Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology
Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology
复制标题
使用基于机器学习算法的模型参数区域化方法对非计量流域径流进行建模
DOI：
10.1016/j.eng.2021.12.014
复制
发表时间：
2023-12-15
期刊：
影响因子：
12.8
通讯作者：
Wang，Jie
中科院分区：
文献类型：
--
作者：
Wu，Houfa;Zhang，Jianyun;Wang，Jie
模型参数估计是无资料流域径流模拟的关键问题。模型参数和流域描述符之间的非线性关系是参数区域化的主要障碍，而参数区域化是应用最广泛的方法。对黄淮海流域38个流域的径流模型进行了研究。纳什-萨克利夫效率系数(NSE)、决定系数(R2)和偏倚百分比(PBIAS)的值表明土壤和水评价工具(SWAT)模型在青藏高原土壤资源评价系统中的表现是可以接受的。用气候、土壤、植被和地形等9个描述符来描述与水文过程有关的流域特征。利用线性回归(LR)方程、支持向量回归(SVR)、随机森林(RF)、k近邻(KNN)、决策树(DT)和径向基函数(RBF)等6种回归模型分析了SWAT模型参数与流域描述符之间的定量关系。38个集水区中的每一个都被假定为一个未测量的集水区。然后，根据剩余的37个供给者集水区建立的回归模型，对每个目标集水区的参数进行估计。此外，使用基于相似度的区划方案与基于回归的方法进行了比较。结果表明，基于支持向量机的流域径流模型精度最高。与传统的基于LR的方法相比，由于机器学习算法对非线性关系的处理能力突出，提高了无资料流域径流模拟的精度。在潮湿地区，不同方法的性能相似，而在干旱地区，机器学习技术的优势更为明显。当研究区内有嵌套流域时，由于流域密度大、空间距离短，采用基于相似系数的参数区划方法计算结果最好。新的发现可能会改善缺乏观测数据的地区的洪水预报和水资源规划。
Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin (YHHRB). The values of the Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and percent bias (PBIAS) indicated the acceptable performance of the soil and water assessment tool (SWAT) model in the YHHRB. Nine descriptors belonging to the categories of climate, soil, vegetation, and topography were used to express the catchment characteristics related to the hydrological processes. The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models, including linear regression (LR) equations, support vector regression (SVR), random forest (RF),k-nearest neighbor (kNN), decision tree (DT), and radial basis function (RBF). Each of the 38 catchments was assumed to be an ungauged catchment in turn. Then, the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments. Furthermore, the similarity-based regionalization scheme was used for comparison with the regression-based approach. The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments. Compared with the traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships. The performances of different approaches were similar in humid regions, while the advantages of the machine learning techniques were more evident in arid regions. When the study area contained nested catchments, the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance. The new findings could improve flood forecasting and water resources planning in regions that lack observed data.