Search published articles


Showing 45 results for Ahmadi
Evaluation of TRMM and ECMWF Data for Drought Time Monitoring (Case Study: Kurdistan Province)
M. Ahmadi, H. Ramezani Etedali, A. Kaviai, A.r. Tavakkoli,
Volume 27, Issue 1 (Spring 2023)
Abstract
Studying the effects of drought in mountainous areas is facing problems due to the inappropriate distribution of stations, the lack of long-term data, and areas lacking statistics. Therefore, the main objective of this research was to investigate the drought indices of Kurdistan province using TRMM satellite data and ECMWF dataset, as well as to evaluate their accuracy against the data of land stations in Kurdistan province. First, ECMWF precipitation data for the 2000-2020 period and TRMM precipitation data for the 2000-2019 period were obtained and evaluated using RMSE, MBE, and correlation coefficient statistics. Spearman's correlation coefficient showed a significant relationship between the TRMM satellite precipitation data and the ECMWF dataset with ground stations at the 5% level, and the value of this coefficient was between 0.95-0.85. According to the results, it can be acknowledged that the TRMM satellite rainfall and ECMWF dataset in the monthly time scale had proper accuracy at the Kurdistan province level. Therefore, these two sources were used to examine the drought indices. SPI, SPEI, and ZSI drought indices were calculated in different monthly periods (1-48), PNI in different monthly, seasonal, and annual periods in Kurdistan province (Saqqez, Qorveh, Bijar, Sanandaj stations). Spearman's correlation coefficient indicated a significant relationship at the 5% level between the SPI, ZSI, PNI, and SPEI index of the ECMWF dataset with ground stations. The results of the SPI index showed that the lowest RMSE value for the TRMM satellite at the Saqqez station and the three months was equal to 0.45, and for the ECMWF dataset at the Sanandaj station and the 24 months was equal to 0.35.

Experimental Study of Bed Shear Stress in Straight Channels Covered with Dunes of Different Sizes
M. Badzanchin, M. Bahrami Yarahmadi, M. Shafai Bejestan,
Volume 27, Issue 1 (Spring 2023)
Abstract
The formation of bed form in alluvial rivers due to sediment transport has a significant effect on the hydraulic parameters of the flow such as bed shear stress. The formation of the bed form and its shape and geometry depends on the bed shear stress. Therefore, the relationship between bed form and flow parameters (such as bed shear stress) is complicated. In the present study; the effect of dune bed forms with different heights on bed shear stress has been investigated. Artificial dunes made by sand-cement mortar with a length of 25 cm and heights of 1, 2, 3, and 4 cm were used. In the tests of this research, flow discharge of 10, 15, 20, 25, and 30 l/s and bed slopes of 0, 0.0001, 0.0005, 0.001, and 0.0015 were used. The results showed that with increasing the relative submergence and Δ/λ, the bed shear stress increased in dune-covered beds. The formation of the dune bed form and the increase in its height leads to an increase in the bed shear stress. The bed shear stress in dunes with a height of 1, 2, 3, and 4 cm was, on average, 39, 80, 141, and 146% more than in plane beds, respectively. Moreover, form shear stress for dunes with a height of 1, 2, 3, and 4 cm was, on average, 27.37, 43, 57.11, and 58.74% of the total shear stress, respectively.

Experimental Investigation of the Effect of the Pier Group Skew Angle on Local Scour Around the Piers under Steady Flow
L. Hashemi, S. M. Kashefipour, M. Ghomeshi, M. Bahrami Yarahmadi,
Volume 28, Issue 2 (Summer 2024)
Abstract
Local scour around bridge piers is one of the most significant factors for the bridges’ destruction. Therefore, it is necessary to investigate the scour depth around the bridge piers. The effect of the skew angle of the single-column pier group related to the flow direction in two different arrangements including 1×2 and 1×3 piers on the maximum scour depth around the pile group was investigated in this study. The experiments were carried out under steady flow conditions. The pier group was placed in the 1×2 arrangement at the skew angles of 0 to 90 degrees and in the 1×3 arrangement at the skew angles of 0 to 45 degrees. The results showed that increasing the skew angle of the pier group is almost ineffective on the maximum scour depth around the first pier. However, it has a great effect on the maximum scour depth, its temporal development, and the expansion of the scour hole around the second and third piers in different arrangements of the pier groups. The maximum scour depth of the pier group in both different arrangements occurred at a skew angle of 30 degrees, in the arrangement of 1×2 around the second pier and by 13.33% more than the first pier and in the arrangement of 1×3 around the third pier and by 21.57% more than the first pier.

Wheat Production Simulation Using Sentinel 2 Images and Machine Learning Techniques
H. Ramezani Etedali, M. Ahmadi,
Volume 29, Issue 2 (Summer 2025)
Abstract
change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.

Drought and Land Use Change Monitoring using spectral indices and support vector machine (SVM) model (Case study: Bonab Rural District, Zanjan County)
Mehdi Feyzolahpour, Manizheh Ahmadi,
Volume 29, Issue 3 (Fall 2025)
Abstract
Drought is a hazard that can have widespread impacts on biodiversity, wildlife habitats, and ecosystem stability. The present study investigated the drought situation in the Bonab rural district. To assess the drought situation during the period 2013 to 2024, the Normalized Difference Vegetation Index (NDVI), Water Index (NDWI), Moisture Index (NDMI), Soil Adjusted Vegetation Index (MSAVI), and Land Surface Temperature (LST) were used. The results showed that the maximum value of the NDWI index reached from 0.16 in 2013 to 0.14 in 2024, which indicates an intensification of drought. However, the maximum value of the NDVI increased from 0.53 in 2013 to 0.58 in 2024, and the value for the MSAVI index for the years 2013 and 2024 was 0.69 and 0.73, respectively. All indices except the NDWI index had a negative correlation with the LST index, and the MSAVI index had the highest negative correlation with a Pearson coefficient of -0.39 in 2013. The results are also consistent with the results obtained from the SVM model. It is also observed that the area of barren lands has decreased from 887 square kilometers in 2013 to 851 square kilometers in 2024.

Page 3 from 3     

© 2026 CC BY-NC 4.0 | Journal of Water and Soil Science

Designed & Developed by: Yektaweb

تحت نظارت وف ایرانی