In the face of escalating climate change, the specter of floods looms large, posing significant threats to infrastructure, economies, and lives. For the energy sector, which relies heavily on stable and secure infrastructure, the stakes are particularly high. Enter Seyed Vahid Razavi-Termeh, a researcher from the Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea. Razavi-Termeh and his team have developed a groundbreaking approach to flood susceptibility mapping, leveraging the power of artificial intelligence (AI) to predict and identify flood-prone areas with unprecedented accuracy.
The research, published in the International Journal of Applied Earth Observations and Geoinformation, focuses on enhancing the Categorical Boosting (CatBoost) algorithm using three metaheuristic methods: Augmented Artificial Ecosystem Optimization (AAEO), Germinal Center Optimization (GCO), and Water Circle Algorithm (WCA). The study, conducted in Jahrom County, Iran, utilized 13 flood conditioning geophysical factors as input parameters and flood occurrence data derived from satellite imagery as the output.
Razavi-Termeh explains, “Our goal was to bridge the gap in creating accurate flood susceptibility maps using AI. By integrating metaheuristic methods with CatBoost, we aimed to produce more efficient and reliable models.”
The results were striking. The CatBoost-AAEO model outperformed other combined models, including CatBoost-WCA, CatBoost-GCO, and the basic CatBoost model. This breakthrough could revolutionize how we approach flood management, particularly in the energy sector. Accurate flood susceptibility maps can help energy companies identify and mitigate risks, protect critical infrastructure, and avoid costly disruptions.
The study also highlighted key factors influencing flood occurrence, such as low slope, low elevation, minimal vegetation cover, flat curvature, and proximity to rivers. These insights, visualized through Partial Dependence Plots (PDP), offer a deeper understanding of the environmental dynamics at play.
Razavi-Termeh elaborates, “By understanding these factors, we can better inform planners and decision-makers, helping them manage and prevent floods more effectively. This not only saves lives but also reduces financial losses caused by floods.”
The implications of this research are vast. For the energy sector, which often operates in flood-prone areas, this technology could be a game-changer. By predicting flood risks with greater accuracy, energy companies can better protect their assets, ensure continuous operations, and minimize downtime. This could lead to more resilient energy infrastructure, reduced maintenance costs, and enhanced service reliability.
As we look to the future, the integration of AI and metaheuristic algorithms in flood susceptibility mapping could shape how we approach disaster management. This research paves the way for more sophisticated models that can adapt to changing environmental conditions, providing real-time insights and actionable intelligence. The energy sector, along with other critical industries, stands to benefit significantly from these advancements, ensuring a more resilient and sustainable future.
