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In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.
Huawei Li; Guohe Huang; Yongping Li; Jie Sun; Pangpang Gao. A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China. Sustainability 2021, 13, 4627 .
AMA StyleHuawei Li, Guohe Huang, Yongping Li, Jie Sun, Pangpang Gao. A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China. Sustainability. 2021; 13 (9):4627.
Chicago/Turabian StyleHuawei Li; Guohe Huang; Yongping Li; Jie Sun; Pangpang Gao. 2021. "A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China." Sustainability 13, no. 9: 4627.
In this study, a stepwise cluster modeling approach (SCMA) is developed for analyzing urban ecosystem variation via Normalized Difference Vegetation Index (NDVI). NDVI is an indicator of vegetation growth and coverage and useful in reflecting urban ecosystem. SCMA is established on a cluster tree that can characterize the complex relationship between independent and dependent variables. SCMA is applied to the City of Dongguan for simulating the urban NDVI and identifying associated drivers of human activity, topography and meteorology without specific functions. Results show that SCMA performances better than conventional statistical methods, illustrating the ability of SCMA in capturing the complex and nonlinear features of urban ecosystem. Results disclose that human activities play negative effects on NDVI due to the destruction of green space for pursuing more space for buildings. NDVI reduces gradually from the south part to the north part of Dongguan due to increased gross domestic product and population density, indicating that the ecosystem in Dongguan is better in the south part. NDVI in the northeast part (dominated by agriculture) is sensitive to the growth of economy and population. More attention should be paid to this part for sustainable development, such as increasing afforestation, planting grass and constructing parks. Precipitation has a positive effect on NDVI due to the promotion of soil moisture that is beneficial to plants’ growth. Awareness of these complexities is helpful for sustainable development of urban ecosystem.
J. Sun; Y.P. Li; P.P. Gao; C. Suo; B.C. Xia. Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach. Environmental Research 2018, 166, 276 -289.
AMA StyleJ. Sun, Y.P. Li, P.P. Gao, C. Suo, B.C. Xia. Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach. Environmental Research. 2018; 166 ():276-289.
Chicago/Turabian StyleJ. Sun; Y.P. Li; P.P. Gao; C. Suo; B.C. Xia. 2018. "Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach." Environmental Research 166, no. : 276-289.