Integrating Hydrological, Physical, and Chemical Factors for Soil Moisture Prediction Using Advanced Machine Learning Models

dc.contributor.author	Al-Juaidi, Ahmed E. M.
dc.date.accessioned	2026-08-20T11:08:05Z
dc.date.available	2026-08-20T11:08:05Z
dc.date.copyright	2026/06/06
dc.date.issued	2026
dc.identifier.other	JHYEFF.HEENG-6811.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311738
dc.description.abstract	AbstractAccurately predicting soil moisture content (SMC) is critical for effective water management and sustainable agriculture. This study demonstrates that combining hydrological factors, such as rainfall, with soil physical characteristics—including ...
dc.publisher	American Society of Civil Engineers
dc.title	Integrating Hydrological, Physical, and Chemical Factors for Soil Moisture Prediction Using Advanced Machine Learning Models
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-6811
dc.journal.fristpage	04026025-1
dc.journal.lastpage	04026025-15
dc.page	15
dc.tree	Journal of Hydrologic Engineering:;2026:;Volume ( 031 ):;issue: 004
dc.contenttype	Fulltext