Global sensitivity analysis for large-scale socio-hydrological models using Hadoop. (November 2015)

• Record Type: Journal Article
• Authors: Hu, Yao; Garcia-Cabrejo, Oscar; Cai, Ximing; Valocchi, Albert J.; DuPont, Benjamin
• Is Part Of: Environmental modelling & software. Volume 73(2015:Nov.)
• Page Start: 231
• Page End: 243
• Publication Date: 2015-11
• Subjects: Multi-agent system -- Socio-hydrological model -- Global Sensitivity Analysis -- Hadoop -- Polynomial Chaos Expansion; 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.2015.08.015
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A multi-agent system (MAS) model is coupled with a physically-based groundwater model to understand the declining water table in the heavily irrigated Republican River basin. Each agent in the MAS model is associated with five behavioral parameters, and we estimate their influences on the coupled models using Global Sensitivity Analysis (GSA). This paper utilizes Hadoop-based Cloud Computing techniques and Polynomial Chaos Expansion (PCE) based variance decomposition approach for the improvement of GSA with large-scale socio-hydrological models. With the techniques, running 1000 scenarios of the coupled models can be completed within two hours with Hadoop clusters, a substantial improvement over the 42 days required to run these scenarios sequentially on a desktop machine. Based on the model results, GSA is conducted with the surrogate model derived from using PCE to measure the impacts of the spatio-temporal variations of the behavioral parameters on crop profits and the water table, identifying influential parameters. Highlights: Develop a framework to combine Hadoop-based cloud computing with PCE for GSA. Reduce the computation time of 1000 simulations from 42 days to two hours. Identify influential behavioral parameters via spatio-temporal sensitivity analysis.