|
|
 |
| |
|
|
|
Quantifying the Impact of Sea Level on Coastal Cities using Bayesian optimized Monte Carlo Simulation model |
|
|
|
PP: 641-651 |
|
|
|
doi:10.18576/amis/180316
|
|
|
|
Author(s) |
|
|
|
Awatif M. A. Elsiddieg,
|
|
|
|
Abstract |
|
|
| The immediate treatment of the increasing of sea levels represent on coastal cities around the globe is the subject of this
research. Using an all-encompassing and probabilistic methodology, the study quantifies the complex effects of rising sea levels using a
Bayesian-optimized Monte Carlo simulation framework. The current approaches for assessing the effects of rising sea levels frequently
do not adequately capture the intrinsic complexity and unpredictability of changing structures. This work presents a novel method
combining Monte Carlo simulation and Bayesian optimization to overcome these drawbacks. The present research is innovative since
it incorporates Bayesian optimization methods into a Monte Carlo simulation framework. By constantly modifying distributions of
probabilities according to observable results, this combination improves simulation performance. This constantly changing optimization
methodology closes a significant gap within current methods by ensuring a more accurate portrayal of shifting conditions. In addition,
the method used in this work emphasizes cooperation among mathematicians, coastal scientists, and climate researchers, thereby
promoting a comprehensive knowledge of the complex issues raised by rising sea levels. This set of variables includes the current
level of the sea, land height, frequency of storm surges, population density, and infrastructure resilience. The model’s effectiveness is
rigorously assessed, utilizing procedures for verification and calibration that use past information. Calculations are guided by various
rising sea-level situations based on scientific forecasts and uncertainty ranges. Sensitivity analysis pinpoints important factors, guiding
further efforts to gather data and improve the model. The structure for the performance assessment ensures that the model is dependable
and applicable to those making decisions who want to prioritize flexible measures and create resilient communities along the coast. |
|
|
|
|
|
|
|
