Multivariate polynomial regression modeling of total dissolved-solids in rangeland stormwater runoff in the Colorado River Basin. (November 2022)

• Record Type: Journal Article
• Authors: Kim, Sojung; Kim, Sumin; Green, Colleen H.M.; Jeong, Jaehak
• Is Part Of: Environmental modelling & software. Volume 157(2022)
• Publication Date: 2022-11
• Subjects: Total dissolved solids -- Machine learning -- Artificial intelligence -- Polynomial regression -- Factor analysis -- Colorado river basin; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105523
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: A parameter estimation model for TDS is developed based on experimental data collected in the upper Colorado River basin. An MPR technique is devised with Factor analysis and Tabu search for identification of the optimum parameters. Significant factors in estimating TDS include EC-water, TSS, and other soil and stormwater properties.