Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand
Piyawat Wuttichaikitcharoen | Mukand Singh Babel
Predicting sediment yield is necessary for good land and water management in any river basin. However, sometimes, the sediment data is either not available or is sparse, which renders estimating sediment yield a daunting task. The present study investigates the factors influencing suspended sediment yield using the principal component analysis (PCA). Additionally, the regression relationships for estimating suspended sediment yield, based on the selected key factors from the PCA, are developed. The PCA shows six components of key factors that can explain at least up to 86.7% of the variation of all variables. The regression models show that basin size, channel network characteristics, land use, basin steepness and rainfall distribution are the key factors affecting sediment yield. The validation of regression relationships for estimating suspended sediment yield shows the error of estimation ranging from −55% to +315% and −59% to +259% for suspended sediment yield and for area-specific suspended sediment yield, respectively. The proposed relationships may be considered useful for predicting suspended sediment yield in ungauged basins of Northern Thailand that have geologic, climatic and hydrologic conditions similar to the study area.
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