|
|
|
 |
Search published articles |
 |
|
Showing 4 results for Soleimani
Mr. Sina Abolhoseini, Dr. Saadi Mohammad Mesgari, Mr. Reza Mohammadi Soleimani,
Volume 8, Issue 3 (1-2021)
Abstract
Nowadays, traffic congestion is a big problem in metropolises all around the world. Traffic problems rise with the rise of population and slow growth of urban transportation systems. Car accidents or population concentration in particular places due to urban events can cause traffic congestions. Such traffic problems require the direct involvement of the traffic police, and it is urgent for them to be present at the scene as soon as possible. Due to the shortage of space, constructing traffic police centers in all areas is not possible. As a result, building traffic police kiosks with limited number of personnel and small cabins is a solution to solve this problem. Finding suitable places to build kiosks is a location optimization problem that can be solved by geospatial analyses. Artificial intelligent algorithms are suitable approaches to solve such problems. Particle Swarm Optimization (PSO) algorithm proved to be a fast and exact algorithm in solving continuous space problems. However, this algorithm cannot be used for discrete space problems without any modifications. In this paper, we modified PSO to solve problems in combinatorial space. Crossover and mutation operators from Genetic Algorithm were used to modify the behavior of particles. After conducting experiments on a part of Tehran’s transportation network, results were compared to the results of Artificial Bee Colony algorithm. In experiments with 2 and 4 kiosks, both algorithms are performing the same in accuracy, stability, convergence trend, and computation time. But in experiments with 10 kiosks on a bigger environment, results are in favor of the modified PSO algorithm in obtaining the optimum value; stability and better distribution in the area of interest. Results indicate that the proposed algorithm, is capable of solving combinatorial problems in a fast and accurate manner.
Ramin Papi, Dr. Meysam Argany, Shahab Moradipour, Masoud Soleimani,
Volume 8, Issue 3 (1-2021)
Abstract
Sand and Dust Storms (SDS) are known as one of the most common environmental problems in arid and semi-arid regions of the world. This phenomenon is harmful to human health as well as to economy. Over the past two decades, SDS have been increasing on a local, regional and even global scale. The Euphrates Basin is recognized as one of the most active SDS sources in the world. The first step in managing this environmental phenomenon, is to identify dust storm sources. The aim of this study is mapping the potential sources of SDS in the Euphrates basin by using Multi-Layer Perceptron Neural Network. In the first step, the long-term time series of which is data, related to key environmental parameters affecting the occurrence of SDS including: soil moisture, soil temperature, soil texture, land surface temperature, wind speed, precipitation, evapotranspiration, dusty months, land use population, pressure, the identified elevation and slope were used as artificial neural network model inputs. Using the visual interpretation of 2500 MODIS images in natural color composite, 190 SDS centers were identified visually and introduced to the neural network as training points. 70% of the points (133 points) and 30% of them (57 points) were used for training, testing and validation of model, respectively. After running the model, the estimated mean squared error (MSE) was equal to 0.1, which indicats acceptable accuracy of the neural network model in mapping the potential SDS sources. The results show that, 147000 km2 of the basin is prone to the formation of SDS sources, which mainly include low rainfall, dry and barren areas of the basin.
Mr Masoud Soleimani, Dr. Sara Attarchi, Ms Narjes Mahmoody-Vanolya, Ms Farimah Bakhshizadeh, Mr Hamed Ahmadi,
Volume 9, Issue 3 (12-2021)
Abstract
In this study, the capabilities of Interferometric Synthetic Aperture Radar (InSAR) time series data and machine learning have been evaluated for land cover mapping in Iran. In this way, a time series of Sentinel-1 SAR data (including 16 SLC images with approximately 24 days time interval) from 2018 to 2020 were used for a region of Ahvaz County located in Khuzestan province. Using InSAR processing, 25 coherence images were obtained based on different SAR pairs. Five dominant land cover classes in the region including built-up lands, agricultural lands, water bodies, bare soil, and dense natural vegetation cover were identified and considered. Through Google Earth's high-resolution imagery, a total of 4,930 ground truth samples with appropriate spatial distribution were acquired for all classes. The obtained multi-temporal coherence images were used as input variables to the support vector machine (SVM) classifier. The training and validation process of different SVM kernels was performed using 80% and 20% of the ground truth samples, respectively. Overall accuracy in different kernels including linear, 2th-degree polynomial, 4th-degree polynomial, 6th-degree polynomial, radial base function (RBF), and sigmoid were computed 60.7, 64.7, 67.7, 69.9, 66.3, and 59.5%, respectively. Likewise, Kappa coefficients were reported 50. 8, 55.87, 59.62, 62.38, 57.87, and 49.38%, respectively. Accordingly, the highest and lowest overall accuracy and Kappa coefficient were belong to the 6th-degree polynomial and sigmoid kernels, respectively. Based on the user and producer accuracy assessments in all kernels, the built-up lands has the highest accuracy (93%–up to 98.5%), and in opposite the dense vegetation has the lowest accuracy (11%–up to 56.25%). Generally, the results emphasize the high potential of Sentinel-1 InSAR coherence data in land cover mapping. Meanwhile, the contribution of the classifier to the efficiency of data is also important.
Somayeh Ebrahimzadeh, Masoud Soleimani, Sara Atarchi, Mehdi Saadat Novin, Seyed Hassan Shabanian,
Volume 11, Issue 3 (12-2023)
Abstract
Abstract
Soil erosion has devastating and irreversible consequences for human life. Hence, supportive measures are necessary to reduce and control soil erosion in the most affected areas. Achieving this goal requires detecting severely soil-eroded areas (SSEA), because it is not possible to implement supportive measures throughout the area. Detection of SSEA using field-based methods is very difficult, costly, and faces various limitations. To deal with it, taking advantage of remote sensing data capabilities has been widely attention today. The interaction of the radar signal with the surface roughness changes can be evaluated through Interferometric Synthetic Aperture Radar (InSAR) coherence changes. In fact, soil erosion causes the movement of soil particles and decreases InSAR coherence. Accordingly, the aim of this study is to detect SSEA in Khuzestan province as one of the areas with high soil erosion rates using a processed time series of Sentinel-1 InSAR coherence from 2018 to 2020. The map of SSEA was obtained by detecting and excluding other effective factors causing InSAR coherence reduction, such as water, vegetation, and topography. Validation of the results based on comparison with the valid soil erosion map of the study area revealed that 86% of SSEA detected by the proposed method are consistent with the ground reality. Also, the compatibility of SSEA with the genus and resistance of different geological formations in the region emphasizes the validity of the results.
|
|
