ARTIFICIAL NEURAL-NETWORK MODELING OF THE RAINFALL-RUNOFF PROCESS
ARTIFICIAL NEURAL-NETWORK MODELING OF THE RAINFALL-RUNOFF PROCESS
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DOI：
10.1029/95wr01955
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发表时间：
1995-10-01
影响因子：
5.4
通讯作者：
SOROOSHIAN, S
中科院分区：
文献类型：
--
作者：
HSU, KL;GUPTA, HV;SOROOSHIAN, S
人工神经网络（ANN）是一种灵活的数学结构，能够识别输入和输出数据集之间的复杂非线性关系。人工神经网络模型已被发现是有用的和有效的，特别是在问题的过程中的特性是很难描述使用物理方程。本研究提出了一种新的程序（题为线性最小二乘单纯形，或LLSSIM），用于识别三层前馈神经网络模型的结构和参数，并展示了这种模型模拟流域的非线性水文行为的潜力。非线性人工神经网络模型的方法，提供了一个更好的代表性的中型叶河流域附近的柯林斯，密西西比，比线性ARMAX（自回归移动平均与外源输入）时间序列的方法或概念SAC-SMA（萨克拉门托土壤水分会计）模型的径流关系。由于这里提出的人工神经网络方法不提供具有物理上真实的组件和参数的模型，它绝不是概念流域建模的替代品。然而，人工神经网络的方法提供了一个可行的和有效的替代ARMAX时间序列的方法开发投入产出模拟和预测模型的情况下，不需要建模的内部结构的流域。
An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been found useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using physical equations. This study presents a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three-layer feed forward ANN models and demonstrates the potential of such models for simulating the nonlinear hydrologic behavior of watersheds. The nonlinear ANN model approach is shown to provide a better representation of the rainfall-runoff relationship of the medium-size Leaf River basin near Collins, Mississippi, than the linear ARMAX (autoregressive moving average with exogenous inputs) time series approach or the conceptual SAC-SMA (Sacramento soil moisture accounting) model. Because the ANN approach presented here does not provide models that have Physically realistic components and parameters, it is by no means a substitute for conceptual watershed modeling. However, the ANN approach does provide a viable and effective alternative to the ARMAX time series approach for developing input-output simulation and forecasting models in situations that do not require modeling of the internal structure of the watershed.