Cloud Segmentation, Validation of Weather Data, and Precipitation Prediction Using Machine Learning Algorithms

Rajendiran, Nagaraj; Sebastian, Sruthy; Kumar, Lakshmi Sutha

doi:10.1007/s13369-023-08611-0

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2024

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Cited by 4 publications

References 66 publications

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Credit Rating Prediction Based on WOA-Cross-Validation-LightGBM

Sun

2024

2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE)

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Credit Rating Prediction Based on WOA-Cross-Validation-LightGBM

Sun

2024

2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE)

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Surface Water Mapping Using Machine Learning and Deep Learning Techniques from LISS-III Imagery

Kumar

2024

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)

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Enhanced Landslide Susceptibility Mapping Using Machine Learning and InSAR Integration: A Case Study in Wushan County, Three Gorges Reservoir Area, China

Cui,

Kou,

Tao

et al. 2024

Preprint

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Landslides pose a severe threat to the safety of mountainous regions, and existing landslide susceptibility assessment methods often suffer from limitations in data quality and methodology. This study focused on Wushan County, China, combining machine learning algorithms with InSAR data to improve the accuracy of landslide susceptibility mapping. Employing seven machine learning models, the investigation identified CNN, LR, and RF as the most effective, with AUC values of 0.82, demonstrating their ability to predict landslide-prone areas. Key influencing factors for landslides included digital elevation model (DEM), rainfall, lithology, normalized difference vegetation index (NDVI), terrain curvature, roughness, and distances to roads and rivers. Integrating InSAR data significantly enhanced the accuracy of landslide susceptibility mapping, particularly in areas with high deformation, refining assessments and reducing misclassifications. Slope analysis and InSAR monitoring provided insights into instability mechanisms, highlighting InSAR's potential for early warning systems. The study concludes that combining InSAR with machine learning holds promise for improving landslide susceptibility mapping. Future research should explore advanced machine learning techniques and other remote sensing data to address the impacts of climate change and seasonal variations on slope stability, ultimately supporting disaster risk management and sustainable land-use planning.

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Cloud Segmentation, Validation of Weather Data, and Precipitation Prediction Using Machine Learning Algorithms

Cited by 4 publications

References 66 publications

Credit Rating Prediction Based on WOA-Cross-Validation-LightGBM

Credit Rating Prediction Based on WOA-Cross-Validation-LightGBM

Surface Water Mapping Using Machine Learning and Deep Learning Techniques from LISS-III Imagery

Enhanced Landslide Susceptibility Mapping Using Machine Learning and InSAR Integration: A Case Study in Wushan County, Three Gorges Reservoir Area, China

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