ArticleScientific reports2021
A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed.
- Beyond the Individual: A Data-Driven Approach to Protect Kidneys from Environmental Change.Health data science · 2026Article
- Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India.Scientific reports · 2025Article
- Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province.Scientific reports · 2025Article
- Article
- Study on long short-term memory based on vector direction of flood process for flood forecasting.Scientific reports · 2024Article
- Gullying and landscape evolution: Lavaka in Lac Alaotra, Madagascar shed light on rates of change and non-anthropogenic controls.Science advances · 2024Article
- Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia.Scientific reports · 2023Article
- Multi-hazard risk characterization and collaborative control oriented to space in non-coal underground mines.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.