How statistical modeling and machine learning could help in the calibration of numerical simulation and fluid mechanics models? Application to the calibration of models reproducing the vibratory behavior of an overhead line conductor

Amroun, Hamdi; Hafid, Fikri; Ammi, Mehdi

doi:10.1016/j.array.2022.100187

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML models have improved the existing models when combined with statistical ones, e.g., Alzheimer's disease (Tan et al 2021). They have also optimized model calibration (Amroun et al 2022). Although the core of these techniques uses mathematical models; the eld of the search is signicantly expanded by the acceleration and automation provided by computers.…”

Section: Machine Learning and Resiliencementioning

confidence: 99%

Prediction models and testing of resilience in regions

Manousiadis

Gaki

2023

REGION

View full text Add to dashboard Cite

A significant amount of research has been conducted regarding the resilience of the regions and the factors that contribute to allow them to face challenges, crises, or disasters. The rise of promising sectors like Machine learning (ML) and Artificial Intelligence (AI) can enhance this research using computing power in regional economic, social, and environmental data analysis to find patterns and create prediction models. Through Machine Learning, the following research introduces the use of models that can predict the performance of a region in disasters. A case study of the performance of USA Counties during the Covid19 first wave period of the pandemic and the related restrictions that were applied by the authorities was used in order to reveal the obvious or hidden parameters and factors that affected their resilience, in particular their economic response, and other interesting patterns between all the involved attributes. This paper aims to contribute to a methodology and to offer useful guidelines in how regional factors can be translated and processed by data and ML/AI tools and techniques. The proposed models were evaluated on their ability to predict the economic performance of each county and in particular the difference of its unemployment rate between March and June of 2020. The former is based on several economic, social, and environmental data -up to that point in time- using classifiers like neural networks and decision trees. A comparison of the different models' execution was performed, and the best models were further analyzed and presented. Further execution results that identified patterns and connections between regional data and attributes are also presented. The main results of this research are i) a methodological framework of how regional status can be translated into digital models and ii) related examples of predictive models in a real case. An effort was also made to decode the results in terms of regional science to produce useful and meaningful conclusions, thus a decision tree is also presented to demonstrate how these models can be interpreted. Finally, the connection between this work and the strong current trend of regional and urban digitalization towards sustainability is established.

show abstract

Section: Machine Learning and Resiliencementioning

confidence: 99%

Prediction models and testing of resilience in regions

Manousiadis

Gaki

2023

REGION

View full text Add to dashboard Cite

show abstract

Pathways to identify and reduce uncertainties in agricultural climate impact assessments

Wang,

Jägermeyr,

O’Leary

et al. 2024

Nat Food

View full text Add to dashboard Cite

Numerical Modeling of Landslide Susceptibility: A Case Study for Uma Oya Catchment, Sri Lanka

ABEYGUNASEKARA,

KAZAMA,

SAMARASURIYA

2023

Journal of JSCE

View full text Add to dashboard Cite

Landslide susceptibility being a severe threat abundant worldwide, they tend to occur in a variety of geological and environmental settings, while imposing devastating consequences for both human life and infrastructure. Characterized by a unique combination of geological and climatic factors that are conducive to slope instability, the central highlands of Sri Lanka, are more frequently exposed to the risk of slope failures compared to other regions in the country. Provided the necessity of comprehensive knowledge and understanding of the related hydro-geological phenomena underlying the specific geological setting, developing a deterministic landslide model finds a high level of applicability and usability in disaster risk mitigation strategies, which sets the objective of this research. The study enabled the development of an integrated numerical model within the geographical information system environment, to simulate the regional hydro-geological processes to predict the groundwater levels and the associated measures of slope stability to map the distribution of landslide susceptibility within the region. Backed by a recovery rate of 79% in predicted water table elevations, the model's predictions on slope stability were in agreement with a known past landslide occurrence within the period of simulation. The developed model with enhanced features of adaptability, being the key outcome, this research is aimed at setting an initiative that with the potential to be developed into an advanced modeling environment for landslide susceptibility evaluations, enabling effective decision-making in the future.

show abstract

How statistical modeling and machine learning could help in the calibration of numerical simulation and fluid mechanics models? Application to the calibration of models reproducing the vibratory behavior of an overhead line conductor

Cited by 3 publications

References 26 publications

Prediction models and testing of resilience in regions

Prediction models and testing of resilience in regions

Pathways to identify and reduce uncertainties in agricultural climate impact assessments

Numerical Modeling of Landslide Susceptibility: A Case Study for Uma Oya Catchment, Sri Lanka

Contact Info

Product

Resources

About