Artificial Intelligence Algorithms in Flood Prediction: A General Overview

This paper presents a comprehensive general overview of the extensive literature available in the field of application of artificial intelligence (AI) in flood prediction. The initial approach involves examining the sources responsible for flood occurrence and the various components associated with them. Subsequently, it explores the potential of different remote sensing platforms and sensors in quantifying the diverse flood variables originating from these sources and components. These quantified variables are then utilized as inputs for training, testing, predicting, and validating AI models. A concise explanation of the AI concept, along with its subfields such as machine learning (ML) and deep learning (DL) algorithms has also been provided. Furthermore, a brief overview of prospective AI algorithms is provided. Moving forward, we summarize various types of AI methodologies and provide relevant references in the context of natural hazards, with a specific focus on floods. Finally, we discuss advancements in AI and highlight how it enhances flood modeling. Additionally, it presents insights into potential future applications in this field, emphasizing the continuous progress and the immense potential of AI in flood prediction. The findings suggest that the field of the chapter’s theme is highly relevant but there is significant lack of work in this area of research, and it should be one of the most important thrust area in the field of flood disaster prediction.