Shirani et al. (2025) An integrated machine learning framework for flood susceptibility assessment

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-29
• Authors: Kourosh Shirani, Mehrdad Pasandi
• DOI: 10.1016/b978-0-443-26722-2.00014-3

Research Groups

• Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Tehran Province, Iran
• Department of Geology, University of Isfahan, Isfahan, Isfahan, Iran

Short Summary

This chapter introduces an integrated machine learning framework for flood susceptibility assessment, emphasizing the critical and complex interplay between drought conditions and subsequent flood events. The provided text is an introduction and does not present the framework's specific findings.

Objective

• To develop and apply an integrated machine learning framework for flood susceptibility assessment, considering the intricate links between drought and flood phenomena.

Study Configuration

• Spatial Scale: Not specified in the provided introductory text. The discussion covers general regions, South Asian countries, and global/regional climate systems.
• Temporal Scale: Not specified in the provided introductory text. The discussion refers to seasonal transitions, prolonged droughts, and cyclical phenomena.

Methodology and Data

• Models used: The chapter title indicates an "integrated machine learning framework." Specific models are not detailed in the provided introductory text.
• Data sources: Not specified in the provided introductory text. The introduction discusses general hydrological and climatic phenomena (e.g., precipitation, river levels, vegetation, oceanic-atmospheric patterns).

Main Results

No specific results from the integrated machine learning framework are presented in this introductory chapter.

Contributions

• Proposes an integrated machine learning framework to assess flood susceptibility, specifically addressing the complex and often overlooked interactions between drought conditions and subsequent flood risks.
• Highlights the importance of considering factors like soil compaction, vegetation loss, altered riverbeds, and climate change impacts in flood risk assessment.

Funding

No funding information is provided in the text.

Citation

@article{Shirani2025integrated,
  author = {Shirani, Kourosh and Pasandi, Mehrdad},
  title = {An integrated machine learning framework for flood susceptibility assessment},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-26722-2.00014-3},
  url = {https://doi.org/10.1016/b978-0-443-26722-2.00014-3}
}