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Showing 15 results for Shamsoddini
Volume 5, Issue 1 (Winter 2023 2022)
Abstract
A subfield of political geography is election geography, which investigates issues of election geography, including the spatial layout of elections, the diversity of spatial voting patterns, and the impact of spatial and geographical factors on electors' decisions. The neighborhood voting pattern is one of the diverse voting patterns that voters typically use to express their preferences for various candidates in accordance with their needs, convictions, and way of life. In accordance with this model, voters from a community who inhabit in a particular geographic location, such as a neighborhood, village, city, or province, identify with candidates who were born or now reside there and believe that the candidate from that particular area of their hometown, more aware of their challenges and issues then they support him more. This study aims to examine how neighborhood and tribe tendencies varied in the 11th Islamic Consultative Assembly term in the Boyer Ahmad Dana and Margun constituencies. GIS and EXCEL software were employed in the descriptive and analytical study method to better represent the problem. The research's conclusions lead to the neighborhood variable, hometown tendencies, and tribal tendencies are the most significant influencing factors on the voting pattern of the electoral candidates of the aforementioned constituency. On the other hand, it was discovered that the impact of the neighborhood is greater in some cities and districts (Boyrahmad and Dana) and less in some locations and spaces by examining the quantity and intensity of neighborhoods among clans and ethnic groups residing in the cities of the said constituency (Margun). These differences are brought about by how many people live in the cities indicated.
Volume 12, Issue 1 (Spring & Summer 2008)
Abstract
Difference in pixel size between multi-spectral and pan images, is one of the main effective factors on the spectral and spatial performance of image fusion methods. In this research, after producing simulated images with different pixel sizes of 8, 12, 16, 44 meters from IKONOS multi-spectral images and 4 meters from IKONOS pan image,by using four different fusion algorithms (i.e. Brovey, PCA, wavelet and combination of PCA and wavelet algorithms),all the simulated multi-spectral and pan derived images were fused.Correlation coefficient index and entropy were used to assess the spatial and spectral quality of the fused images, respectively. The results showed that the effect of increasing of multi-spectral images resolution difference compared with pan image on spectral and spatial quality of the fused images is related to the methods used for image fusion. Among the methods used for image fusion, Brovey transform and PCA-wavelet transform methods have the lowest and highest sensivity respectively, in respect to the variations of resolution difference. Also the relationship between spectral and spatial quality changes of the fused images with respect to the increasing of difference in pixel sizes of multi-spectral and pan images is non-linear.
Rahim Shamsoddini, Mohammad Sefid, Rouhollah Fatehi,
Volume 14, Issue 11 (2-2015)
Volume 14, Issue 11 (2-2015)
Abstract
In the present study, the mixing fluids flow in the twin and circular mixers is investigated by using an improved robust weakly compressible Smoothed Particle Hydrodynamics method. In order to remove the Smoothed Particle Hydrodynamics complications and according to a predictive corrective scheme, a robust modified algorithm which uses the advanced second order discretization, pressure velocity decoupling, kernel gradient corrections and shifting algorithm is offered. After the verification and validation of the present algorithm for the moving boundary problems, the present algorithm is applied for investigation of the mixing behaviors of the two-blade circular and twin chamber mixers. By investigation of the mixing paths, the proper geometry for the two-blade mixers is proposed and examined. The effects of the rotation direction of the blades, geometry and Reynolds number on the mixing rate are investigated. The results show that the twin chamber mixer can improve the mixing performance over 60% in comparison with the circular chamber mixer while the case with circular chamber and same direction rotation of the blades has the weakest performance among the cases which have been examined.
