نشریه کاربرد سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی
سال چهارم شماره 10 (بهار 1403)

In this research, an attempt has been made to provide futuristic strategies in urban water supply and management by estimating the population, consumption of citizens in region 5 and identifying the influencing factors on urban water supply and management, and to find the best futuristic strategy. It was used for short-term use in the 5th area of Tabriz.In this research, the population was predicted using the People software, and then each component was weighted using the Ahp model, and the best strategy was selected using the ExpertChoice software.Among the strategies (strategies to reduce water wastage, intelligentization of water infrastructure, education and information, incentive and punishment policies, increasing water knowledge, economical technologies and modern water resources) presented in order to improve conditions, the strategy of intelligentization of water infrastructure with a coefficient of 0.310 The title of the most optimal strategy has been introduced.Traditional and old strategies of urban water supply and management mechanisms will not be responsible for future cities and their population growth; Therefore, in order to improve its fields, forward-looking strategies have been presented and actions should be taken to change the ineffective traditional organizations. In this article, an attempt has been made to choose the most optimal strategy for the 5th region of Tabriz by presenting several active and passive strategies in the field of supply and management and identifying the major influencing factors in the water supply process of Tabriz city.

on the agenda of crisis management of Tabriz. One of the things to consider before constructing these databases is study and select a suitable location for the establishment of this type of use.In this study, a multi-location site of crisis management has been studied in Regions 4 and 10 of Tabriz Municipality. In the first instance, was paid to identify and analyze factors influencing site's Site selection. Raster maps were obtained after collection and preparation of the layers, and then weighting parameters was performed using the AHP in software Expertchoice. Next, the information layer, integrated with each other, based on fuzzy models, and finally combining the results of this stage, the options as desired location, were introduced, among their southern part of the four, was identified as the preferred option. Due to the geographic information system, in solving complex urban issues, and facilitate spatial analysis, has been applied, from ability of this system to prepare, integrate and analyze of layers.as a result of this extraction,bakhsh,south of the 4 region,has the address of a garden, which indicates the appointment of a suitable monthly residential property,such as sabz, amuzshi, warzahi, and … appropriate domestic policies and conditions this is priority to buy for the stability of this water.i am going to the place of my neighbourhood,and iam connected to it, park Hai Amir Kabir.

In addition to natural disasters such as floods, which cause great damage to the environment and human societies, the phenomenon of dust also causes irreparable damage to urban environments, transportation systems, respiratory systems, etc. Identifying Potential Dust Source Areas is considered the first step to control and prevent this phenomenon. Various researches have been conducted to identify dust sources, but most studies have relied on small-scale images. This research aims to use medium-scale satellite images to identify local areas prone to dust production. Google Earth Engine was used to analyze factors influencing dust generation, including slope, Digital Elevation Model (DEM), land use, soil salinity, Normalized Difference Vegetation Index (NDVI), soil moisture, wind speed, precipitation, and Land Surface Temperature (LST). Analytical Hierarchy Process (AHP) was used to assign weights to these elements. The resulting weights were: soil moisture (0.264), NDVI (0.208), wind speed (0.153), precipitation (0.107), land use/soil salinity (0.081), LST (0.064), DEM (0.024), and slope (0.020). The inconsistency index (0.015) indicated a high degree of consistency between the assigned weights, which is below the acceptable threshold (1). Due to the lack of ground-based air quality measurements, the Aerosol Optical Depth product (a satellite-derived measurement of airborne particles) was used to validate the resulting dust source maps. The final map showed that the potential for dust generation decreased closer to the heights of Sahand and increased closer to the lake.

Land surface temperature, as one of the important and fundamental parameters in climatology, indicates the relationship between the atmosphere and the Earth. Considering the environmental issues of cities, including the intensification of urban heat islands, accurately estimating LST and identifying its influencing factors play a significant role in urban thermal management and adopting adaptive strategies for heat islands. In this regard, this study compares two regression methods multiple linear regression and random forest in order to estimate the LST. Daily nighttime MODIS images were used to extract the LST of Tabriz city during the summer. These images were processed in the Google Earth Engine platform and averaged for the period from 2018 to 2022. According to the results, the random forest showed significantly better performance with a coefficient of determination of 0.924 (RMS = 0.009) compared to multiple linear regression. The random forest was also used to determine the importance of the indices. Based on the index importance results, night lights (51/06%), sky view factor (48/01%) and frontal area index (45/27%) were the most important factor affecting the nighttime summer LST in Tabriz city, respectively. The findings of this study, in addition to revealing the strength of the random forest regression in estimating LST, also highlight the importance of various indices in the LST. In this context, the study's results will be practical for managing the thermal environment of Tabriz city and adopting mitigation strategies for its heat islands.

Urban areas have undergone significant changes over time, which has led to the phenomenon of "urban climate change". This study aimed to investigate the role of climatic parameters in the formation of UHII using remote sensing techniques, with a case study of Tabriz metropolis, Iran. In this study, Landsat 8/9 satellite images were used to calculate land surface temperature (LST) and urban heat island intensity (UHII) and ERA5_Land reanalysis data for meteorological parameters for the spring and summer seasons of 2023. Split Window algorithm was used to calculate LST and UHII algorithm was used to check the intensity of urban heat islands. Also, statistical analyzes of linear regression and multi-criteria or multi-variable regression and Pearson's correlation were used for the relationship between UHII and climatic parameters. The results show that bare areas without vegetation, as well as areas close to industries and airports, have the highest surface temperature and UHII. On the other hand, areas with high building density and abundant green spaces have lower surface temperatures. Analysis of meteorological parameters shows that moving from west to east in the study area, air temperature and surface pressure decrease, while precipitation increases. In addition, the wind speed increases from west to east in spring and decreases in summer, while the surface solar radiation has been constant in both seasons of spatial induction.

The current research was conducted to evaluate the effectiveness of the reanalysis data of Era-interim, Agera5, and station data to simulate runoff using the Sacramento model in the Shapur watershed. Focusing on an innovative approach, this study provides solutions for integrating reanalysis data into runoff modeling that can improve the flexibility and accuracy of hydrological models. The research data were analyzed by statistically comparing the simulated discharge with the observed discharge at the watershed outlet on a daily time scale. The results showed that the discharge simulated by the station precipitation data with a correlation coefficient of 0.93 with the observed discharge performs better than the discharge simulated by the reanalysis data. Also, among the reanalysis data, the Agera5 data with a correlation coefficient of 0.82 performs better than the Era-interim data. The results of Era-interim data show an underestimation in the amount of precipitation, which is because it is located in the Shapur watershed (the coastline bordering the Oman Sea and the Persian Gulf). Also, in the review of the long-term monthly data, the reanalysis data in the hot months of Dubai has not been correctly simulated, which is due to the durability and low thickness of the clouds, and this has caused a decrease in the accuracy of the amount of precipitation. Finally, due to the proper distribution of rain gauge stations in this basin, the results obtained from the station data are better than the reanalysis data.