Novel data-driven model for flood prediction using big data: Social resilience and risk management

Among the most destructive natural catastrophes, floods can cause significant disruptions that have long-term effects on the environment, society, and economy. Reducing hazards and improving readiness require efficient flood prediction models. To predict flood events, traditional flood forecasting methods mostly use hydrological and meteorological data. However, these approaches, frequently overlook the larger socioeconomic elements that affect community readiness and flood resistance. To overcome this limitation, a new data-driven method has been presented that combines big data analytics with social resilience and risk management frameworks. This model integrates important socioeconomic variables with large datasets to improve flood prediction accuracy, and the data is subjected to a rigorous preprocessing step that includes cleaning and Z score normalization to ensure the quality, consistency, and reliability of input variables. Principal Component Analysis (PCA), a statistical method that preserves the most significant variation while reducing the dimensionality of data, is used to extract features. PCA improves model performance and computing efficiency by streamlining the dataset without sacrificing important information. The Bison Optimized Efficient Deep Neural Network (BO-EDNN) is used in the research to anticipate floods. In contrast to traditional deep learning models, BO-EDNN dynamically adjusts to intricate flood patterns, enabling more accurate predictions. Conventional flood prediction models ignore the socioeconomic factors that influence disaster response and recovery in favor of concentrating only on physical dangers. According to empirical findings, the BO-EDNN model performs noticeably better than conventional techniques. 95.2% prediction accuracy, 96.1% precision, 92.6% recall, and 93.2% F1 score are attained. The robustness and dependability of the model are demonstrated by these excellent metrics. The incorporation of social resilience components, such as infrastructure vulnerability, adaptive capability, and community readiness, is a significant innovation of this approach. Conventional flood prediction models ignore the socioeconomic factors that influence disaster response and recovery in favor of concentrating only on physical dangers. The robustness and dependability of the model are demonstrated by these excellent metrics.