Sajad Ghanbari, Mohammad Sefid, Rahim Shamsoddini,
Volume 16, Issue 8 (10-2016)
Volume 16, Issue 8 (10-2016)
Abstract
In this present study, the mixing of two incompressible miscible fluids with different density and viscosity has been investigated in a two-dimensional microchannel equipped with an oscillating stirrer in different excitation frequency. Although most studies in the field of fluid mixing, have been studied the mixer performance when the two fluids were absolutely identical, but the mixing make sense when two fluids has been non-uniformity such as different temperature, concentration or properties. The aim of this study is to evaluating the effect of various properties of the fluids in mixer performance and mixing value. Simulation has been performed in Re=100 and Sc=10, between 0.1 to 1 strouhal number by using element based finite volume method by means of commercial code CFX. Mixer performance has been evaluated in three different modes: mixing of two identical fluids, mixing of two fluids with different density and mixing of two fluids with different viscosity. The results show that, mixing of the fluids with different properties leads to change in mixer performance, and has unique performance in each case. In comparison with similar properties fluids, mixing of fluids with different viscosity and density show lesser inclined in mixing. It has been shown that variation of strouhal number has lesser effect on mixing index changes. The ratio of maximum mixing index changes to base mixing index in the case of different density and viscosity is 54.01 and 51.15 percent, respectively, while the value is 577.94 percent for the mixing of similar fluids.
M.k. Tahmasebi, R. Shamsoddini, B. Abolpour,
Volume 20, Issue 2 (February 2020)
Volume 20, Issue 2 (February 2020)
Abstract
The motion of the liquid free surface in a container (sloshing phenomenon) inserts a momentum on the container walls. This makes a great disorder in the movement of the carrier vehicle or inserts a large force and momentum on the container walls. The reason for this phenomenon is the establishment of destructive waves and hydrodynamic forces. The side effects of this phenomenon in various industries, such as ship industries carrying liquid fuels, liquid fuel rocket industries, fuel tanks or water tanks, increase the importance of predictions of the behaviors of this phenomenon. One way of controlling is to use baffles or plates in the transverse direction of the tank. In this study, the governing equations on this phenomenon have been solved using the OpenFOAM software. This software solves partial differential equations using the finite volume method, which by default considers geometry to be three dimensional. In order to solve the two-phase flow, a modified volume of the fluid model (VOF) is applied and the moving mesh model is used for the movement of the container body. In the VOF method, the phases are expressed as a fraction of one (volume fraction). To determine this parameter, based on the continuity equation, a differential equation is regulated and solved. For the turbulent flow model, a modified k-e model is used by considering the effects of free-surface flows. Also, an experimental model of a real moving liquid container has been used for validation of the predictions of the presented simulation. The results show that the experimental and numerical results are in good accordance. In addition, the results show that using vertical baffles up to 50% can reduce the fluctuations caused by this phenomenon.
Volume 20, Issue 4 (Winter 2016)
Abstract
The legal doctrine of subrogation was presented in England and developed in some fields of law. British legal precedent provided this theory based on equity and gradually developed its rules, followed and completed. English Legislator in his statutory decisions, developed or adjusted the theory, and changed its rule in necessity. Since two centuries from presentation of this theory, the English courts began to change the foundations of the theory from rules on the equity to the rules on the unjust enrichment. This shift, gradually, will create some changes in this theory. Although some examples and cases of this theory can be seen in Iran' acts and court decision, but this theory is not accepted as a general theory in Iranian law. The study of principles, criteria and functions of this theory in English law helps us to use this theory according to iranian law.
Volume 21, Issue 2 (Summer 2017)
Abstract
Rapid urbanization and population growth has resulted in increased traffic congestion and consequently air pollution in most major cities, in particular, in the developing countries. Knowledge on the amount of different air pollutants and their spatial and temporal concentrations is of great importance for decision makers on health, environment and air quality estimation in different scales. Mashhad, as a metropolitan, due to its specific religious, socio-cultural and geographical role in the region is declared as one of the most polluted cities of the country. Given that there is a direct relationship between traffic volume data and air pollutants (PM2.5, CO and ), this study attempts to estimate the amount of each pollutant based on traffic volume and some primary weather data. We used empirical models proposed in the literature, such as Baker model and AERMOD, as well as linear regression and nonlinear neural network methods to explore the correlation between traffic volume and air pollutants over a period of six months in the city of Mashhad. The results showed low correlation coefficients between traffic volume and air pollutants in all models, indicating that such models may not be suitable to further estimate air pollutants using only traffic volume and primary weather data. Correlation coefficients were lowest for the pollutant PM2.5 over the time period of the study. Sensitivity analysis demonstrated that vehicle average velocity is by far the most influential variable in the empirical models used.
