Predicting the Meteorological Drought based on the NDVI and Rainfall by using Long Short-Term Memory Algorithm

dc.contributor.author	Maung, Thiri
dc.contributor.author	Thein, Thin Lai Lai
dc.date.accessioned	2022-07-05T04:04:02Z
dc.date.available	2022-07-05T04:04:02Z
dc.date.issued	2021-02-25
dc.identifier.uri	https://onlineresource.ucsy.edu.mm/handle/123456789/2724
dc.description.abstract	Myanmar is exposed to almost all types of natural hazards. Drought is a slow onset hazard type. The central dry zone in Myanmar is the most vulnerable area of the country from drought. The frequency and severity of drought are getting increased year after year because of global warming and climate change. This paper examines the relationship between rainfall and NDVI from 2015 to 2019 by using Google Earth Engine (GEE) to observe the meteorological drought in the past which is useful for identifying the drought response action plans and tries to predict the meteorological drought in the future by using long short-term memory algorithm.	en_US
dc.language.iso	en_US	en_US
dc.publisher	ICCA	en_US
dc.subject	drought, NDVI, GEE, GIS, disaster management, LSTM	en_US
dc.title	Predicting the Meteorological Drought based on the NDVI and Rainfall by using Long Short-Term Memory Algorithm	en_US
dc.type	Presentation	en_US