AI for Sustainable Development: Bridging Environmental Science, Engineering, and Policy

Abstract

This research looks at how artificial intelligence can help push environmental sustainability in agriculture, infrastructure, healthcare and urban development. The study typically applies AI based algorithms and techniques to understand how AI can optimize the use of resources, enhance decision making and address the global challenges in the domains of climate change, food security, and infrastructure resilience. The research then assesses the performance of four AI algorithms (Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and K Nearest Neighbors (KNN)) on sustainability related datasets. The results displays that Random Forest algorithm’s accuracy in predicting the agricultural yield is 92.5%, SVM and ANN, accuracy in forest of climate and crop monitoring was 89.2 and 90.8 respectively. Also, energy consumption forecasting in the urban environments was achieved with an accuracy of 87.6% using KNN. Also, the study reveals the challenges to AI adoption, including energy consumption and ethics. By validating the potential of AI for innovation in environmental management, policy making and resilience in infrastructure, these findings continue the discourse on the role of AI for sustainable development, adding to the wealth of such knowledge.