基于自适应网络模糊推理方法在河道洪水预报中的应用

对于易受洪灾的地区而言，快速而准确的洪水预报非常重要，能够为洪水预警消息的发布提供更长的先导时间，从而为可能受灾地区的人们提供更充足的时间以采取相应的防洪措施或安全转移。 常用的预报模型包括基于物理性模型和基于系统技术模型。尽管物理性模型能对洪水形成的物理过程提供很好的解释, 水文学家并不愿意使用它们，因为模型中参数的率定是比较复杂的。因此,一种基于纯数据集的黑箱技术已被广泛采纳。常用的黑箱模型包括线性模型(LR)、自回归移动平均模型(ARMA)和人工神经网络模型（ANN）等。 在当前的研究中，一个相对新颖的黑箱模型--基于自适应网络的模糊推理系统（ANFIS）被用来对长江某河段的洪水进行预报。与此同时，一个线性回归模型（LR）用来作为ANFIS模型的对照。在构建ANFIS中，混合学习算法 (即误差反衍（BP）耦合最小二乘法（LSE）) 用来训练模型的参数。此外，为避免出现过度训练现象，原始数据集基于统计特征值划分成3个子集：训练集、测试集和校正集。当对ANFIS模型训练时，测试集用来帮助控制训练代数。结果表明，ANFIS的预报效果优于LR模型。分析认为ANFIS能够提供预报精度是因为其采用了局部拟合技术，通常它会优于LR模型所采用的全局拟合技术。最后，对本研究而言，最适合的ANFIS模型是输入量为梯形的成员度函数。

APPLICATION OF ANFIS IN FLOOD PREDICTION IN MAIN STREAM OF THE YANGTZE RIVER

As far as a flood-prone region is concerned,a rapid and accurate flood forecasting is especially significant because it can extend the lead time for issuing disaster warnings,thus allowing sufficient time for people in hazardous areas to take appropriate action,such as evacuation.Although they give a deep clairvoyance to physical mechanism of flood forming,conventional conceptual forecasting models are inconvenient for operational hydrologists in practice.Therefore,many called"black box"models based on systems theoretic techniques,such as linear regression (LR),autoregressive moving average (ARMA), and artificial neural network (ANN),are employed.Compared with conceptual models,often they can provide a rapid prediction with an accepted degree of accuracy in view of depending only on data-driven techniques.In the present study,a relative novel black box technology,namely adaptive-network-based fuzzy inference system (ANFIS) in which Takagi and Segeno's rule was adopted,was proposed for streamflow forecasting in the main channel of the Yangtze River.In the meantime,a linear regression (LR) model was used as the benchmark for ANFIS model evaluation.In the ANFIS model,back propagation (BP) learning algorithm and hybrid learning algorithm (Combined BP and least squared error) were applied to the model,respectively.In addition,in order to avoid overfitting of training data,a statistic information-based data partition technique was used to split raw data into three parts:training data,testing data,and validation data.Of them,testing data played a role as early stopping,which helps obtain the optimal training epoch during addressing training data.Results showed that ANFIS model is superior to the LR model,and the optimal model is the ANFIS model with hybrid learning algorithm and trapezoidal membership functions for the present case.A further analysis revealed the powerful capability of ANFIS is due to the local linear approximation technique being employed in ANFIS model,which improve the capturing capacity for training data if the overfitting can be well controlled.