Flood Prediction: A Comparative Study of Machine Learning Algorithms

Abstract

Among the most devastating natural disasters are floods, which lead to a significant loss of life, extensive damage to properties, and severe disruptions to the economy. The achievement of effective readiness and reduction of disaster impacts hinges on precise flood forecasting. This investigation presents a comprehensive theoretical evaluation of various machine learning methodologies, including Gradient Boosting Machines (GBM), Support Vector Machines (SVM), Random Forest, Deep Learning models, and Clustering techniques, in the context of flood prediction. The analysis delves into the theoretical underpinnings, practical applications, as well as strengths and limitations of each approach. A comparison of different strategies is conducted utilizing fundamental classification measures like accuracy, precision, recall, and F1 score. The findings reveal that, despite the considerable theoretical promise of multiple models, Support Vector Machines (SVM) emerge as the most precise and resilient technique for flood prediction, demonstrating superior performance across all essential metrics. While clustering algorithms are not commonly employed for direct prediction, they provide valuable insights for evaluating regional vulnerabilities. This theoretical exploration underscores the capacity of machine learning to enhance the accuracy and reliability of flood forecasting, setting the stage for forthcoming empirical validation and real-world implementation. To advance flood prediction capabilities, future research should focus on amalgamating data from diverse origins, improving temporal and spatial precision, and developing hybrid forecasting models.