H
Hamid Reza Moradi
Researcher at Tarbiat Modares University
Publications - 78
Citations - 1660
Hamid Reza Moradi is an academic researcher from Tarbiat Modares University. The author has contributed to research in topics: Landslide & Climate change. The author has an hindex of 17, co-authored 47 publications receiving 1206 citations.
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Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances
TL;DR: In this article, the authors presented a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances, where the study area covers the north of Tehran metropolitan, Iran.
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Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran
TL;DR: In this article, the authors investigated the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran.
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GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran)
Hamid Reza Pourghasemi,Hamid Reza Moradi,S. M. Fatemi Aghda,Candan Gokceoglu,Biswajeet Pradhan +4 more
TL;DR: In this article, the authors used remote sensing and GIS integrated techniques to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran.
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Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)
TL;DR: In this paper, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead.
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Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability
TL;DR: In this paper, the authors investigated the effects of climate and land use changes on flood susceptibility areas in the Tajan watershed, Iran and found that elevation (21.55), distance from river (15.28), land use (11.1), slope (10.58), and rainfall (6.8) are the most important factors affecting flooding in this basin.