Volume 23, Issue 1 (Spring 2019)
Abstract
Urban physical growth is affected by different parameters including environmental, neighborhood and socio-economic factors; however, socio-economic variables are often ignored due to the lack of socio-economic information, especially in developing countries, when the urban physical growth analysis and modeling is the aim. Accordingly, there is not many studies conducted to develop GIS-based socio-economic layers to be used along with common data, such as slope, distance to the roads and so on, in urban physical growth modeling. Therefore, this study aims to introduce an efficient method to generate GIS-based socio-economic layers to be exploited along with the information layers extracted from Landsat images and field-collected data for physical growth modeling of Karaj city. After generating the required information layers, random forest feature selection method was applied to select the most important variables. Then, the performance of the three modeling methods including multiple logistic regression, and two artificial neural networks, multi-layer perceptron (MLP) and self-organizing map (SOM) were compared using the selected attributes to model the urban physical growth from 2000 to 2010. The results indicated that SOM with overall accuracy of 84.5%, kappa coefficient of 68.9%, ROC of 90.7%, FOM of 43.98% and PCM of 84.5% performed better than the other methods for modelling of urban physical growth. Moreover, the proposed socio-economic attributes combined with the remote sensing-based data were able to improve the performance of the urban physical growth prediction. Finally, cellular automata was applied to predict the Karaj physical growth in 2017 and 2027.
Volume 25, Issue 4 (Winter 2021)
Abstract
Introduction
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
- All downscaling algorithms used in this research had an acceptable performance in simulating Landsat bands.
- Among the reflectance band-related downscaling methods, STARFM had the best performance, followed by ESTARFM and Regression, respectively.
- Among the thermal band-related downscaling methods, the SADFAT algorithm performed best, followed by Cokriging and Regression.
- The use of STARFM algorithm for reflectance bands and SADFAT algorithm for thermal bands in homogeneous areas is recommended.
- The difference between the different combinations of methods for estimating actual evapotranspiration is small.
Keywords: Downscaling; Landsat-8; MODIS; Evapotranspiration; Cokriging; STARFM
Volume 26, Issue 1 (Spring 2022)
Abstract
Introduction
The strategic city of Mahshahr port is one of the sensitive and key urban spaces of Khuzestan province and the country, which plays a vital role in the export and import of various oil and non-oil materials and goods due to its brilliant historical background, and in political, commercial, security, economic, social, and environmental aspects has a special importance and status. An examination of the historical trend of the location of Mahshahr port shows that the importance of this strategic space is increasing with an increasing speed. Due to the internal correlation of sustainable urban development indicators to each other, the development of Mahshahr port city should be in all its aspects, so that along with economic development, social and environmental development also should be done, because if there is no coordination between the dimensions of sustainable urban development of Mahshahr port, comprehensive development will not happen, and the potentials of sustainable development of Mahshahr port city will not be used optimally. Another issue of sustainable development of Mahshahr port city is that attention is paid only to the port position of this city for economic development, while Mahshahr city has many capacities in other human and natural sectors, especially tourism (natural and human) for sustainable urban development. It should be considered as medium-term and long-term plans. Therefore, explaining the factors affecting the sustainable development of this key port by relying on futures studies can play a significant role in the all-round development of this city. Thus, the main question of the present study is: What are the key drivers of sustainable development in the port city of Mahshahr with a futuristic approach?
