A Non-Stationary Framework for Landslide Hazard Assessment Under the Extreme Rainfall Condition
Author:
Funder
Department of Science and Technology, Government of India
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41748-024-00445-6.pdf
Reference70 articles.
1. Abraham MT, Pothuraju D, Satyam N (2019) Rainfall thresholds for prediction of landslides in Idukki, India: an empirical approach. Water 11:2113. https://doi.org/10.3390/w11102113
2. Abraham MT, Satyam N, Rosi A, Pradhan B, Segoni S (2020) The selection of rain gauges and rainfall parameters in estimating intensity-duration thresholds for landslide occurrence: case study from Wayanad (India). Water 12:1000. https://doi.org/10.3390/w12041000
3. Abraham MT, Satyam N, Lokesh R, Pradhan B, Alamri A (2021) Factors affecting landslide susceptibility mapping: assessing the influence of different machine learning approaches. Sampl Strateg Data Split Land 10(9):989. https://doi.org/10.3390/land10090989
4. Adarsh S, Karthik S, Shyma M, Das PG, Parveen ATS, Narayan S (2018) Developing short term drought severity-duration-frequency curves for Kerala meteorological subdivision, India using bivariate copulas. KSCE J Civ Eng 22:962–973. https://doi.org/10.1007/s12205-018-1404-9
5. Afungang RN, Bateira CV (2016) Temporal probability analysis of landslides triggered by intense rainfall in the Bamenda mountain region, Cameroon. Environ Earth Sci. https://doi.org/10.1007/s12665-016-5835-7
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