جستجوی مقالات مرتبط با کلیدواژه « مدل ترکیبی » در نشریات گروه « جغرافیا »

According to the fact that place identity is influenced by various social, physical, semantic, functional, and natural factors, each of these dimensions has multiple indicators with complex internal relationships; Measuring and evaluating it is a fundamental and complex issue. Therefore, the aim of this study is to "provide a combined model for measuring the identity of place in urban spaces." To achieve this goal, quantitative methods have been used. First, the expert questionnaire with a sample size of 19 people was analyzed in the form of a combination of DEMATEL model and Analytical network process, then the questionnaire of space users with a volume of 384 samples in 12 urban spaces was analyzed by TOPSIS model. The results show that despite the decline in identity in recent decades, Tehran Bazaar as the central core of Tehran's historical district still has an acceptable level, but the further away from the Bazaar, the different dimensions of social, physical, functional, natural and the semantic identity is declining. Also, from a methodological point of view, it can be said that the combined DANP-TOPSIS model is a suitable model for measuring the various dimensions of place identity in an integrated way.

With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure. With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure.In order to improve the classification accuracy, the feature extraction approach through the designed network and the classification by the Extreme Gradient Boosting was compared with the classification method by the global deep network. The proposed capsule approach consists of 3 basic layers: 1) Prime caps, which are capsules of size 8 and 32 with 9 × 9 filters and movement step 2, 2) Digitcaps with 10 16-dimensional capsules, and 3) fully connected layer. The results of examining two approaches for deep networking as well as combining capsule networks with XGBoost reinforcement tree algorithm were compared. Approaches such as SVM, RF-200, LSTM, GRU and GRU-Pretanh were considered to compare the proposed approach based on the configurations mentioned in their research.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined. The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined.The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.

Flood is a disaster which causes a lot of economic damages to farmlands, forests, gas and power transmission lines, roads, engineering structures, and buildings. There are numerous floods in the northwest of country at the beginning of spring and the start of spring rains, which in most cases results in heavy damage [4]. The aim of the present study was to prepare a map of spatial variations in flood risk susceptibility in the Aland Chai basin located in West Azerbaijan province and Khoy city. To achieve this aim, 13 effective parameters in the occurrence of this phenomenon have been used. These parameters include lithology, soil hydrological groups, NDVI, land use, slope, aspect, elevation, distance to river, river density, precipitation, topographic wetness index, stream power index, and sediment transport index.Study areaAland Chai basin is located between 38°- 30¢-14² and 38°- 48¢-22² N and between 44°- 15¢- 13² and 45°- 01¢-02² E in the Northwest of Iran and the Western Azerbaijan province. This basin is one of the sub-basins of the Aras River basin to which surface water flows after joining the grand Qotour River. Basin elevation variations are from 1093m in the Aland Chai River bed to 3638m above sea level in the Avrin Mountain [4].

The following data, software, and methods were used to analysis flood risk susceptibility and prepare flood risk maps in the study area:- A frame of Landsat 8 satellite image OLI scanner with path of 169 and row of 33, in 30m spatial resolution- Geological maps in 1:100000 and 1:250000 scale from Khoy and Dizaj- Topographic map in 1:50000 scale from Khoy city- Digital Elevation Model (DEM) in 12.5m spatial resolution- ArcGIS software to generate maps- ENVI software for land use mapping- WEKA software to data miningThe new ensemble model of FURIA-GA-AdaBoost have been used to investigate the role of parameters in the occurrence of floods. FURIA is a fuzzy rule-based classification method, an extension of the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) rule learner (Cohen, 1995), introduced by Hühn and Hüllermeier (2009) [1, 3]. AdaBoost is a machine learning algorithm introduced by Freund and Schapire in 1997 [2].

To implementation the FURIA-GA algorithm, the following characteristics were obtained after trial and error. For the GA, crossover probability was set to 0.2, mutation probability was set to 0.035, population size at 250, and the number of generations set to 50. For the FURIA evaluator, a 10-fold cross-validation technique with T-Norm product as a fuzzy aggregation operator was trained to combine rule antecedents. The results showed that the FURIA-GA classification with 86.45% was very accurate. The following settings are used to run the AdaBoost algorithm: batchsize, 100; number of iterations, 12; seed, 1. Decision tree C4.5 was also selected as the base classifier. WEKA software was used to perform these algorithms.

