Design of an Intelligent Urban Flood Prediction and Management Model Based on Satellite Meteorological Data and Machine Learning Algorithms with an Infrastructure Damage Reduction Approach in Metropolitan Areas

Abstract

The increasing frequency and intensity of extreme precipitation events in recent decades, combined with unbalanced urban expansion, have transformed urban flooding into one of the most critical infrastructural hazards in metropolitan areas. The inefficiency of conventional flood forecasting systems, limitations in processing large-scale meteorological data, and weaknesses in dynamic urban runoff analysis have highlighted the necessity of intelligent models based on machine learning and remote sensing technologies. The present study aims to design an intelligent urban flood prediction and management model using satellite meteorological data, hydrological variables, land-use information, and machine learning algorithms with an emphasis on reducing infrastructure damage in metropolitan regions. In this study, satellite-based precipitation data, land surface temperature, soil moisture, terrain slope, urban construction density, drainage network characteristics, and historical flood occurrence records were analyzed. Following the preprocessing stage, modeling procedures were implemented using Random Forest, Support Vector Machine, Deep Neural Network, and Gradient Boosting algorithms. Model performance was evaluated based on accuracy, sensitivity, correlation coefficient, and mean squared error indicators. Furthermore, Geographic Information System techniques and multi-source remote sensing datasets were integrated to identify flood-prone urban zones and analyze spatial flood vulnerability patterns. The findings demonstrated that the hybrid deep learning model integrated with satellite meteorological data achieved high capability in identifying inundation-prone regions and forecasting short-term flood intensity. In addition, the integration of real-time meteorological observations with machine learning algorithms significantly improved forecasting accuracy and reduced spatial prediction errors in urban crisis management. The results indicate that intelligent flood prediction systems can substantially contribute to reducing infrastructure damage, enhancing urban resilience, improving managerial decision-making, and strengthening metropolitan flood risk management strategies.