hossein etemadfard

Industrialization and the escalated consumption of fossil fuels have resulted in global warming, which has caused alterations to nature, climate, and human life. The long-term monitoring and examination of parameters associated with global warming, particularly land surface temperature (LST), using advanced technologies like remote sensing, can contribute to a more comprehensive and precise understanding of this phenomenon. Solar energy during the daytime, raising its temperature, while dissipation of energy gradually occurs during the night, leading to a decrease in temperature. Here, a collection of images related to the LST was amassed over 20 years, from 2003 to 2023, for Khorasan Razavi Province in northeastern Iran, utilizing the MODIS data. These images were averaged weekly. Subsequently, the surface temperature trend encompassing all climates in the region was determined through linear regression. The findings demonstrate a general increase in the LST index during both daytime and nighttime; however, the rise in surface temperature during the daytime exhibits a more substantial increase compared to nighttime. The province is predominantly characterized by arid and semi-arid climates, with an average rise in surface temperature during the day of 0.80°C and 1.07°C per decade, respectively, and during the night of 0.55°C and 0.59°C per decade. Moreover, it was observed that there is no significant correlation between climate and surface temperature trends. The daytime LST trend in areas with agricultural land use displays a decreasing pattern, whereas the nighttime LST trend does not seem to be influenced by agricultural land use or vegetation cover.

Dust particles are one of the significant and fundamental challenges in dry, semi-arid, and desert regions, constantly causing numerous problems for the people and the environment in those areas. Assessments of the effects of aerosols on the climate and environment still feature large uncertainties, and a better understanding of the spatiotemporal variation in these effects is needed. Dust particles exist in various dimensions and sizes, dispersing in the air and influencing a region's environment. Identifying the sources of dust mass formation is crucial for taking necessary measures to combat it. The present study examines this issue in the Sabzevar City area. Using the satellite-based Aerosol Optical Depth (AOD) index collected from MODIS, the study focuses on mapping dust masses in a three-dimensional tensor approach, with each pixel having a dimension of 2 kilometers. The Google Earth Engine platform was used to extract satellite images for the region's study. The formation sources of dust masses during two different time intervals, June 2008 and March 2018, are studied. According to the obtained results, the sources of dust mass formation in Sabzevar are located in the plains surrounding the city of Davarzan, approximately 70 to 80 kilometers away from the study area, extending towards Sabzevar city. In the same way, the tensor tool can be used to accurately identify the source of pollution in different regions of the country and help to eliminate or reduce the pollution in the region. In addition to dust, this tool can be used to check industrial pollutants.

The increasing demand for sustainable food consumption as well as the change in the consumption pattern has led to efforts to improve the food distribution process. This is to speed up service delivery and prevent the spoilage of perishable materials. Among the most significant topics in the food supply chain is perishability, a phenomenon that occurs in certain categories of products such as fruits, vegetables, and dairy products. Perishability refers to the property in which a product loses its commercial value and usability after a certain period. However, meeting the general needs of citizens, especially the supply of food, is one of the most significant axes of urban service activities on the city's economic platform. In addition, the provision of comfort and well-being for residents depends on the proper establishment, optimal distribution, and sufficient variety of products offered in shopping centers. Day markets as well as fruit and vegetable fields provide fast and appropriate daily needs for residents. In addition, choosing fast and reliable routes for food distribution in the city is one of the other significant and influential factors in providing quality services. It should also be noted that in vehicle routing problems (VRP) related to food products, routes for vehicles must be created that match the schedules of some stores to deliver products.

To optimize the fruit and vegetable distribution routes between the fruit and vegetable fields and Shahre-ma stores in Mashhad, this research will use genetic algorithms and particle swarm algorithms. This research will have the aim of optimizing distribution time, which was not addressed in previous research. This research presents its innovation by considering a three-hour time limit in the problem-solving algorithm. Genetic Algorithm (GA) is a learning method based on biological evolution and influenced by the hypothesized mechanism of natural selection in which the fittest individuals in a generation survive longer and produce a new generation. And in this article, it is implemented in such a way that the algorithm itself determines the most appropriate number of vehicles. The number of vehicles should be such that distribution among all stores is done in less than three hours and five minutes in each store. There should be a stop. And if distribution among all stores is not done in less than 3 hours, a new vehicle will be added to the number of vehicles. Also, particle swarm optimization (PSO) is a technique inspired by the behavior of birds when searching for food. In this research, the data collected include the location of Shahre-ma stores and the fruit and vegetable square in Mashhad city. These data were prepared from the information of Mashhad municipality. Also, to implement these algorithms, MATLAB software has been used. Network analysis has been done to determine the distance between Bar Square and Shahre-ma stores in ArcGIS software using network analysis.

