The present study investigated the impacts of NAO and ENSO on the precipitation in the southern shores of Caspian Sea. The accumulated monthly and annual rainfalls from 5 synoptic stations during the years (1956-2017) were taken through Islamic Republic of Iran Meteorology Organization (IRIMO) and the Multivariate Enso Indices (MEI) and NAO activity years are obtained from National Oceanic Atmospheric Administration. Pearson correlation was used to investigate the relationship between indices and precipitation amounts of selected stations. The results showed that there was a significant relationship between precipitation and NAO index in some months in all stations but this correlation was not following a particular pattern in all the stations. The maximum correlations were observed at Babolsar and   Anzali station and the least correlation was found at  Gorgan stations. The correlation between precipitation and different phases of NAO showed that there was a positive correlation between precipitation and negative phase of the index in Ramsar station and a negative correlation between precipitation and positive phase in the Gorgan station.The results of the Pearson correlation show a significant correlation between the MEI and rainfall amounts in the autumn in some stations in the early winter. In Review drought and wet periods with both Indicator it was observed that the behavior of the stations in the El Niño period, which was with different phases of the NAO was not entirely harmonious but the coefficient of 89% of rainfall in normal and more than normal during the period of El Niño showed that Elnino is better fitted to normal and more than normal rainfall in these stations also coefficient of 60%  of weak to severe droughts in the Lanina period in the selected stations Indicates that the LaNina phase was more related with severe droughts in the under studied period.

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.