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
Quality Control in Remote Sensing
Remote Sensing of Aerosols and Atmospheric Profiles
Remote Sensing Vegetation Indexes and Soil Parameters
Remote SensingAerosol measurmentsMapping ang GISPhotogrammetryImprovement 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
- Ph.D., PhD, Tehran [Tehran - Iran]
Remote Sensing Applications
Physics of Remote Sensing
Analytical Photogrammetry
Environmental Modeling in GIS
Articles
International Journal of Environmental Science and Technology (17351472)22(9)pp. 7797-7814
Modeling PM2.5 concentrations in urban environments is complex due to the irregular distribution of air pollution monitoring (APM) stations, uncertainties in spatiotemporal relationships, and the dynamic, heterogeneous nature of urban environments. To address these challenges, this study proposes a novel three-stage framework to enhance PM2.5 modeling accuracy. First, a graph attention network (GAT) effectively handles the irregular distribution and uncertainty in spatiotemporal relationships by using multi-graphs to capture both spatial and temporal correlations between APM stations. The GAT's attention mechanism adaptively assigns greater weights to more relevant inputs, improving both interpretability and prediction precision. In the final stage, reinforcement learning, through the use of a Deep Q-Network (DQN), a reinforcement learning algorithm, optimizes the ensemble of GAT with deep recurrent networks long short-term memory (LSTM), and Gated recursive unit (GRU), dynamically adjusting model weightings to better adapt to rapidly changing urban environments. This framework significantly outperforms thirteen state-of-the-art models, demonstrating superior adaptability and accuracy in capturing PM2.5 dynamics. These findings offer a robust and scalable solution for air pollution prediction, with direct implications for public health interventions and urban policy planning. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2025.
Plant and Soil (15735036)509(1)pp. 377-397
Aims: Land surface emissivity (LSE) is an important variable in soil studies. Although there are various remote sensing methods to estimate LSE, accurately predicting LSE still remains a major challenge. Typically, the correlation between LSE and the Visible Near Infrared bands is employed for LSE estimation. However, some studies have raised some concerns about this correlation, especially in bare soil areas. Therefore, it is necessary to conduct further investigation to determine if there exists a nonlinear relationship between the LSE and other spectral bands, which was not detected by simple linear correlation/regression. Methods: In this study, firstly, a deep Auto-encoder network has been used to investigate the correlation between LSE and other spectral bands. Subsequently, we have applied a Conditional Generative Adversarial Network (CGAN) to estimate the LSE. The proposed CGAN was trained using the Landsat and ECOSTRESS satellite datasets. The performance of the developed network was then compared with NDVI-based method on satellite/simulated-based bare soil pixels. Results: For satellite data, the RMSE (Root Mean Squared Error) and correlation coefficient (R) between the estimated LSE using proposed CGAN and ECOSTRESS LSE are 0.005 and 0.97, respectively. For the simulated data, the RMSE and R between the estimated LSE and the simulated one are 0.01 and 0.92, respectively. Conclusion: The results of the deep Auto-encoder show considerable relationship between the LSE and Short-Wave Infrared bands which not be seen using simple linear correlation. In cases of satellite/simulated data of bare soils, the developed network showed superior performance compared to NDVI-based method. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Environmental Science and Pollution Research (09441344)31(40)pp. 53140-53155
Accurately predicting the spatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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
Remote Sensing Applications: Society and Environment (23529385)33
Drought is a complex natural disaster characterized by unique features influenced by environmental factors, particularly at a regional scale. Remote Sensing (RS) indices have proven to be valuable in assessing drought conditions. In this study, we utilized the five most commonly used RS indices: Normalized Difference Vegetation Index (NDVI), Deviation/Anomaly of NDVI (NDVI-Dev/ANDVI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and 2-band Enhanced Vegetation Index (EVI2). These indices were extracted over a 30-year period (1988–2017) using Google Earth Engine (GEE) for modeling the Standardized Precipitation Index (SPI) with various time-spans (1, 3, 6, 9, 12, and 24 months) as the Ground-Based (GB) drought indices. The models were trained from 1988 to 2014 and tested on data from 2015 to 2017. To enhance RS-based drought modeling over the complex and diverse study area of Iran, we proposed two different data clustering methods based on distinct environmental factors – climatic condition and land-cover type – and compared the results. The first method employed an enhanced-climate-based approach to improve upon the standard-climate-based method. This approach considered variations in the environment caused by seasonal changes in different climatic zones, which had received less attention in previous studies. Unique Support Vector Machine (SVM) models were trained using various clusters of inputs/outputs to assess the efficiency of the RS indices in predicting different drought conditions under various circumstances. Furthermore, we introduced an innovative data clustering method based on RS-derived land-cover similarity, which resulted in further improvements in drought modeling compared to agroclimatic zoning. The fusion-based drought modeling approach, using all RS indices as inputs in the enhanced-climate-based and land-cover-based methods, demonstrated an aggregated Overall Accuracy (OA) of 92.93% and 95.11% for predicting SPI-3, respectively. Notably, within the land-cover-based method, TCI exhibited the highest aggregated performance compared to other RS indices in predicting SPI-3 with an OA of approximately 95%. © 2023 Elsevier B.V.
