Performance Evaluation and Uncertainty Quantification of Deep Learning Models for Agricultural Drought Prediction

dc.contributor.author	Das K., Saranya
dc.contributor.author	Chithra, N. R.
dc.date.accessioned	2026-08-20T11:07:56Z
dc.date.available	2026-08-20T11:07:56Z
dc.date.copyright	2026/05/16
dc.date.issued	2026
dc.identifier.other	JHYEFF.HEENG-6751.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311733
dc.description.abstract	AbstractEarly prediction of agricultural drought is critical for minimizing its adverse impacts. Although numerous studies have addressed drought forecasting, limited attention has been given to the uncertainty analysis of predictive models. This study ...
dc.publisher	American Society of Civil Engineers
dc.title	Performance Evaluation and Uncertainty Quantification of Deep Learning Models for Agricultural Drought Prediction
dc.type	Journal Article
dc.journal.volume	31
dc.journal.issue	4
dc.journal.title	Journal of Hydrologic Engineering
dc.identifier.doi	10.1061/JHYEFF.HEENG-6751
dc.journal.fristpage	04026013-1
dc.journal.lastpage	04026013-13
dc.page	13
dc.tree	Journal of Hydrologic Engineering:;2026:;Volume ( 031 ):;issue: 004
dc.contenttype	Fulltext