Methodology
The present research is applied in terms of purpose and in terms of nature and method is based on new methods of futurology, analytical, and exploratory science. Questionnaire and Delphi technique and documentary and library studies have been used to collect the required data and information. To apply the Delphi technique and analyze the cross-effects, questionnaires have been prepared in two stages. The first stage includes 50-item open questionnaires, in which the most important factors affecting the sustainable development of Mahshahr port city have been provided to experts in the central issues considering different areas and comprehensive sustainable development, which led to the general extraction of factors affecting sustainable development in the port city of Mahshahr. The second stage includes 30-item questionnaires to determine the main factors affecting the sustainable development of Mahshahr port city through weighting, which were completed by the experts and finally used to analyze the collected data from MicMac software.
Results and discussion
Among the 35 main factors affecting the sustainable development of Mahshahr port city, a total of 8 key variables affect the sustainable development of Mahshahr port city. Thus, these variables have the most and least impacts on the future of sustainable development of Mahshahr port city and include the factors of "domestic tourism development (V4), urban development plans (V19), efficient local management (V21), efficient macro management" (V23), education (V24), communication network (V28), security (V30), and geographical location of Mahshahr port (V33) ".
Conclusion
Sustainable urban development is a multifaceted process that is influenced by various economic, socio-cultural, and environmental factors. Given the interdependence of each of these factors success or failure to achieve sustainable urban development depends on the type of planning and institutional capacity of city officials regarding sustainable urban development. Geographical location and human characteristics of Mahshahr port city has provided the necessary infrastructure for the development of urban sustainability in the city. Due to the high impact of the role of management on other factors, adoption of rational policies regarding sustainable urban development by managers and officials of Mahshahr port city, can determine the directions of success or failure of sustainable development of this port city.
The strategic city of Mahshahr port is one of the sensitive and key urban spaces of Khuzestan province and the country, which plays a vital role in the export and import of various oil and non-oil materials and goods due to its brilliant historical background, and in political, commercial, security, economic, social, and environmental aspects has a special importance and status. An examination of the historical trend of the location of Mahshahr port shows that the importance of this strategic space is increasing with an increasing speed. Due to the internal correlation of sustainable urban development indicators to each other, the development of Mahshahr port city should be in all its aspects, so that along with economic development, social and environmental development also should be done, because if there is no coordination between the dimensions of sustainable urban development of Mahshahr port, comprehensive development will not happen, and the potentials of sustainable development of Mahshahr port city will not be used optimally. Another issue of sustainable development of Mahshahr port city is that attention is paid only to the port position of this city for economic development, while Mahshahr city has many capacities in other human and natural sectors, especially tourism (natural and human) for sustainable urban development. It should be considered as medium-term and long-term plans. Therefore, explaining the factors affecting the sustainable development of this key port by relying on futures studies can play a significant role in the all-round development of this city. Thus, the main question of the present study is: What are the key drivers of sustainable development in the port city of Mahshahr with a futuristic approach?
Methodology
The present research is applied in terms of purpose and in terms of nature and method is based on new methods of futurology, analytical, and exploratory science. Questionnaire and Delphi technique and documentary and library studies have been used to collect the required data and information. To apply the Delphi technique and analyze the cross-effects, questionnaires have been prepared in two stages. The first stage includes 50-item open questionnaires, in which the most important factors affecting the sustainable development of Mahshahr port city have been provided to experts in the central issues considering different areas and comprehensive sustainable development, which led to the general extraction of factors affecting sustainable development in the port city of Mahshahr. The second stage includes 30-item questionnaires to determine the main factors affecting the sustainable development of Mahshahr port city through weighting, which were completed by the experts and finally used to analyze the collected data from MicMac software.
Results and discussion
Among the 35 main factors affecting the sustainable development of Mahshahr port city, a total of 8 key variables affect the sustainable development of Mahshahr port city. Thus, these variables have the most and least impacts on the future of sustainable development of Mahshahr port city and include the factors of "domestic tourism development (V4), urban development plans (V19), efficient local management (V21), efficient macro management" (V23), education (V24), communication network (V28), security (V30), and geographical location of Mahshahr port (V33) ".
