TY - JOUR
T1 - Global sensitivity analysis for large-scale socio-hydrological models using Hadoop
AU - Hu, Yao
AU - Garcia-Cabrejo, Oscar
AU - Cai, Ximing
AU - Valocchi, Albert J.
AU - DuPont, Benjamin
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - 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.
AB - 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.
KW - Global Sensitivity Analysis
KW - Hadoop
KW - Multi-agent system
KW - Polynomial Chaos Expansion
KW - Socio-hydrological model
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U2 - 10.1016/j.envsoft.2015.08.015
DO - 10.1016/j.envsoft.2015.08.015
M3 - Article
AN - SCOPUS:84940778157
SN - 1364-8152
VL - 73
SP - 231
EP - 243
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
ER -