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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. [ABSTRACT FROM AUTHOR]

One of the most fundamental aspects of any country is the housing economy. Because its price changes will cause numerous effects on the national economy in the short and long periods; therefore, it is essential to obtain a model which can assess housing prices. In this regard, the objectives of this research are to compare multiple linear regression and random forest regression to evaluate the price of residential units and extract the important factors in relation to the price of them. The statistical population included four north valiasr neighborhoods (n=30,272 units) and the sample size was estimated to be 379 units using the cochran formula at 95% confidence level and with a 5% error. But 400 samples considered. To eliminate the effect of time, only the data of september, 2020 were used. Also, arcmap, spss and rstudio software were used to analyze the data. According to the results, area, apartment floors, construction year, proximity to health centers, urban facilities, green spaces, religious land-use, medical centers, military land-uses and floor level, are the ten most important variables in relation to housing prices in the north valiasr neighborhoods, respectively. Further, according to the findings, random forest regression has a superior capability in predicting housing prices in north valiasr of tabriz compared to multiple linear regression.

Population growth and the increase in urban migration in the past decades have led to an increase in population density and size of major cities. Unfortunately, this kind of pollution has mostly gone under-noticed. The purpose of this research was to model the correlation between noise pollution level and landscape metrics of urban structures and vegetation. To do so, 67 stations were selected in different parts of Isfahan and noise parameters were measured at peak traffic hours (16 to 19) during the fall season. Sampling stations were located through a systematic-random method based on the amount of construction, green spaces and structural diversity. There were 27 types of landscapes and three stations were randomly selected in each. In most stations, the noise level was above the permitted level(Residential 45-55, Residential-Commercial 50-60). The advanced regression method of random forest was used for the analysis. Through this method, the most effective metrics identified in different buffers were IJI index, FRAC_MN index, CLUMPY index, CONTIG_MN index, SHAPE_MN index, ENN_MN index. Also, checking of the first six metrics in each of the buffers and land uses showed the importance of the metrics is different. Identification of important metrics in each buffer and land use helps better design urban blocks and their arrangement.