Artificial Intelligence and Machine Learning for Prediction of Extreme Floods in Pakistan

Abstract

This paper discusses the application of artificial intelligence (AI) and machine learning (ML) techniques in predicting extreme floods in Pakistan, a country frequently affected by monsoon-related flooding. The study aims to develop predictive models that utilize historical hydrological and meteorological data to improve flood forecasting and mitigation. By integrating AI/ML models, such as regression, classification, and anomaly detection, with datasets on rainfall, river inflows, topography, and climate factors, this research seeks to enhance early warning systems and flood management practices. Various algorithms, including Random Forest, Gradient Boosting, and Neural Networks, are evaluated based on prediction accuracy and the models’ ability to detect anomalies that precede extreme flood events. These methods, coupled with real-time data from weather stations and satellite imagery, offer a robust approach to forecasting and mitigating the impact of floods in Pakistan. The paper also highlights the practical implications of these AI/ML techniques for disaster management and water resource planning, with the aim of reducing the socioeconomic impact of floods.