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mehdi Ramezanzadeh

mehdi Ramezanzadeh

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Faculty of Humanities and Social Sciences
Address:
Phone: 09126343108

Research

Title
Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed, Iran
Type
JournalPaper
Keywords
: Artificial neural network; Flood susceptibility; Machine learning; ROC curve; RFD approach; Tajan watershed
Year
2021
Journal Advances in Space Research
DOI
Researchers Mohammadtaghi Avand ، Hamidreza Moradi ، mehdi Ramezanzadeh

Abstract

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.