An understanding of the potential for these algorithms

The gap

Evidence profile

Stated in the cells research gap and abstract sections of the source papers, classified as general, drawn from work published between 2023 and 2026, spanning 5 journals.

Supporting evidence — 5 representative gaps

• Machine learning applications for modeling and mapping soil erosion in tropical regions (2026) · ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences · doi

  The study identifies a gap in the development of reliable susceptibility models for soil erosion. - The study highlights the need for effective machine learning algorithms for soil erosion susceptibility mapping.

  generalstated in cells research gapevidence 5/5
  Keywords: study identifies gap development reliable susceptibility models soil
• Toward a hybrid machine Learning–physics framework for cohesive soil erosion in climate-resilient civil infrastructure (2026) · Frontiers in Built Environment · doi

  Existing methods have limitations, including empirical models that are highly variable and sensitive to soil composition, and mechanistic approaches that are computationally intensive. - There is a need for a more accurate and reliable approach for cohesive soil erosion modeling.

  generalstated in cells research gapevidence 5/5
  Keywords: existing methods have limitations including empirical models highly
• 25-year, quarterly land change maps of China's Loess Plateau reveal long-term and substantial water-induced soil erosion mitigation (2026) · Earth System Science Data · doi

  However, the evolution of soil erosion in these regions remains poorly quantified due to the absence of high-resolution, long-term, and high-frequency monitoring data.

  generalstated in abstractevidence 4/5
  Keywords: high evolution soil erosion regions remains poorly quantified absence resolution long term frequency monitoring
• Determining soil erosion rates on a grazed Australian hillslope: Comparison of two landform evolution models with field‐based methods (2026) · Earth Surface Processes and Landforms · doi

  Although various landform evolution models (LEMs) have been developed to simulate erosion processes and landscape change, relatively few studies have directly compared modelled outputs with field‐based erosion estimates.

  generalstated in abstractevidence 4/5
  Keywords: erosion various landform evolution models lems developed simulate processes landscape change relatively directly compared modelled
• Soil water erosion susceptibility assessment using deep learning algorithms (2023) · Journal of Hydrology · doi

  However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of soil erosion susceptibility is lacking.

  generalstated in abstractevidence 4/5
  Keywords: understanding potential algorithms provide fast cheap accurate predictions soil erosion susceptibility lacking

Questions about this gap