Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks

Autor:	P. Distefano, D. J. Peres, P. Scandura, A. Cancelliere
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation Environmental sciences GE1-350 Geology QE1-996.5
Zdroj:	Natural Hazards and Earth System Sciences, Vol 22, Pp 1151-1157 (2022)
Druh dokumentu:	article
ISSN:	1561-8633 1684-9981
DOI:	10.5194/nhess-22-1151-2022
Popis:	In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy) show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow other variables to be easily added, like peak rainfall intensity, with a further performance improvement (TSS = 0.66). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/974ee26a001e467f9ad4c2156d21c083 Zobrazit plný text záznamu View record in DOAJ