Simin Akbariyeh

Dr. Simin Akbariyeh


Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 November 2020 in Environments
Verified
Journal Article
Open access
PDF file
FYFan Yu
Fan Yu
AMAmin Mohebbi
Amin Mohebbi
SCShiqing Cai
Shiqing Cai
SASimin Akbariyeh
Simin Akbariyeh
BRBrendan Russo
Brendan Russo
ESEdward J. Smaglik
Edward J. Smaglik
This study aims to develop a hybrid approach based on backpropagation artificial neural network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using aerosol optical depth (AOD) and several meteorological indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using the Motor Vehicle Emission Simulator (MOVES). The measured meteorological variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA’s Moderate Resolution Spectroradiometer (MODIS), respectively. The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150. Coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with R2 = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with R2 = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can reasonably predict the PM2.5 mass and can be used to forecast future trends.

1 May 2021 in Journal of Hydrologic Engineering
Verified
Journal Article
Closed access
PDF file
AMAmin Mohebbi
Amin Mohebbi
SASimin Akbariyeh
Simin Akbariyeh
MMMontasir Maruf
Montasir Maruf
ZWZiyan Wu
Ziyan Wu
JAJuan Carlos Acuna
Juan Carlos Acuna
KAKatlynn Rose Adams
Katlynn Rose Adams
The ideal framework for water infrastructure design in any region relies on rainfall characteristics of that region, which is defined through rainfall intensity-duration-frequency (IDF) curves. The current IDF curves are based on historical observations of precipitation. However, with the help of numerical models, more up-to-date IDF curves can be developed to reflect the current precipitation regime. Here, a weather research and forecasting (WRF) model was applied to produce the precipitation data for Arizona from 1950 to 2017. A total of 20 weather forecasting scenarios were simulated by changing the microphysics schemes to improve precipitation forecasting accuracy. The National Severe Storm Laboratory (NSSL) scheme with cloud condensation nuclei (CCN) improved the coefficient of determination by 10% and was selected as the optimum forecasting scenario. The IDF curves were then constructed based on the modeled data and annual maximum series analysis for each climate division in Arizona. The comparison between updated IDF curves and historical IDF curves showed that incorporating up-to-date precipitation data resulted in lower rainfall intensities for short durations.

15 October 2020 in Preprints.org
Verified
Preprint
Open access
PDF file
FYFan Yu
Fan Yu
AMAmin Mohebbi
Amin Mohebbi
SCShiqing Cai
Shiqing Cai
SASimin Akbariyeh
Simin Akbariyeh
BRBrendan Russo
Brendan Russo
ESEdward J. Smaglik
Edward J. Smaglik
This study aims to develop a hybrid approach based on backpropagation Artificial Neural Network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using Aerosol Optical Depth (AOD) and several climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using Motor Vehicle Emission Simulator (MOVES), the measured climatic variables and AOD were obtained from the California Irrigation Management Information System (CIMIS), and NASA’s Moderate Resolution Spectroradiometer (MODIS). The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150, and precipitation was determined to be the most important independent variable. Coefficient of determination ( ), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can accurately predict the PM2.5 concentration and can be used to forecast future trends.