Harnessing Machine Learning and AI for Sustainable Development: Applications in Supply Chains, Cybersecurity, Climate Forecasting, and Economic Impact Analysis

Abstract

The rapid growth and advancements in AI and Machine Learning have greatly influenced sustainable development across multiple domains. This research explores the transformative impact of AI and Machine Learning and their ability to provide solutions for optimizing supply chain operations, enhancing cybersecurity, improving climate forecasting, and assessing economic risks. The datasets used for this research were obtained from various sources such as databases, public datasets, and IoT data. In this Research different machine learning models were used, notably, Random Forest, Neural Networks, Logistic Regression, Support Vector Machine, XGBoost, and Linear Regression. Other metrics are employed to evaluate the performance of these models. Precision, recall, f1-score, accuracy AUC score, Mean Squared error, and R-squared are the major evaluation metrics used for this research. The different machine learning models employed in this research perform differently for different domains due to the disparity in dataset properties.