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Showing 4 results for Bosak

Ms Atefeh Bosak, Dr Zahra Hejazizadeh, Dr Akbar Heydari Tashekaboud,
Volume 0, Issue 0 (3-1921)
Abstract

Air pollution has significant impacts on human health, environmental quality, and the sustainable development of cities. This study aimed to evaluate PM10 using meteorological data from the city of Ahvaz through statistical methods and artificial neural networks. Daily meteorological data and air quality control station data for 4485 days (from 2011 to 2023) were obtained from the National Meteorological Organization and the Khuzestan Department of Environment. Initially, the data were processed and refined, and their normality was assessed using the Kolmogorov-Smirnov test. Given the non-normality of the data, Spearman's and Kendall's Tau-b methods were employed to examine their correlations. The time series and statistical information of the data were obtained using Python programming language. Furthermore, to predict future PM10 levels, the Multilayer Perceptron (MLP) neural network method was utilized. The results of these analyses indicated a significant correlation between meteorological variables and PM10. The Spearman and Kendall Tau-b correlations showed that PM10 had a positive and significant correlation with wind speed (0.094 and 0.061) and temperature (0.284 and 0.187) at a 99% confidence level. Conversely, PM10 exhibited a negative and significant correlation with visibility (-0.408 and -0.300), wind direction (-0.048 and -0.034), precipitation (-0.159 and -0.125), and relative humidity (-0.259 and -0.173) at the 99% confidence level. For future PM10 predictions, the MLP neural network was used. The model was of the Sequential type with an input layer consisting of 6 neurons, three hidden layers of Dense type with 16, 32, and 64 neurons, and an output layer with a linear activation function. The mean squared error (MSE) for the training set was 0.0034, and for the validation data, it was 0.0012. For the test set, the obtained validation accuracy was mse_mlp=0.0048 and val_loss=0.0012. The results indicate a significant direct or inverse correlation between meteorological data and PM10. Additionally, the outcomes of the MLP neural network demonstrated that the network provided satisfactory performance and acceptable predictions for PM10 data in Ahvaz.

Dr Zahra Hejazizadeh, Mr Meysam Toulabi Nejad, Mr Alireza Rahimi, Mrs Nasrin Bazmi, Mrs Atefeh Bosak,
Volume 17, Issue 47 (12-2017)
Abstract

The aim of this study is modeling spatiotemporal variations of albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of albedo values in Iran showed that the highest value is in 44 to 45 degrees of east longitude about 2.8 to 3.3 and the lowest value of albedo is also in 52 to 53 degrees of east longitude, that is, the eastern slopes of the Zagros Mountains, have been recorded at 1 to 1.5 units. In terms of provincial rank, the largest albedo is about 0.25 units in Ilam province and the Fars province is ranked next about 0.24 units. The lowest amount of albedo also in the Gilan provinces and in next Mazandaran province are about 0.19 and 0.18 respectively. In addition, the results of temporal analysis in seasonal scale showed that the highest albedo in Iran in winter was 0.26 and its lowest amount was recorded in spring with 0.23 units. In general, according to the factors used, it can be said that the western and central parts of the country have a highest albedo, and the north and northwest regions of the country have a lowest albedo.
 

Meysam Toulabi Nejad, Dr Zahra Hejazizadeh, Mrs Atefeh Bosak, Mrs Nasrin Bazmi,
Volume 18, Issue 49 (3-2018)
Abstract

The purpose of this study was to investigate the effects of the North Atlantic Oscillation on the middle levels of Atmosphere and precipitation changes in the West of country. To do this, first monthly rainfall data of 17 synoptic stations of the West Country in period of 30 years from 1984 to 2014 of country were collected from Meteorological Organization. As well as North Atlantic Oscillation data and anomalies geopotential height data, sea level pressure and precipitation were received from NOAA. To clarify the relationship between the NAO index phase with precipitation of west of Iran used Pearson correlation coefficient was at least 95%, (P_value = 0.05). Finally, using synoptic maps, spatial relationships among data, were analyzed. The results indicate that between North Atlantic Oscillation changes with middle level height anomalies of the Atmosphere and the amount of precipitation in West of Iran in January, March, April and November there is communication and concurrency.  The results showed that , at a time of sovereignty positive phase of the North Atlantic oscillation , an average of height atmospheric middle level in mid - western Iran 17 meters long - term and less than the average rainfall per month 23.5 mm increased and wetly sovereign. But when phase of governance is negative, high atmospheric middle level anomaly to an average of 20 meters more than normal. As a result, the drought will prevail in the west and precipitation in the region each month will face a reduction of 30 mm. In general, we can say that droughts more severe than wet coincide with the negative phase of the North Atlantic Oscillation is positive phase.

Mr Milad Khayat, Ms Atefeh Bosak, Dr Zahra Hejazizadeh, Dr. Ebrahim Afifi,
Volume 25, Issue 76 (3-2025)
Abstract

By employing urban growth and development modeling, it is feasible to delineate a developmental trajectory that aligns with the specific circumstances of a city, considering environmental factors, natural elements, and population dynamics. The aim of this research is to propose an urban development model for Shushtar, which can serve as a valuable tool for analyzing the intricate processes of urban transformations. To accomplish this objective, two datasets were utilized: urban land use maps (including educational spaces, healthcare facilities, residential areas, etc.) and Landsat satellite imagery for key land uses such as rivers, barren lands, and forests, spanning three time periods: 1991, 2004, and 2014. These datasets were processed using GIS and MATLAB software. Existing urban land use maps were digitized and subsequently updated using Landsat satellite imagery. Subsequently, influential parameters in urban development were introduced as inputs to the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. After training the model for the years 1991 and 2004, the predicted results of urban development using the algorithm were compared with the actual situation in 2014, demonstrating a high accuracy of 93.7%. The land use change map, resulting from the change detection process, can be generated based on multi-temporal remote sensing images and their integration with urban land use maps, enabling an analysis of the associated consequences. The use of intelligent algorithms in this research has facilitated modeling with a high level of accuracy. The obtained results are deemed acceptable, and this development has also been predicted for the upcoming years.


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