Conclusion
Sustainable urban development is a multifaceted process that is influenced by various economic, socio-cultural, and environmental factors. Given the interdependence of each of these factors success or failure to achieve sustainable urban development depends on the type of planning and institutional capacity of city officials regarding sustainable urban development. Geographical location and human characteristics of Mahshahr port city has provided the necessary infrastructure for the development of urban sustainability in the city. Due to the high impact of the role of management on other factors, adoption of rational policies regarding sustainable urban development by managers and officials of Mahshahr port city, can determine the directions of success or failure of sustainable development of this port city.
Volume 26, Issue 2 (Summer 2022)
Abstract
Evapotranspiration is one of the most important parameters of the water cycle that its correct estimation is important in water resources management, especially in arid and semi-arid climates. With the development of remote sensing, methods were developed to produce evapotranspiration products using satellite data. In this study, the output of evapotranspiration products including GLEAM, GLDAS, and MOD16A2 was compared with the evapotranspiration of the FAO-Penman-Monteith method During wet, normal and dry years in the Zayandehrood basin. 
, RMSE, BIAS, and IOA statistical indices were used to evaluate the results. Also, Taylor simple skill fusion method was also used to combine the product. The results showed that two products MOD16A2 and GLDAS estimated the rate of reference evapotranspiration compared to the FAO-Penman-Monteith method during the wet, normal and dry years and the GLEAM product less. RMSE values for these products ranged from 37.4 to 47.6, from 136.2 to 141.4, and from 92.8 to 98.7 mm per month, respectively. The error rates of GLEAM and GLDAS were lower in the wet years than in the dry and normal years, while the MOD16A2 product had better performance in the dry years. Also, the results of the products combination showed that the product produced had a better performance than other products in the Basin and in different moisture conditions.
, RMSE, BIAS, and IOA statistical indices were used to evaluate the results. Also, Taylor simple skill fusion method was also used to combine the product. The results showed that two products MOD16A2 and GLDAS estimated the rate of reference evapotranspiration compared to the FAO-Penman-Monteith method during the wet, normal and dry years and the GLEAM product less. RMSE values for these products ranged from 37.4 to 47.6, from 136.2 to 141.4, and from 92.8 to 98.7 mm per month, respectively. The error rates of GLEAM and GLDAS were lower in the wet years than in the dry and normal years, while the MOD16A2 product had better performance in the dry years. Also, the results of the products combination showed that the product produced had a better performance than other products in the Basin and in different moisture conditions.
Modelling of Soil Heavy Metal contamination using Machine learning techniques and spectroscopic data
Volume 26, Issue 4 (Winter 2023)
Abstract
Mines and their related-industries are able to affect their surrounding environment, not only by their activities, but also after being abandoned. Among their different harmful effects, under water and surface water contaminations, and soil contamination can be mentioned. In order to manage these environmental effects, it is necessary to use reasonable methods for modelling heavy metal concentration in soil. This study aims to present a framework for modelling heavy metal soil contamination based on spectroscopy and statistical models. For this purpose, the spectral curves of the 53 soil samples, derived from an abandoned mine and its surrounding areas in New South Wales, Australia, were collected using a spectroradiometer in visible to short wavelength infrared (SWIR) wavelengths. Calculating the second derivative of the collected spectral data, random forest feature selection method (RFFS) was used to determine the most important spectral data for modelling heavy metal concentrations including lead, silver, cadmium and mercury. Then, the modelling techniques including multiple linear regression, random forest regression, and support vector regression (SVR) were applied on the selected spectral data. The results indicated that SWIR wavelengths are the most important spectral data for modelling heavy metal concentrations. Moreover, the non-linear machine learning methods, especially random forest with RMSE of 0.8 ppm and R2 of 0.51 for lead and RMSE of 9.4 ppm and R2 of 0.46 for cadmium performed better than multiple linear regression.
