine learning models.

  generalstated challengesevidence 5/5
  Keywords: paper notes challenge creating interpretable models particularly regions
• How suitable are current approaches to simulate flood risk under future urbanization trends? (2024) · Environmental Research Letters · cited 15× · doi

  In regions with limited data, we argue that reducing the level of detail in flood models and increasing the level of detail in urbanization patterns should be considered to improve the quality of flood risk projections under future urbanization.

  generalabstractevidence 4/5
  Keywords: level detail flood urbanization regions limited argue reducing models increasing patterns considered improve quality risk
• Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China (2024) · Hydrology and earth system sciences · cited 12× · doi

  Although machine learning is increasingly employed for hydrological simulations, few studies have used it to project hydrological droughts, not to mention bivariate risks (referring to drought duration and severity) as well as their socioeconomic effects under climate change.

  generalabstractevidence 4/5
  Keywords: hydrological machine learning increasingly employed simulations used project droughts mention bivariate risks referring drought duration

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