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Updated: Jun 24, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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集成的元启发算法与极端学习机器模型用于河流流量预测

Nguyen Van Thieu1, Ngoc Hung Nguyen2, Mohsen Sherif3,4

  • 1Faculty of Computer Science, PHENIKAA University, Yen Nghia, Ha Dong, Hanoi, 12116, Viet Nam. thieu.nguyenvan@phenikaa-uni.edu.vn.

Scientific reports
|June 12, 2024
PubMed
概括

新的混合模型将极端学习机器 (ELM) 与数学元启发学相结合,显著改善了河流流量预测. 这些先进的模型为水资源管理和降低洪水风险提供了更高的准确性和稳定性.

关键词:
极端学习的机器学习.预测模型的预测模型超启发式优化算法 超启发式优化算法灵感来自自然的算法.尼罗河 尼罗河 尼罗河河流流程 河流流程 流程

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