This research proposes several hypotheses, including that the maximum optimal time is 3 hours and products should be distributed by 7 am in all places. Also, city traffic is uniform from 4 to 7 in the morning and the same product package is distributed in all stores. Comparing the results of two genetic algorithms and particle swarm shows that the genetic algorithm has a higher efficiency in optimizing the distribution path of fruits and vegetables. Because the time of the four routes derived from the genetic algorithm is approximately 92 minutes, 84 minutes, 80 minutes, and 82 minutes respectively. The total length of all routes is 127 km and 779 meters and the total time of all routes is 338 minutes. And the time of the four routes obtained from the particle swarm algorithm is approximately 102 minutes, 103 minutes, 89 minutes, and 91 minutes respectively. The total length of all routes is 175 km and 390 meters and the total time of all routes is 385 minutes. And in total, the times obtained for four vehicles in the genetic algorithm were 47 minutes less than the particle swarm algorithm. In addition, the total length of the paths in the genetic algorithm was 47 km and 611 meters less than the particle swarm algorithm.

Conclusion:

The genetic algorithm was able to achieve the optimal solution by evaluating the objective function 12,000 times. This is 2,900,000 in the particle swarm algorithm. Accordingly, the time required to reach the optimal solution differs significantly between the two algorithms.

The Shatta al-Arab River is approximately 200 km long and is formed by the confluence of the Tigris and Euphrates rivers in the city of al-Qarna, 74 km northwest of the port city of Basra in southern Iraq. The river is affected by climate change and frequent rains, it shows the risk of flooding and overflowing at the riverside. The Shatta al-Arab River Basin in the city of Basra has been faced with floods and devastating consequences. The purpose of this study is to evaluate and analyze the spatial risk of river overflow of the Shatta al-Arab River in the city of Basra, Iraq, using the Geospatial Information System (GIS). GIS was used to estimate and evaluate the location and zoning of the Shatt al-Arab floodplain in Basra using the available data. By identifying the types of land uses around the river, provided a detailed analysis of how they are distributed in different risk areas. The results show that the agricultural landuse due to its large area is more at risk than other uses. The results of this study can have a significant impact on preventing irreparable flood damage in this area.

 One of the service centers that play an important role in the safety of the city is the fire stations. Prompt and timely access from these stations to the scene of the accident requires the optimal distribution of fire stations throughout the city. The study's purpose is to investigate the proper location of fire stations in the city of Mashhad using hybrid methods.

This research is an applied goal and is descriptive-analytical in type. Layers of information The criteria for distance from existing fire stations, medical centers, roads, administrative centers and training centers in the spatial information system environment are prepared and weighted using the Analytic Hierarchy Process (AHP). Then, the overlap map of these criteria was classified into five classes and the first 20 points in the most suitable classes for the construction of the fire station were selected. Then, using genetic algorithms (GA) and particle swarm (PSO) and using the criteria of distance from sports centers, religious centers, gas stations and gas stations, cultural, historical and commercial centers, these 20 selected points were compared with each other and The optimal point for the construction of a fire station has been proposed.

GA and PSO have proposed the same point with a fit function of 4.98 as the best place for the construction of a fire station in Mashhad.

The performance results of the two meta-heuristic algorithms show that according to the defined parameters for each of the algorithms, PSO reaches the optimal answer in less time than GA.

