Sensitivity analysis of a Monte Carlo atmospheric diffusion model

1988 ◽  
Vol 11 (1) ◽  
pp. 13-28 ◽  
Author(s):  
D. Anfossi ◽  
G. Brusasca ◽  
G. Tinarelli
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Diffusion Model ◽  
Atmospheric Diffusion

scholarly journals Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3871
Author(s):  
Jérôme Morio ◽  
Baptiste Levasseur ◽  
Sylvain Bertrand
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Loss Of Control ◽  
Minimum Volume ◽  
Multiple Importance Sampling ◽  
Impact Position ◽  
Engine Failure ◽  
Main Engine ◽  
Ground Impact

This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.

Disentangling uncertainties in risk-based planning of water resources in the UK

2021 ◽  
Author(s):  
Francesca Pianosi ◽  
Andres Penuela-Fernandez ◽  
Christopher Hutton
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Water Resources ◽  
Water Resources Planning ◽  
Adaptive Planning ◽  
Water Companies ◽  
The Uk ◽  
Resources Planning

<p>Proper consideration of uncertainty has become a cornerstone of model-informed planning of water resource systems. In the UK Government’s 2020 Water Resources Planning Guidelines, the word “uncertainty” appears 48 times in 82 pages. This emphasis on uncertainty aligns with the increasing adoption by UK water companies of a “risk-based” approach to their long-term decision-making, in order to handle uncertainties in supply-demand estimation, climate change, population growth, etc. The term “risk-based” covers a range of methods - such as “info-gap”, “robust decision-making” or “system sensitivity analysis” - that come under different names but largely share a common rationale, essentially based on the use of Monte Carlo simulation. This shift in thinking from previous (deterministic) “worst-case” approach to a “risk-based” one is important and has the potential to significantly improve water resources planning practice. However its implementation is diminished by a certain lack of clarity about the terminology in use and about the concrete differences (and similarities) among methods. On top of these difficulties, in the next planning-cycle (2021-2026) two further step changes are introduced: (1) water companies are requested to move from a cost-efficiency approach focused on achieving the supply-demand balance, towards a fully multi-criteria approach that more explicitly encompasses other objectives including environmental sustainability; (2) as a further way to handle long-term uncertainties, they are required to embrace an “adaptive planning” approach. These changes will introduce two new sets of uncertainties around the robust quantification of criteria, particularly environmental ones, and around the attribution of weights to different criteria. This urgently calls for establishing structured approaches to quantify not only the uncertainty in model outputs, but also the sensitivity of those outputs to different forms of uncertainty in the modelling chain that mostly control the variability of the final outcome – the “best value” plan. Without this understanding of critical uncertainties, the risk is that huge efforts are invested on characterising and/or reducing uncertainties that later turn out to have little impact on the final outcome; or that water managers fall back to using oversimplified representation of those uncertainties as a way to escape the huge modelling burden. In this work, we aim at starting to establish a common rationale to “risk-based” methods within the context of a fully multi-criteria approach. We use a proof-of-concept example of a reservoir system in the South-West of England to demonstrate the use of global (i.e. Monte Carlo based) sensitivity analysis to simultaneously quantify output uncertainty and sensitivity, and identify robust decisions. We also discuss the potential of this approach to inform the construction of a “decision tree” for adaptive planning.</p>

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