Assessment of land degradation susceptibility within the Shaqlawa subregion of Northern Iraq-Kurdistan Region via synergistic application of remotely acquired datasets and advanced predictive models
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View abstract on PubMed
Summary
This summary is machine-generated.Land degradation, including soil erosion, landslides, and rockfalls, was analyzed using 22 factors and machine learning. Fisher
Area Of Science
- Geosciences
- Environmental Science
- Remote Sensing
Background
- Land degradation (LD) reduces land functionality due to natural and human drivers.
- Soil erosion, landslides, and rockfalls are prevalent LD types in the Shaqlawa district.
- Understanding LD is crucial for sustainable land management and hazard mitigation.
Purpose Of The Study
- To identify and map land degradation susceptibility in the Shaqlawa district.
- To evaluate the effectiveness of various machine learning algorithms in predicting LD.
- To pinpoint key conditioning factors influencing land degradation.
Main Methods
- Integration of 22 land degradation conditioning factors (e.g., TWI, NDVI, lithology, slope).
- Statistical analysis using Variance Inflation Factor (VIF) and Tolerance (TOL) for multicollinearity.
- Application and comparison of machine learning algorithms: RF, Naive Bayes, LR, Rotation Forest, FPA, FLDA.
- Model validation using Receiver Operating Characteristic (ROC) curves.
Main Results
- Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), and lithology were identified as key LD predictors.
- Fisher's Linear Discriminant Analysis (FLDA) model indicated the highest very high degradation risk (26.72%).
- Random Forest (RF) showed superior performance on the training dataset (AUC=0.882), while FLDA excelled on the testing dataset (AUC=0.883).
Conclusions
- Machine learning models effectively categorize land degradation susceptibility.
- Identifying LD-prone areas is vital for developing targeted mitigation strategies.
- The study provides valuable data for land-use planners and emergency managers in similar terrains.
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