Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-022-05588-1.pdf
Reference50 articles.
1. Abraham MT, Pothuraju D, Satyam N (2019) Rainfall thresholds for prediction of landslides in Idukki, India: an empirical approach. Water 11(10):2113
2. Abraham MT, Satyam N, Pradhan B, Alamri AM (2020) Forecasting of landslides using rainfall severity and soil wetness: a probabilistic approach for Darjeeling Himalayas. Water 12(3):804
3. Al-Najjar HA, Kalantar B, Pradhan B, Saeidi V (2019) 'Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms. In: Earth resources and environmental remote sensing/GIS applications X. International Society for Optics and Photonics, p 111560K
4. Beck HE, Pan M, Miralles DG, Reichle RH, Dorigo WA, Hahn S et al (2021) Evaluation of 18 satellite-and model-based soil moisture products using in situ measurements from 826 sensors. Hydrol Earth Syst Sci 25(1):17–40
5. Bordoni M, Meisina C, Valentino R, Lu N, Bittelli M, Chersich S (2015) Hydrological factors affecting rainfall-induced shallow landslides: from the field monitoring to a simplified slope stability analysis. Eng Geol 193:19–37
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1. Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm;Mathematics;2023-06-24
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