AI-Powered Predictive Analytics for Sustainable Urban Development: Addressing Climate Impacts of La Niña and El Niño

Abstract

The integration of Artificial Intelligence (AI) in urban development has emerged as a transformative solution to combat the challenges posed by climate change, particularly the impacts of La Niña and El Niño. These climatic events significantly affect urban areas by causing extreme weather conditions, including floods, droughts, and temperature anomalies, which endanger infrastructure, public health, and resource management. This paper explores the use of AI-powered predictive analytics to improve urban planning, forecasting, and climate resilience strategies in the face of such environmental disruptions. By leveraging machine learning algorithms, such as Random Forest, Adaboost, and Voting Classifiers, AI offers improved predictive accuracy and the ability to process large datasets from diverse sources, including satellite imagery and sensor networks. The study highlights the critical role AI plays in enhancing urban adaptability by enabling real-time monitoring, early warnings, and resource optimization. Moreover, AI helps design adaptive urban strategies, such as flood control systems and sustainable resource management, ultimately fostering resilient cities. This research underlines AI's potential to support sustainable urban development while addressing climate impacts and ensuring long-term environmental stability through innovative, data-driven approaches to climate adaptation.