Quantifying Irrigation Water Demand through Optimized Daily Vapor Pressure Deficit Forecasting Using LSTM and Metaheuristic AlgorithmsSource: Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002::page 04025155-1Author:Alsumaiei, Abdullah A.
DOI: 10.1061/JCCEE5.CPENG-7139Publisher: American Society of Civil Engineers
Abstract: AbstractThis 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 ...
|
Collections
Show full item record
| contributor author | Alsumaiei, Abdullah A. | |
| date accessioned | 2026-08-20T21:27:42Z | |
| date available | 2026-08-20T21:27:42Z | |
| date copyright | 2025/11/26 | |
| date issued | 2026 | |
| identifier other | JCCEE5.CPENG-7139.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314491 | |
| description abstract | AbstractThis 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | Quantifying Irrigation Water Demand through Optimized Daily Vapor Pressure Deficit Forecasting Using LSTM and Metaheuristic Algorithms | |
| type | Journal Article | |
| journal volume | 40 | |
| journal issue | 2 | |
| journal title | Journal of Computing in Civil Engineering | |
| identifier doi | 10.1061/JCCEE5.CPENG-7139 | |
| journal fristpage | 04025155-1 | |
| journal lastpage | 04025155-13 | |
| page | 13 | |
| tree | Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002 | |
| contenttype | Fulltext |