Results of the summarizing process for the arXiv paper: 1908.02781v1

Floods are devastating natural disasters that can cause significant loss of life and property damage. The complexity of floods makes them difficult to model; however, the advancement of flood prediction models has greatly contributed to risk reduction and policy suggestions. In the past two decades, machine learning (ML) methods have become increasingly popular among hydrologists due to their numerous benefits and potential for improving prediction systems by mimicking the mathematical expressions of flood processes. This paper aims to demonstrate the state-of-the-art ML models in flood prediction and provide insights into the most suitable approaches. It reviews literature on ML models benchmarked through qualitative analysis and evaluates and compares their robustness, accuracy, effectiveness, and speed. Additionally, it explores major trends for improving the quality of flood prediction models such as hybridization, data decomposition, algorithm ensemble, and model optimization. Furthermore, this paper discusses two important aspects related to ML algorithms: robust data enrichment techniques like implementing distribution functions or recovering missing variables; and generalization problem which refers to how well an ML algorithm can predict cases beyond its training dataset range. The methodology involves identifying peer-reviewed articles in top-level subject fields with performance evaluation and comparison of ML methods based on quality measures such as source normalized impact per paper, CiteScore, SCImago journal rank, and h-index. Overall this literature review provides a comprehensive overview of the state-of-the-art ML methods used in flood prediction highlighting their strengths and weaknesses while identifying effective strategies for improvement.

• Floods are devastating natural disasters that cause loss of life and property damage
• Flood prediction models have contributed to risk reduction and policy suggestions
• Machine learning (ML) methods are increasingly popular among hydrologists for flood prediction
• ML models in flood prediction are reviewed and benchmarked through qualitative analysis
• Robustness, accuracy, effectiveness, and speed of ML models are evaluated and compared
• Major trends for improving flood prediction models include hybridization, data decomposition, algorithm ensemble, and model optimization
• Robust data enrichment techniques and generalization problems are discussed in relation to ML algorithms
• Peer-reviewed articles in top-level subject fields are used for performance evaluation and comparison of ML methods
• The literature review provides a comprehensive overview of state-of-the-art ML methods in flood prediction, highlighting strengths, weaknesses, and strategies for improvement.

Flood Prediction Models: A Comprehensive Overview of Machine Learning Algorithms

Floods are among the most devastating natural disasters, causing significant loss of life and property damage. The complexity of floods makes them difficult to model; however, the advancement of flood prediction models has greatly contributed to risk reduction and policy suggestions. In recent years, machine learning (ML) methods have become increasingly popular among hydrologists due to their numerous benefits and potential for improving prediction systems by mimicking the mathematical expressions of flood processes. This paper aims to demonstrate the state-of-the-art ML models in flood prediction and provide insights into the most suitable approaches.

Overview of Machine Learning Algorithms

The paper reviews literature on ML models benchmarked through qualitative analysis and evaluates and compares their robustness, accuracy, effectiveness, and speed. It explores major trends for improving the quality of flood prediction models such as hybridization, data decomposition, algorithm ensemble, and model optimization. Additionally, it discusses two important aspects related to ML algorithms: robust data enrichment techniques like implementing distribution functions or recovering missing variables; and generalization problem which refers to how well an ML algorithm can predict cases beyond its training dataset range.

Methodology

The methodology involves identifying peer-reviewed articles in top-level subject fields with performance evaluation and comparison of ML methods based on quality measures such as source normalized impact per paper (SNIP), CiteScore, SCImago journal rank (SJR), and h-index.

Conclusion

Overall this literature review provides a comprehensive overview of the state-of-the-art ML methods used in flood prediction highlighting their strengths and weaknesses while identifying effective strategies for improvement. By understanding these algorithms better we can make more informed decisions when designing new predictive models that can help reduce risks associated with floods.