相关实验视频

Updated: Sep 13, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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同时识别地下水污染源和基于Rime优化算法的模拟模型参数

Xiao Wang1,2,3, Wenxi Lu4,5,6, Zibo Wang1,2,3

  • 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China.

Environmental monitoring and assessment
|August 2, 2025
PubMed
概括

本研究引入了使用一维卷积神经网络 (1DCNN) 和 rime优化算法 (RIME) 的地下水污染源识别 (GCSI) 的新框架. 该方法准确地识别了污染源和含水层参数,改善了整治工作.

关键词:
地下水污染源的识别和识别一维卷积神经网络的一个维度.里姆优化算法 里姆优化算法模拟优化方法的模拟优化方法替代模型的替代模型

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A Protocol for Conducting Rainfall Simulation to Study Soil Runoff10:35

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Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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