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.
 

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.

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.