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首页> 外文期刊>Journal of Hydroinformatics >Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study
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Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study

机译：改进神经遗传算法预测短期水位的适用性：案例研究

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摘要

This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of river infrastructure. The authors build an artificial neural network model coupled with the genetic algorithm (often called a hybrid neuro-genetic algorithm), and then apply the model to predict water levels at 15 points of four major rivers in Korea. This case study demonstrates that the approach can be highly compatible with the real river situations, such as hydrological disturbances and water infrastructure under emergencies. Therefore, proper adoption of this approach into a river management system certainly improves the adaptive capacity of the system.
机译：本文提出了一种实用的神经遗传算法方法，以增强其预测河流水位的能力。它的实用性具有三个属性：（1）使用神经遗传算法轻松开发模型； （2）在不同条件下的各个预测点对模型进行验证； （3）提供信息，以便对河流基础设施的运行作出紧急决定。作者建立了与遗传算法（通常称为混合神经遗传算法）耦合的人工神经网络模型，然后将该模型应用于预测韩国四大河流15个点的水位。该案例研究表明，该方法可以与实际河流情况高度兼容，例如紧急情况下的水文干扰和水利基础设施。因此，将这种方法正确地应用到河流管理系统中无疑会提高系统的适应能力。

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