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赵铜铁钢, 杨大文. 神经网络径流预报模型中基于互信息的预报因子选择方法[J]. 水力发电学报, 2011, 20(1): 24-30. ZHAO Steel, YANG Dawen. Mutual information-based input variable selection method for runoff-forecasting neural network model. Journal of Hydroelectric Engineering, 2011, 20(1): 24-30. (in Chinese)

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• 标题: 熵理论及其在水文水资源中的应用研究Entropy Theory and Its Application in Hydrology and Water Resources

  作者: 熊丰, 陈璐, 张俊宏

  关键字: 水文水资源, 熵理论, 最大熵原理, 相关性分析, 应用研究Hydrology and Water Resources, Entropy Theory, Principle of Maximum Entropy, Correlation Analysis, Application

  期刊名称: 《Journal of Water Resources Research》, Vol.5 No.1, 2016-02-05

  摘要: 水文水资源系统是一个复杂的非线性系统，研究水资源系统中的不确定信息以及水文事件的相关性属性具有重要意义。熵理论是进行水文不确定性度量和相关性分析的有效方法。本文综述了最大熵原理和基于熵理论的相关性分析方法，探究了其在水文水资源学科中的应用，分析了其特点、优势和存在的问题，并对熵理论今后的研究方向进行了展望。 Hydrology and water resources system is a complex and nonlinear system. It is of great significance to study how to deal with the uncertainty in water resources system and analyze the correlation among hydrological variables. Entropy theory can measure the uncertainty of hydrological information and analyze the dependences among hydrological variables. In this paper, the principle of maximum entropy (POME) and the correlation analysis method based on entropy theory were introduced. The application of entropy theory in hydrology and water resources was reviewed. The characteristics, advantages and disadvantages of these methods were analyzed. Finally, the future research on entropy theory and its application in hydrology and water resources was discussed.