【作者】 梅松；

【导师】 刘国华；

【作者基本信息】 浙江大学 ， 防灾减灾工程及防护工程， 2004， 博士

【摘要】 洪水预报是一项重要的防灾减灾非工程措施，而以往的洪水预报方法较为复杂，建模工作要求较高，不易普及。从上世纪八十年代以来，以人工神经网络技术为代表的机器学习技术逐渐应用于洪水预报领域，并取得了一定的成果。本文结合机器学习技术，从寻找易用的、准确的、可靠的、实用性强的洪水预报方法的角度出发，建立了多种基于新型的学习机器的洪水预报模型，并通过这些模型在实例中的表现，对它们的性能进行了评价，提出了几种基于学习机器的洪水预报解决方案。本文的主要内容包括： (1) 对Elman型回馈人工神经网络模型进行了介绍，并对利用Elman型人工神经网络进行洪水预报动态建模中数据降噪、训练控制、模型结构选择等关键技术问题进行了较为深入的探讨，对采用不同模型输入时模型的预报效果进行对比。实例结果表明该模型是有效的，并具有强壮性、外延性等优点。 (2) 针对人工神经网络建模模型规模较大的问题，引入了因素分析法，通过这种方法，缩减了模型参数的规模，在不降低模型效果的前提下，减少了模型训练和模拟的计算量，加强了模型的实用性。 (3) 通过建立复合型型人工神经网络模型的方法，有效的利用给定水文系统的先验知识为人工神经网络模型的建模提供指导，使得建立出的模型更具合理性，该方法不同于传统的人工神经网络建立方法，为基于人工神经网络的洪水预报建模提供了一种新的思路。 (4) 支持向量机(SVM)是一种适于在小样本情况下进行建模的学习机器。本文参考前人的工作，对统计学习理论和支持向量机的相关知识进行了介绍，分析了SVM模型的特点，并对选用不同的模型和参数对支持向量机模型的影响进行了探讨。 (5) 根据支持向量机的特点，建立了固定训练样本集的SVM洪水预报模型、固定训练样本集长度的动态递推SVM洪水预报模型和带记忆的动态递推SVM洪水预报模型三种基于SVM的洪水预报模型，它们在实例中的表现体现出了良好的应用前景。 (6) 针对各种基于学习机器的洪水预报模型，文中通过实例对它们的预报效果进行了评价，较为系统的对比了它们的特点，为它们在洪水预报领域内的产品化提供了指导。 (7) 提出了洪水预报模型预评估机(PEM)的概念，介绍了其工作原理，并提出了基于相似系数、基于人工神经网络和基于支持向量机三种类型的预评估机模型。指出了建立洪水预报模型的预评估机模型的可行性，并对在建立预评估机模型过程中仍存在的困难进行了初步的探讨。更多还原

【Abstract】 Flood forecasting is an important nonstructural measure of natural disaster prevention and alleviation. The traditional flood forecast methods are relatively complex and hard to be popularized. Since the early nineties last century, machine learning techniques such as artificial neural networks (ANN) have been attempted in flood forecast areas, such as rainfall-runoff modeling and stream flow forecasting, with some valuable experiences achieved. This paper presents several precise, reliable and practical flood forecast models based on some new style learning machines. Their performances were valued in case studies. The components of this paper are listed as follow:(1) The characteristics of Elman recurrent neural networks are introduced. The main course of modeling, including data noise reducing, training controlling and model structure selecting, is proposed. The model’s performances, when employing different kinds of input data, have been compared. The performance in case study indicates that flood forecast modeling using Elman ANN is a flexible, extendable and accurately method.(2) The huge size of ANNs is a frustration when they were applied in practice. Principle component analysis (PCA) can be used to reduce the amount of input elements so that the size of ANN will shrink relevantly. Also the training time can be shorten with little lose or even no lose of accuracy.(3) ANNs are often viewed as Black Box models whose parameters don’t have any physical meaning. And the structures of ANNs are similar in different hydrologic systems, by this mean, the basic information such as distributing of hydrometric stations can’t be utilized. This paper presents a new flood forecast model based on complex ANN, which can make the information of hydrologic systems as guidance when constructing the structure of ANN. The parameters of complex ANN have physical meanings to some extent and the whole model is more reasonable.(4) Support vector machine (SVM) is a novel powerful learning machine, which can solve small-sample learning problem better. The basic ideas of statistical learning theory (SLT) and SVM are introduced, and the characteristics of SVM are illuminated. Meanwhile, the SVM’s parameters selection method and the representations of different models are researched.(5) Three types of flood forecast models based on SVM are presented. These models are SVM flood forecast model with changeless training set, dynamic recursion SVM flood forecast model with fixed length training set and dynamic recursion SVM flood forecast model with memory. It is proved to be promising and valuable for these models with experimental results.(6) All these flood forecast models are examined in case studies, and their characteristics are obtained by performances evaluating, which can be considered as guidance in practical use.(7) The concept and principle of pre-evaluation machine (PEM) of flood forecast models are described. PEM models based on resemblance coefficient, ANN and SVM are posed. The feasibility and difficulties of their application are discussed.更多还原