A Multi-Objective AI Framework for Robust and Scalable Urban Flood Early Warning Under Climate Uncertainty

Abstract

The problem of urban flooding has been increasing with the climate variability, rapid urbanization process, and the increased uncertainty in the rainfall patterns. Conventional flood prediction models are usually based on single-objective optimization, which restricts their capacity to optimise competing needs which include accuracy, robustness, and computational efficiency. The proposed paper presents a multi-objective framework of Artificial Intelligence (AI) flood early warning in an urban environment that will also provide optimal prediction accuracy, model resilience to climate uncertainty, and model scalability to real-time application. The model combines the Convolutional Neural Networks (CNNs) to extract spatial features using satellite data and the Long Short-Term Memory (LSTM) networks to model rainfall and sensor data over time. Multi-objective optimization approach is used to have an optimum tradeoff in performance measures in different environmental conditions. Moreover, the uncertainty-aware learning mechanisms are included to increase the resilience of the model to the unpredictable climate scenario. The outcomes of the experiments prove that the given framework offers a better reliability of the prediction and stability in contrast to traditional models. The system should facilitate real-time decisions, and therefore it can be implemented in smart cities flood management systems.