Show simple item record

Quantifying Irrigation Water Demand through Optimized Daily Vapor Pressure Deficit Forecasting Using LSTM and Metaheuristic Algorithms

dc.contributor.authorAlsumaiei, Abdullah A.
dc.date.accessioned2026-08-20T21:27:42Z
dc.date.available2026-08-20T21:27:42Z
dc.date.copyright2025/11/26
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-7139.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314491
dc.description.abstractAbstractThis study presents a robust computational framework that integrates long short-term memory (LSTM) neural networks with four advanced metaheuristic optimization algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm ...
dc.publisherAmerican Society of Civil Engineers
dc.titleQuantifying Irrigation Water Demand through Optimized Daily Vapor Pressure Deficit Forecasting Using LSTM and Metaheuristic Algorithms
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue2
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-7139
dc.journal.fristpage04025155-1
dc.journal.lastpage04025155-13
dc.page13
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002
dc.contenttypeFulltext


Files in this item

Thumbnail
Name:
JCCEE5.CPENG-7139.pdf
Size:
3.520Mb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record