Volume 27, Issue 3 (Fall 2023)
Abstract
The warming of the urban environment is one of the consequences of unsustainable growth. This research aims to investigate the possibility of modeling the effect of the structural parameters on the city’s surface temperature in the summer season in Tehran. For this purpose, the Landsat-8 image taken in 2018 was used to calculate the surface temperature. In order to determine the study units in this research, the segmentation method was used on the Sentinel-2 image of 2018, and the ratio of the vegetation cover and the separation of built-up areas from non-built-up ones were extracted using this image. The multi-layer perceptron neural network and the convolutional neural network methods were used to model the effect of urban structural parameters on the surface temperature during the summer. The results obtained from random forest feature selection for the summer indicates that the presence of vegetation and urban uses that include residential and industrial areas, the presence of mixed residential/commercial/administrative areas, and the presence of vegetation affect changes in the urban surface temperature. Further, the information layers of road and population density in this season have an effect on the changing temperature of the earth's surface. Additionally, the results obtained through modeling and t-test of paired samples demonstrate the superiority of the convolutional neural network method, with a root mean square error of 0.61, determination coefficient of 0.62, and 17.75% estimation error, compared to the multi-layer perceptron model, which had 0.82 root mean square error, 0.26 determination coefficient, and 23.34% estimation error.
Volume 28, Issue 1 (Spring, 2024 2024)
Abstract
The estimation and measurement of precipitation in situ presents considerable challenges due to factors such as exorbitant costs, a scarcity of monitoring stations, point sampling limitations, and its lack of generalizability to broader surface areas. Consequently, it is imperative to evaluate the accuracy of satellite-derived precipitation products as viable alternatives to conventional field measurements. Given that precipitation is influenced by the climatic conditions and physiographic characteristics inherent to specific regions, this study aims to not only validate and verify satellite precipitation products but also to examine the impact of temperature and elevation on the efficacy of MERRA, TRMM, and CHIRPS satellite precipitation products over a monthly scale from 2005 to 2019, utilizing data from 222 synoptic stations located throughout Iran. The findings indicated that the root mean square error for the TRMM, MERRA, and CHIRPS satellites was recorded at 23.8 mm, 30.6 mm, and 35 mm respectively, suggesting a superior performance of the TRMM satellite in comparison to the other two products. Moreover, the results demonstrated that the TRMM satellite consistently outperformed the other two satellites across all temperature and elevation classifications. At elevations below 500 m and above 1500 m, as well as at temperatures less than 18 °C, MERRA exhibited superior performance relative to CHIRPS, offering more accurate estimations of actual precipitation. Overall, the results indicate that TRMM satellite products may serve as a reliable substitute for observational data, as this satellite not only demonstrates commendable performance in the assessment of satellite products but also excels across varying elevation and temperature conditions.
Volume 28, Issue 3 (autumn 2024)
Abstract
The phenomenon of urban shrinkage, recognized as a pervasive global challenge, induces significant alterations in demographic patterns. The primary indicator of urban shrinkage manifests as a decline in the urban population, which is influenced by a myriad of economic, social, environmental, and political factors and catalysts. Shrinkage can transpire at various scales, encompassing national, regional, urban, and rural dimensions, and its implications profoundly affect both the tangible and intangible frameworks of the settlement in question. Presently, in light of the economic and demographic dilemmas confronting Iran, the mitigation of the population growth rate has emerged as a salient concern. Nonetheless, the decline in population is markedly more pronounced in certain locales; such that the population growth rate progressively trends negative, resulting in a diminished capacity of those areas to retain existing residents and attract prospective migrants. Empirical research indicates that Bostan-Abad city has consistently experienced population decline and contraction during the years 1375 to 1395. The current study employed a quantitative approach, analyzing statistical data and land use modifications within the city over these years through the utilization of object-oriented processing techniques. Subsequently, the interrelations among the examined variables were assessed employing the random forest machine learning algorithm. According to the research findings, the contraction of Bostanabad city is intricately linked to rural depopulation, with its rural populace consistently diminishing. This predicament, correlated with an uptick in out-migration from the city and an aging demographic, has precipitated a decline in agricultural activity within the city; if unchecked, it poses the risk of inflicting more severe detriments upon this urban area.
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