The present study was an attempt to investigate the susceptibility of flood risk in the Aland chai basin. Therefore, 13 effective parameters in flood occurrence were used to prepare a flood risk susceptibility map. ArcGIS and ENVI software were used to prepare each of the information layers. In order to perform the relevant analyzes on each of the parameters, the new ensemble model FURIA-GA-AdaBoost in WEKA software was used. The results of these studies showed that slope, soil hydrological groups, altitude, vegetation, and land use have an important role in the occurrence of floods in the area. Flood risk susceptibility map was prepared in 5 classes of very low, low, medium, high, and very high susceptibility. The results showed that the areas that are highly susceptible in terms of flood risk are mainly concentrated downstream of the basin, which includes flat and low areas. Generally, 26% of the total area of the Aland Chai basin is located in high and very high risk for floods.

Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.

Soil erosion and sediment yield are one of the most destructive phenomena that was cause a lot of damages in different regions. In order to combat  with increasing sediment loads, informed of the nature and sediment sources in watershed scale is essential. Soil conservation projects and for effective sediment control strategies, need to knowledge of sediment sources throughout a catchment environment. The main aims of this study are to apply tracers to determine relative contributions of sediment sources with six mixing models and to use Monte Carlo -mixing model that estimates contributions of sources to associated the uncertainty estimation in sediment fingerprinting in the land uses(rangelands, forests, cultivation and gardens)and geology information(Razak, Kashkan, Bakhtyari, Quaternary, Pabedeh Gorpi and Asmary) of  Tange Bostanak catchment, in Fars province. Multivariate discriminant function analysis was subsequently used to identify the best composite fingerprint incorporating a number of these tracers. Four tracers(C, Cu, Si, Ti) verify  ability to discriminate between land use  source categories and four tracers(Nd143/144, Cu, Si, Ti) verify  ability to discriminate between geology information’s  source categories. The application of six commonly used mixing models(Collins, MCollins, Landwehr, MLandwehr, Motha, Slattery) to Monte Carlo Simulation identified all outputs remained in the acceptable range of contributions  defined by the Monte Carlo uncertainties. Mean relative contribution (percentile 5%, percentile 50% and percentile 95%, respectively) of sediment samples resulting from the Monte Carlo mixing model uncertainty approach. The best explanatory parameters provided by the Collins model based on GOF index(99.3)was best model for land uses and the best explanatory parameters provided by the Modified Collins model based on GOF index(99.98)was best model for geology information. The relative contributions from areas of range lands with 58.76%(42.5%-75.65%)  and Asmary inrormation with 53.63%(46.7%- 60.11%) are highest, respectively. Pabedeh Gorpi and Bakhtyari information’s with 0.24 and 0.27  are the lowest relative importance, also gardens  and range lands  with 3.73 and 3.5  are the highest  relative importance, respectively.

Spatial balance through spatial planning is possible. Spatial planning means planning for the site over time. Territorial planning is the latest and most complete spatial planning, Experience in various countries shows that the aim of spatial and Territorial planning often with reduced spatial inequalities, balance growth and participation in national affairs. This study aimed to investigate the spatial imbalance in the development of various aspects of social, economic, environmental and infrastructure in the Khorasan Razavi province. In Iran in 1345, the issue of Territorial planning at the Institute for Social Research of Tehran University was introduced. In this province, the study of Territorial planning, Interim Comprehensive Plan Development Khorasan" in 1347 which was followed by CETCOPE. This study is the first study in the province before the Islamic revolution in Iran. The final result, the province is divided into twelve geographic areas that studies published in 24 volumes. This paper explains the spatial imbalance in the Khorasan Razavi province .Based on this conceptual model, using 47 indicators in seven field, consist of human development, quality of manpower, economic, social, cultural, environmental facilities and services in the Khorasan Razavi province was made and Spatial Multi Criteria Decision Models to action on province level variables were considered. The development of composite index was calculated. Then the cities were clustered using Pearson index calculation is based on the assessment ward in Minitab software. The results showed that the existing imbalances in the various areas, spatial clustering and imbalance in the development of homogeneous regions in the province. Mashhad, with a considerable distance from the other cities of the province, is ranked first. Combination indices indicated imbalances even within the cluster value. Understanding the capabilities and potential of cities can be developed to meet internal and external imbalances and regulation of homogeneous clusters of cities, used to be the province of Town Planners