Journal of Ecohydrology

Journal of Ecohydrology

Comparative evaluation of machine learning and remote sensing models in flood zoning of the Nekarood basin

Articles in Press

Document Type : Research Article

Authors
SeidMohamad Mousavi 1
Fatemeh Shokrian 2
Karim Soleimani 2
Omid Ghorbanzadeh 3
1 sari agricultural sciences and natural resources university
2 Department of Watershed Engineering - Faculty of Natural Resources of Sari - Sari University of Agricultural Sciences and Natural Resources - Sari - Iran
3 Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna, Austria
10.22059/ije.2026.402776.1882
Abstract
The present study was conducted with the aim of flood risk zoning in Nekarud. For this purpose, some important variables were prepared using multi-source data and standardized in a GIS environment. Random forest, support vector machine and multilayer perceptron machine learning algorithms were used for modeling and their performance was evaluated with indicators such as overall accuracy, F1 score and area under the curve. The results showed that the multilayer perceptron model had the best performance with an accuracy of 94.5% and an area under the curve of 91.11%, while the random forest model with an accuracy of 88.64% and an area under the curve of 88.64% and the support vector machine model with an accuracy of 87.2% and an area under the curve of 82.92% were ranked next. In terms of spatial distribution, the random forest model assigned the highest contribution to the medium and low flood risk classes, the support vector machine model predicted a higher contribution to high-risk areas, and the multilayer perceptron model provided a balanced distribution. Analysis of the significance of variables in the random forest model showed that distance from the river, slope, and topographic moisture index played the greatest role in flood occurrence.
Keywords
'
Flood risk zoning'
, '
machine learning'
random forest'
support vector machine'
,'
Nekarood basin'
Subjects
Journal of Ecohydrology

Articles in Press, Accepted Manuscript
Available Online from 19 April 2026

  • Receive Date 20 September 2025
  • Revise Date 01 February 2026
  • Accept Date 19 March 2026
  • First Publish Date 19 April 2026
  • Publish Date 19 April 2026