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Partial Least Squares Regression for Determining the Control Factors for Runoff and Suspended Sediment Yield during Rainfall Events
by
Wang, Yixia
, Fang, Nufang
, Shi, Zhihua
, Chen, Fangxin
in
agricultural land
/ Case studies
/ forests
/ Freshwater
/ hydrograph
/ Hydrology
/ land use
/ least squares
/ mountains
/ rain
/ Runoff
/ Sediment transport
/ sediment yield
/ sediments
/ Soil erosion
/ suspended sediment
/ Water conservation
/ Watershed management
/ Watersheds
2015
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Partial Least Squares Regression for Determining the Control Factors for Runoff and Suspended Sediment Yield during Rainfall Events
by
Wang, Yixia
, Fang, Nufang
, Shi, Zhihua
, Chen, Fangxin
in
agricultural land
/ Case studies
/ forests
/ Freshwater
/ hydrograph
/ Hydrology
/ land use
/ least squares
/ mountains
/ rain
/ Runoff
/ Sediment transport
/ sediment yield
/ sediments
/ Soil erosion
/ suspended sediment
/ Water conservation
/ Watershed management
/ Watersheds
2015
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Partial Least Squares Regression for Determining the Control Factors for Runoff and Suspended Sediment Yield during Rainfall Events
by
Wang, Yixia
, Fang, Nufang
, Shi, Zhihua
, Chen, Fangxin
in
agricultural land
/ Case studies
/ forests
/ Freshwater
/ hydrograph
/ Hydrology
/ land use
/ least squares
/ mountains
/ rain
/ Runoff
/ Sediment transport
/ sediment yield
/ sediments
/ Soil erosion
/ suspended sediment
/ Water conservation
/ Watershed management
/ Watersheds
2015
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Partial Least Squares Regression for Determining the Control Factors for Runoff and Suspended Sediment Yield during Rainfall Events
Journal Article
Partial Least Squares Regression for Determining the Control Factors for Runoff and Suspended Sediment Yield during Rainfall Events
2015
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Overview
Multivariate statistics are commonly used to identify the factors that control the dynamics of runoff or sediment yields during hydrological processes. However, one issue with the use of conventional statistical methods to address relationships between variables and runoff or sediment yield is multicollinearity. The main objectives of this study were to apply a method for effectively identifying runoff and sediment control factors during hydrological processes and apply that method to a case study. The method combines the clustering approach and partial least squares regression (PLSR) models. The case study was conducted in a mountainous watershed in the Three Gorges Area. A total of 29 flood events in three hydrological years in areas with different land uses were obtained. In total, fourteen related variables were separated from hydrographs using the classical hydrograph separation method. Twenty-nine rainfall events were classified into two rainfall regimes (heavy Rainfall Regime I and moderate Rainfall Regime II) based on rainfall characteristics and K-means clustering. Four separate PLSR models were constructed to identify the main variables that control runoff and sediment yield for the two rainfall regimes. For Rainfall Regime I, the dominant first-order factors affecting the changes in sediment yield in our study were all of the four rainfall-related variables, flood peak discharge, maximum flood suspended sediment concentration, runoff, and the percentages of forest and farmland. For Rainfall Regime II, antecedent condition-related variables have more effects on both runoff and sediment yield than in Rainfall Regime I. The results suggest that the different control factors of the two rainfall regimes are determined by the rainfall characteristics and thus different runoff mechanisms.
Publisher
MDPI AG
Subject
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