Urmia Lake; the Turquoise Gem of Northwestern Iran is the second-largest permanent hypersaline lake in the world. It is situated in the northwestern corner of Iran surrounded by East and West Azerbaijan provinces, near the Turkish border. This lake was declared a Wetland of International Importance by the Ramsar Convention in 1971 and designated a UNESCO Biosphere Reserve in 1976. Its surface, unfortunately, has declined sharply in recent decades. Scientists have warned that this continuous shrinking would lead to increased salinity, the collapse of the lake’s ecosystem, loss of wetland habitat, wind-blown salt storms, alteration of local climate, and serious negative impacts on local agriculture and livelihoods as well as regional health in Iran and neighbor countries including Turkey, Iraq, and Azerbaijan.Accurate and comprehensive spatial information (such as the lake’s surface and shorelines) and descriptive information (such as salinity of water and soil) of the lake are essential to deal with current and emerging crises. This information can be obtained by ground-based and space-based measurements. While daily measurement of the lake level at ground stations is one of the most important ground-based observations, but ground measurements of the lake's surface are not a practical method. Thanks to satellite remote sensing data, space observations have been provided for accurate and continuous monitoring of the Lake's surface and its water level. Since each of the ground and space observations has its pros and cons, many efforts have been made to estimate the correlation of these observations (ground-based observations of lake’s level and space-based observations of lake’s surface). Although many studies have been conducted in this field, however, no efficient model has been proposed to relate the surface area and the water level of Urmia Lake, which is an important factor in various hydrological and environmental analyzes. This paper aims to extract the level-surface characteristics of Urmia Lake using space observations of the lake and ground-based measurements of level and analytical models. Level-surface models have been determined based on ground observation of water level and simultaneous space observation of lake surface area. As a result, daily estimates of the lake surface will be provided without the space-based observations. In the present study, 6 proposed models including Polynomials, Exponential function, Fourier transformation, Gaussian function, rational function (RF), and artificial neural network (ANN) along with the two existing models including The EAWO (Eastern Azerbaijan Water Organization) model and the Sahand University model. To evaluate the effectiveness of mentioned methods, 44 samples include the lake’s surface (from Mar. 2005 to Dec. 2015) and corresponding water levels on the same date were used in this study. All of these models were calibrated by training data and evaluated based on validation data. Used data records include water level fluctuation from 1270.43 m to 1273.9 m above mean sea level and lake area (surface) fluctuation from 1837 sq. km to 4508 sq. km.Comparing the RMSE, MAE, MAPE, and NSE of all mentioned models, it was found that the proposed RF-based model (S=(3026×L^2-3.84×〖10〗^6×L-6051)/(L^2-1991×L+9.17×〖10〗^5 )) estimates the area of Urmia Lake only with an error of RMSE = 57.8 sq. km, MAE= 46.2, MAPE=1.5%, and NSE=0.9958, which is five times better than the best previous models in terms of RMSE measure. One level below is ANN whose RMSE equals 62.3 sq. km and NSE= 0.9951. Two-degree Gaussian function (with RMSE= 86.53 sq. km and NSE= 0.9907), Fourier transformation (with RMSE= 145.30 sq. km and NSE= 0.9251), two-degree Exponential function (with RMSE= 173.46 sq. km and NSE= 0.9626) and three-degree polynomial (with RMSE= 173.68 sq. km and NSE= 0.9625) were in the lower levels, respectively. It is worth noting that all of these proposed models are more effective than the state-of-the-art models. The worst results were obtained by the Sahand University model with an error of RMSE = 856.8 sq. km and NSE= 0.088). Despite the similar accuracy of ANN and RF models, the design and computational complexity of the ANN model are much greater than the RF model. Therefore, the RF model with higher accuracy and less complexity than the ANN is introduced and recommended as the best model for estimating the level-surface characteristic of Urmia Lake. According to the proposed level-surface model and daily ground-based measurement of the water level of the lake, it is possible to estimate the daily area of the Urmia Lake surface without any space measurements. The findings of this study can be used in various hydrological and environmental analyzes and monitoring and rehabilitation programs of Urmia Lake.

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the the COVID-19 pandemic. This study draws on a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It was found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad residents and tourists could not visit it and take photos to post on their Instagram accounts.

 The global outbreak of the COVID-19 in 2019, and the high mortality rate of the disease call for decision-making and finding a solution to control its spread. One of the most effective ways is to use the COVID-19 vaccine. Due to the limited supply of corona vaccines, the distribution of this vaccine is generally prioritized and allocated among individuals.

 In this descriptive correlational study, GIS, AHP tools, and fuzzy logic were used to achieve the goal of prioritizing and allocating corona vaccine to the neighborhoods in Mashhad. Neighborhoods' prioritization was analyzed in four scenarios including: AHP, WHO guideline, guideline of the Ministry of Health and Medical Education of Iran, and localized collective wisdom.

 The output of neighborhood prioritization of the four mentioned scenarios has been specified in five classes. In the AHP scenario, the lowest percentage (8.89%), and in the localized collective wisdom , the highest percentage (42.22%) was allocated to the neighborhoods with the first priority. Regarding spatial distribution, only in the first scenario, some kind of order was observed. Furthermore, there is generally no high correlation between the results , and only the scenario of the Ministry of Health and Medical Education and the localized collective wisdom presented a correlation of 0.82.

 Considering the COVID-19 vaccine shortage, spatial allocation based on the presented guidelines is a reliable method which can meet the basic criteria for allocating the limited treatment resources, and 180 neighborhoods throughout Mashhad were identified and prioritized with different scenarios which can assist the decision-makers.

Air pollution is considered a global concern due to its impacts on human life and the urban environment. Therefore, precise modeling techniques are necessary to predict air quality in congested areas such as megacities. Recently, machine learning algorithms such as Neural Networks show significant possibilities in air quality studies. This paper proposes a model to estimate air quality in a congested urban area in Baghdad city using Artificial Neural Network (ANN) algorithm and Geospatial Information System (GIS) techniques. Carbon Monoxide (CO) gas is selected as the main air pollutant. The model parameters involve; CO samples, traffic flow, weather data, and land use information collected in the field. The proposed model is implemented in Matlab environment and the results are processed after entering ArcGIS software. Using its spatial analysis tools, the outputs are presented as a map. The final findings indicate the highest value of CO emissions that reached 34 ppm during the daytime. The most polluted areas are located near congested roads and industrial locations in comparison with residential areas. The proposed model is validated by using actual values that are collected from the field, where the model's accuracy is 79%. The proposed model showed feasibility and applicability in a congested urban area due to the integration between the machine learning algorithm and GIS modeling. Therefore, the proposed model in this research can be used as a supportive model for decision making of city managers.