Digital Twin-Assisted Fuzzy Logic-Inspired Intelligent Approach for Flood Prediction

Natural hazards causing catastrophic damage and infrastructure destruction have increased in recent decades, with floods being a serious problem that leads to crop damage, population loss, infrastructure degradation, and public service collapse. Digital twin (DT) technology is a promising solution for alerting communities of oncoming floods and providing sufficient time for evacuation and property protection. This research introduces a DT-inspired intelligent framework that analyzes hydrological and meteorological parameters causing floods, validated using data from the Indian Meteorological Department (IMD). Artificial intelligence (AI) algorithms improve situational analysis and decision-making for flood forecasting, while advanced blockchain security features keep recorded and analyzed data secure. A case study demonstrates the proposed approach’s efficacy in smart catastrophe management with the best training and testing accuracy of 97.23% and 95.58%, respectively.