Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems
Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems
复制标题
基于假设归纳数据的水文系统机制建模
DOI：
10.1002/wrcr.20068
复制
发表时间：
2013
影响因子：
5.4
通讯作者：
P. Young
中科院分区：
文献类型：
--
作者：
P. Young
本文介绍了基于数据的机械（DBM）建模的逻辑扩展，它提供了假设归纳（HI-DBM）之间的桥梁概念模型，推导出一个假设演绎的方式，DBM模型从相同的时间序列数据归纳识别。该方法通过一个非常详细的应用于著名的叶河数据集和相关的HyMOD概念模型的HI-DBM分析的例子来说明。HI-DBM模型显著改善了对叶河数据的解释，并增强了原始DBM模型的性能。然而，在各种诊断测试的基础上，包括递归时间变量和状态相关参数估计，建议该模型应能够进一步改进，特别是在概念上的有效降雨机制，这是基于概率分布模型假设。为了验证HI-DBM分析在生成数据的实际模型完全已知的情况下的有效性，还将该分析应用于基于修改的HyMOD模型的随机模拟模型。
The paper introduces a logical extension to data‐based mechanistic (DBM) modeling, which provides hypothetico‐inductive (HI‐DBM) bridge between conceptual models, derived in a hypothetico‐deductive manner, and the DBM model identified inductively from the same time‐series data. The approach is illustrated by a quite detailed example of HI‐DBM analysis applied to the well‐known Leaf River data set and the associated HyMOD conceptual model. The HI‐DBM model significantly improves the explanation of the Leaf River data and enhances the performance of the original DBM model. However, on the basis of various diagnostic tests, including recursive time‐variable and state‐dependent parameter estimation, it is suggested that the model should be capable of further improvement, particularly as regards the conceptual effective rainfall mechanism, which is based on the probability distributed model hypothesis. In order to verify the efficacy of the HI‐DBM analysis in a situation where the actual model generating the data is completely known, the analysis is also applied to a stochastic simulation model based on a modified HyMOD model.