Mehdi Momeni Shahraki, Ph.D. ,

Assistant Professor

Department Of Geomatics Engineering
Faculty Of Civil Engineering And Transportation

Email

momeni@surv.ui.ac.ir

Phone

03137935288

Address

University of Isfahan Azadi square Isfahan, Iran
Postal Code : 8174673441

Overview

Biography

Dr. Momeni is full professor in University of Isfahan. He has been in the Surveying Engineering Department since 1998. He got his BSc in Surveying Engineering from University of Isfahan in 1996. He graduated in Tehran University from 1996 till 1998 when he got MSc in Photogrammetry. In 2001 he started as a PhD student in Tehran University. He studied Remote Sensing Engineering there and he got his PhD in 2005. Some of his interests have been: * Quality Control in Remote Sensing * Remote Sensing of Aerosols and Atmospheric Profiles * Remote Sensing Vegetation Indexes and Soil Parameters He follows them in the following topics now (2022): @ Improvement of cartographic features extraction for urban area mapping using object-based image processing @ Upgrading estimation of local environmental parameters in remote sensing @Upgrading soil moisture estimation methods in thermal IR remote sensing @ Establishing a local aerosol model and development of its extracted parameters especially aerosol dynamics

Research Interests

Quality Control in Remote Sensing Remote Sensing of Aerosols and Atmospheric Profiles Remote Sensing Vegetation Indexes and Soil Parameters

Remote SensingAerosol measurmentsMapping ang GISPhotogrammetry

Professional activities

Improvement of cartographic features extraction for urban area mapping using object-based image processing Upgrading estimation of local environmental parameters in remote sensing Upgrading soil moisture estimation methods in thermal IR remote sensing Establishing a local aerosol model and development of its extracted parameters especially aerosol dynamics

Education

Ph.D., PhD, Tehran [Tehran - Iran]

Teaching

Remote Sensing Applications Physics of Remote Sensing Analytical Photogrammetry Environmental Modeling in GIS

Research Output

All

Article

Articles

2025

Enhancing PM2.5 modeling with reinforcement learning: dynamic ensembling of multi-graph attention networks and deep recurrent models

( Article . )

Haghbayan s., S., Momeni shahraki, M., Tashayo b., B.

Publication Date: 2025

International Journal of Environmental Science and Technology (17351472)22(9)pp. 7797-7814

Spatiotemporal analysis of land surface temperature dynamics and biophysical influences across diverse climatic zones in Iranian cities

( Article . )

Abolhasani, M., Lotfi, A., Soffianian, A.R., Momeni shahraki, M., Kalantari, Z.

Publication Date: 2025

Sustainable Cities and Society (22106715)135

SWIR based estimation of TIR emissivity of bare soil surfaces using deep conditional generative adversarial network in Landsat data

( Article . Green )

Ataei, S., Momeni shahraki, M., Monadjemi, A.

Publication Date: 2025

Plant and Soil (15735036)509(1)pp. 377-397

2024

A new attention-based CNN_GRU model for spatial–temporal PM2.5 prediction

( Article . )

Haghbayan s., S., Momeni shahraki, M., Tashayo b., B.

Publication Date: 2024

Environmental Science and Pollution Research (09441344)31(40)pp. 53140-53155

Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf

( Article . Bronze )

Pashayi, M., Sattari, M., Momeni shahraki, M., Kavianpour, N.

Publication Date: 2024

Atmospheric Pollution Research (13091042)15(1)

Aerosol Optical Depth (AOD) retrieved using Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) plays a crucial role in aerosol sensing. However, the performance of these products varies across different aerosol concentrations. This research assessed the performance of CALIOP and MODIS AOD products over a three-year period (January 2017 to June 2019) under various aerosol concentrations in the Red Sea and the Persian Gulf. The products were compared with ship-based AERONET (Aerosol Robotic Network) measurements conducted in the study area. The findings reveal that MODIS AOD products exhibit greater reliability during clear days, whereas CALIOP AOD products are more accurate in regions characterized by high aerosol concentrations. However, CALIOP's limited product resolution prevents it from providing adequate spatial-temporal coverage under heavy aerosol concentrations. To enhance the accuracy of MODIS AOD, this paper proposes a methodology based on various machine learning (ML) algorithms, including Random Forest (RF), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVR), and Bayesian network. The study explores the performance of MODIS and CALIOP AOD products during moderate and pollution days in the Persian Gulf and Red Sea regions. The ML models are ranked based on their accuracy, with the order being XGBoost > MLP > SVRrbf > RF > Bayesian > SVRlinear for the annual and two semi-annual networks. In other words, the Bayesian method demonstrates higher efficiency (R2 = 0.97, 0.91, and 0.90) in handling seasonal subsets. The proposed model's outcomes are validated using ship-based AERONET AOD data. By comparing the estimated AOD values with the ship-based AERONET AOD values and analyzing the overall correlation results, it is evident that machine learning techniques effectively provide accurate AOD estimates for moderate and pollution days. © 2023 Turkish National Committee for Air Pollution Research and Control