Many existing landslide inventories lack type-specific

The gap

Evidence profile

Sourced from the limitations and stated research gap and future-work section of the source papers, classified as general, spanning 6 journals.

Supporting evidence — 6 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
• Predictive Modeling of Flash Floods: Investigating Hydrology and Land Cover Dynamics through Remote Sensing Data (2026) · Journal of Earth Observation and Geospatial Applications · doi

  The lack of large-scale public participation and access to predictive models. The gap in preparedness for climate-driven disasters throughout the United States. The need for a reliable predictive model that can identify high-risk zones and inform flood mitigation strategies.

  generalstated research gapevidence 5/5
  Keywords: lack large-scale public participation access predictive models gap
• Predicting the flood susceptibility under land use and climate change scenarios using deep learning algorithms (2026) · Scientific Reports · doi

  Future studies should use more historical data or hybrid models (e.g., CA-Markov with ML) to project land-use changes. Future studies should explore the use of other deep learning algorithms or machine learning techniques to model flood generation potential.

  generalfuture-work sectionevidence 5/5
  Keywords: future studies use historical data hybrid models ca-markov
• A Transferable Machine Learning Approach for Identifying Rainfall-Induced Cliff-Type (Shallow) Landslides in Seismic and Non-Seismic Regions (2026) · Water · doi

  Many existing landslide inventories lack type-specific information. Limited applicability of existing landslide inventories in risk management. Need for a transferable machine learning framework to identify rainfall-induced cliff-type landslides.

  generalstated research gapevidence 5/5
  Keywords: many existing landslide inventories lack type-specific information limited

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