رسائل ماجيستير
English
2023
Deep Learning-based Multi-objective Resource Allocation For Interdependent Infrastructure Network Resilience
Yasser Almoghathawi , قصي محمد كرار محمد
كلية علوم وهندسة الحاسب الآلي-جامعة الملك فهد للبترول والمعادن · السعودية
In contrast to traditional regression algorithms, we harness the power of advancedmachine learning techniques, including Linear Regression, Polynomial Regression, Decision Tree Regressor, K-Nearest Neighbors Regressor, Gradient Boosting Regressor,Radius Neighbors Regressor, Bayesian Ridge Regressor, Random Forest Regressor,and Elastic Net Regression. However, what sets this research apart is the integrationof Deep Learning and Graph Neural Networks architecture. These sophisticated models undergo rigorous training and validation, showcasing their remarkable capabilitiesin predicting node significance while simultaneously minimizing restoration costs andmaximizing infrastructure network resilience.To assess model performance, we employ a comprehensive suite of evaluation metrics such as Mean Squared Error (MSE), R-squared, Root Mean Squared Error(RMSE), and Mean Absolute Error (MAE). Notably, the Decision Tree Regressor,KNN Regressor, and Random Forest Regressor consistently outshine their counterparts, delivering the lowest MSE and RMSE scores.This research holds significant practical implications, as it unveils the potentialof our models for real-time node assessment, optimal resource allocation, risk mitigation, and emergency response planning within the complex fabric of InterdependentInfrastructure Networks. Our interdisciplinary approach underscores the importanceof collaboration between infrastructure experts and data scientists, emphasizing ethical considerations and model refinement for responsible predictive modeling in criticalinfrastructure management.This study represents a pioneering effort in advancing node significance prediction within Interdependent Infrastructure Networks. It provides indispensable toolsfor infrastructure operators and resilience planners, highlighting the pivotal role ofdata-driven decision support in safeguarding the functionality of Interdependent Infrastructure Networks and ensuring the well-being of modern society. By seamlesslyintegrating Machine Learning, Deep Learning, and Graph Neural Networks, this research pushes the boundaries of predictive modeling, paving the way for resilient andreliable infrastructure networks in an ever-changing world. Interdependent Infrastructure Networks are the backbone of modern societies, vital for the delivery of essentialservices. This research centers on predicting the significance of nodes within thesenetworks, which are crucial components of contemporary civilization. To enhance infrastructure resilience and reliability, this study leverages a combination of machinelearning models, deep learning models, and graph neural networks in a novel approach.Our methodology explores developing and evaluating predictive models, primarily focusing on enhancing multi-objective resource allocation.In contrast to traditional regression algorithms, we harness the power of advancedmachine learning techniques, including Linear Regression, Polynomial Regression, Decision Tree Regressor, K-Nearest Neighbors Regressor, Gradient Boosting Regressor,Radius Neighbors Regressor, Bayesian Ridge Regressor, Random Forest Regressor,and Elastic Net Regression. However, what sets this research apart is the integrationof Deep Learning and Graph Neural Networks architecture. These sophisticated models undergo rigorous training and validation, showcasing their remarkable capabilitiesin predicting node significance while simultaneously minimizing restoration costs andmaximizing infrastructure network resilience.To assess model performance, we employ a comprehensive suite of evaluationmetrics such as Mean Squared Error (MSE), R-squared, Root Mean Squared Error(RMSE), and Mean Absolute Error (MAE). Notably, the Decision Tree Regressor,KNN Regressor, and Random Forest Regressor consistently outshine their counterparts, delivering the lowest MSE and RMSE scores.This research holds significant practical implications, as it unveils the potential ofour models for real-time node assessment, optimal resource allocation, risk mitigation, and emergency response planning within the complex fabric of InterdependentInfrastructure Networks. Our interdisciplinary approach underscores the importanceof collaboration between infrastructure experts and data scientists, emphasizing ethical considerations and model refinement for responsible predictive modeling in criticalxviiiinfrastructure management.This study represents a pioneering effort in advancing node significance prediction within Interdependent Infrastructure Networks. It provides indispensable toolsfor infrastructure operators and resilience planners, highlighting the pivotal role ofdata-driven decision support in safeguarding the functionality of Interdependent Infrastructure Networks and ensuring the well-being of modern society. By seamlesslyintegrating Machine Learning, Deep Learning, and Graph Neural Networks, this research pushes the boundaries of predictive modeling, paving the way for resilient andreliable infrastructure networks in an ever-changing world.
