TY - JOUR
T1 - Multivariate polynomial regression modeling of total dissolved-solids in rangeland stormwater runoff in the Colorado River Basin
AU - Kim, Sojung
AU - Kim, Sumin
AU - Green, Colleen H.M.
AU - Jeong, Jaehak
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11
Y1 - 2022/11
N2 - A multivariate polynomial regression modeling (MPR) framework is developed to estimate total dissolved solids (TDS) in stormwater runoffs from rangelands in the Colorado River Basin in the Southwestern United States. An accurate TDS estimation model is needed to simulate terrestrial and aquatic salt transport processes on rangelands, identify critical source areas, and manage these sources effectively. However, modeling stormwater TDS runoff on rangeland sodic soils is challenging due to its complex correlation with variables in many aspects, such as topography, climate, soil, and vegetation. We propose a two-stage MPR framework based on field data collected from multiple rainfall simulator experiments: (1) variable selection with factor analysis and (2) TDS modeling via MPR, considering the nonlinear relationships between variables. Tabu search (TS) is used to optimize the TDS model in MPR. The proposed framework achieved a high prediction accuracy of 74.7% in estimating the TDS runoff transport.
AB - A multivariate polynomial regression modeling (MPR) framework is developed to estimate total dissolved solids (TDS) in stormwater runoffs from rangelands in the Colorado River Basin in the Southwestern United States. An accurate TDS estimation model is needed to simulate terrestrial and aquatic salt transport processes on rangelands, identify critical source areas, and manage these sources effectively. However, modeling stormwater TDS runoff on rangeland sodic soils is challenging due to its complex correlation with variables in many aspects, such as topography, climate, soil, and vegetation. We propose a two-stage MPR framework based on field data collected from multiple rainfall simulator experiments: (1) variable selection with factor analysis and (2) TDS modeling via MPR, considering the nonlinear relationships between variables. Tabu search (TS) is used to optimize the TDS model in MPR. The proposed framework achieved a high prediction accuracy of 74.7% in estimating the TDS runoff transport.
KW - Artificial intelligence
KW - Colorado river basin
KW - Factor analysis
KW - Machine learning
KW - Polynomial regression
KW - Total dissolved solids
UR - http://www.scopus.com/inward/record.url?scp=85138555006&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2022.105523
DO - 10.1016/j.envsoft.2022.105523
M3 - Article
AN - SCOPUS:85138555006
SN - 1364-8152
VL - 157
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 105523
ER -