ArticleScientific reports2025
Future flood susceptibility mapping under climate and land use change.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Data-driven flood susceptibility assessment using hybrid machine learning and optimization techniques: case of the Sedrata Watershed, NE Algeria.Scientific reports · 2026Article
- Mapping coastal transformations with a novel Cellular Automata-Markov-Random forest framework for land use change modeling.Scientific reports · 2025Article
- Collaborative spatial decision making empowering urban flood governance toward city resilience.Scientific reports · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Floods are a significant natural hazard, causing severe damage. Understanding how climate change and land use and land cover (LULC) changes influence flood patterns is crucial for developing sustainable management strategies. This research aims to develop flood susceptibility maps considering the impacts of climate change and land use changes, providing insights into risks from urbanization and climate shifts. Three machine learning models-XGBoost, Random Forest (RF), and Support Vector Machine (SVM)-optimized with Particle Swarm Optimization, were applied to the flood-prone Kashkan watershed in Iran. Results showed that distance from the river, digital elevation model, precipitation, and LULC were the most influential factors. The RF model outperformed others in mapping flood-prone areas, with high-risk zones covering 20% (1908 km
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Registered trials
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