Razi Sheikholeslami

Progressive Latin Hypercube Sampling

Efficient sampling strategies that scale with the size of the problem, computational budget, and users needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses. A new sequential sampling strategy called PLHS is proposed for sampling-based analysis of simulation models.PLHS is evaluated across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis.PLHS provides better performance compared with the other sampling strategies in terms of convergence rate and robustness.PLHS can be used to monitor the performance of the associated sampling-based analysis and to avoid over- or under-sampling.

Optimum Cost Design of Reinforced Concrete Retaining Walls Using Hybrid Firefly Algorithm

This paper develops a novel optimization method namely hybrid firefly algorithm with harmony search technique (IFA-HS), to obtain the optimal cost of the reinforced concrete retaining walls satisfying the stability criteria. The hybrid IFA-HS is utilized to find the economical design adhering to provisions of ACI 318-05. Also Coulomb lateral earth pressure theory is used to derive the lateral total thrust on the wall. Some design examples are tested using the new method. The results carried out on these examples confirm the validity of the proposed algorithm. The IFA-HS method can be considered as an improvement of the recently developed firefly algorithm. The improvements include the utilizing of a memory that contains some information extracted online during the search, adding of pitch adjustment operation in harmony search serving as mutation operator during the process of the firefly updating, and modifying the movement phase of firefly algorithm. The detailed implementation procedure for this improved meta-heuristic method is also described.

Multiple factors that shaped sustainability science journal: a 10-year review

Sustainability Science Journal celebrated its 10th anniversary in 2016, and we see this milestone as an opportunity to reflect on its decade of sustainability science research. All the published articles from 2006 to 2015 were reviewed in this study using qualitative and quantitative methods to (a) assess scope, diversity, and representativeness in the publications, (b) analyse the trends and dominance in the content, and (c) evaluate cross-disciplinary collaboration and knowledge transfer along with the practice of transdisciplinarity in sustainability science research. Our assessment shows that the journal has transformed from publishing more uniform contributions into a more diverse international journal demonstrating greater breadth in the range of contributing authors, case studies, and field of studies. We observed a progressive transition in the content of the journal from the domination of natural sciences contributions to cross-disciplinary and sustainability science research. A growing collaboration amongst authors from different disciplines also suggests that researchers from an array of backgrounds are increasingly working together, combining knowledge and advancing sustainability science. Although researchers still base their work largely on their own disciplinary knowledge, there is an increasing trend to cite cross-disciplinary research with the aim of addressing complex sustainability problems.

Improved Understanding of River Ice Processes Using Global Sensitivity Analysis Approaches

AbstractThe high impact of river ice phenomena on the hydrology of cold regions has led to the extensive use of numerical models in simulating and predicting river ice processes. Consequently, ther...

VARS-TOOL: A Toolbox for Comprehensive, Efficient, and Robust Sensitivity and Uncertainty Analysis

Abstract VARS-TOOL is a software toolbox for sensitivity and uncertainty analysis. Developed primarily around the “Variogram Analysis of Response Surfaces” framework, VARS-TOOL adopts a multi-method approach that enables simultaneous generation of a range of sensitivity indices, including ones based on derivative, variance, and variogram concepts, from a single sample. Other special features of VARS-TOOL include (1) novel tools for time-varying and time-aggregate sensitivity analysis of dynamical systems models, (2) highly efficient sampling techniques, such as Progressive Latin Hypercube Sampling (PLHS), that maximize robustness and rapid convergence to stable sensitivity estimates, (3) factor grouping for dealing with high-dimensional problems, (4) visualization for monitoring stability and convergence, (5) model emulation for handling model crashes, and (6) an interface that allows working with any model in any programming language and operating system. As a test bed for training and research, VARS-TOOL provides a set of mathematical test functions and the (dynamical) HBV-SASK hydrologic model.

Global Sensitivity Analysis for High-Dimensional Problems: How tObjectively Group Factors and Measure Robustness and Convergence while Reducing Computational Cost

Abstract Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.