AI-Driven Flood Risk Assessment in Riverine Areas of Pakistan

Abstract

One of the most destructive natural disasters in Pakistan is flooding especially in riverine countries found in the Indus Basin. The occurrence and intensity of floods have increased over the past decades due to the increase in climate variability, unplanned urbanization, deforestation, and poor drainage systems. Pakistan has more or less applied the traditional flood risk assessment methods that have been based on hydrological modeling, past trends analysis which do not integrate real-time data and predictive intelligence. This paper examines how Artificial Intelligence (AI)-based methods can be used to assess flood risks in riverine Pakistan by analyzing machine learning (ML), deep learning (DL), and geospatial analytics. This study combines satellite geography, weather parameters, topography and past history of floods to come up with predictive models of flood prone areas. Algorithms suggested by AI, such as Random Forest, Support Vector Machines or Artificial Neural Networks, are discussed in terms of their potential to improve the early warning system and spatial risk mapping. The results show that AI-based models enhance prediction accuracy and uncertainty reduction and mapping dynamic flood risks is better than the standard statistical models. The paper highlights the role of implementation of AI frameworks alongside national disaster management systems like the National Disaster Management Authority as a way of reinforcing preparedness and response plans. The study has a contribution to the sustainable disaster risk mitigation by suggesting a data-based framework that is specific to the riverine geography of Pakistan, especially in the Indus River system.