Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

2014 ◽  
Vol 28 (7) ◽  
pp. 2005-2019 ◽  
Author(s):  
J Sreekanth ◽  
Bithin Datta
Keyword(s):  
Coastal Aquifers ◽  
Parameter Uncertainty ◽  
Optimal Management ◽  
Multi Objective

scholarly journals Trained meta-models and evolutionary algorithm based multi-objective management of coastal aquifers under parameter uncertainty

2018 ◽  
Vol 20 (6) ◽  
pp. 1247-1267 ◽  
Author(s):  
Dilip Kumar Roy ◽  
Bithin Datta
Keyword(s):  
Coastal Aquifers ◽  
Parameter Uncertainty ◽  
Saltwater Intrusion ◽  
Management Strategies ◽  
Groundwater Extraction ◽  
Aquifer Recharge ◽  
Meta Model ◽  
Aquifer System ◽  
Multi Objective ◽  
Input Parameters

Abstract Meta-model based coupled simulation-optimization methodology is an effective tool in developing sustainable saltwater intrusion management strategies for coastal aquifers. Such management strategies largely depend on the accuracy, reliability, and computational feasibility of meta-models and the numerical simulation model. However, groundwater models are associated with a certain amount of uncertainties, e.g. parameter uncertainty and uncertainty in prediction. This study addresses uncertainties related to input parameters of the groundwater flow and transport system by using a set of randomized input parameters. Three meta-models are compared to characterize responses of water quality in coastal aquifers due to groundwater extraction patterns under parameter uncertainty. The ensemble of the best meta-model is then coupled with a multi-objective optimization algorithm to develop a saltwater intrusion management model. Uncertainties in hydraulic conductivity, compressibility, bulk density, and aquifer recharge are incorporated in the proposed approach. These uncertainties in the physical system are captured by the meta-models whereas the prediction uncertainties of meta-models are further addressed by the ensemble approach. An illustrative multi-layered coastal aquifer system is used to demonstrate the feasibility of the proposed approach. Evaluation results indicate the capability of the proposed approach to develop accurate and reliable management strategies for groundwater extraction to control saltwater intrusion.

Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

2009 ◽  
Author(s):  
M. Li ◽  
N. Williams ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Uncertainty Reduction ◽  
Interval Uncertainty ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Limited Budget ◽  
Output Entropy ◽  
Global Uncertainty ◽  
Significant Attention

Sensitivity analysis has received significant attention in engineering design. While sensitivity analysis methods can be global, taking into account all variations, or local, taking into account small variations, they generally identify which uncertain parameters are most important and to what extent their effect might be on design performance. The extant methods do not, in general, tackle the question of which ranges of parameter uncertainty are most important or how to best allocate investments to partial uncertainty reduction in parameters under a limited budget. More specifically, no previous approach has been reported that can handle single-disciplinary multi-output global sensitivity analysis for both a single design and multiple designs under interval uncertainty. Two new global uncertainty metrics, i.e., radius of output sensitivity region and multi-output entropy performance, are presented. With these metrics, a multi-objective optimization model is developed and solved to obtain fractional levels of parameter uncertainty reduction that provide the greatest payoff in system performance for the least amount of “investment”. Two case studies of varying difficulty are presented to demonstrate the applicability of the proposed approach.

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