Geographic Data
Elham Forootan
Abstract
Extended Abstract
Introduction. In recent years, the population growth, the increase in irrigated land and economic development have caused the increase in the demand for groundwater resources all over the world. In arid and semi-arid regions where surface water does not have a significant amount ...
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Extended Abstract
Introduction. In recent years, the population growth, the increase in irrigated land and economic development have caused the increase in the demand for groundwater resources all over the world. In arid and semi-arid regions where surface water does not have a significant amount due to low rainfall and high evapotranspiration, people lives mainly depend on groundwater. As a result, it is necessary to identify the groundwater potential areas and determine its recharge areas using accurate technologies. So, the aim of this research is to compare the method of multi- influencing factors with the fuzzy method for determining the potential of groundwater in a part of Kebar-Fordo watershed, Qom city, Iran.
Materials & Methods. For this purpose, a part of Kebar-Fordo watershed located in Qom province was selected. Six factors layer, viz. slope, annual rainfall, distance from river, geology, soil, and landuse were considered and classified based on groundwater potential susceptibility in different scales. Multi-influencing factor method can determine the groundwater potential of the region by assigning appropriate weight to different effective factors. In this approach, the layers were combined in Arc-GIS after determining the weight of the layers. In the fuzzy method, the layers of six factors were converted to fuzzy based on the linear function, and then the layers were incorporated using the gamma function. Finally, the statistics of observation points and accuracy index were used in order to evaluate the models,
Results & Discussion. The slope map represents that most part of the studied area (78.56%) has a "0-1" class while "1-3", "3-9" and "9-25" slope classes could be observed in 19.97, 1.29 and 0.18% of the total area, respectively. The soil texture has a significant effect on the infiltration and percolation of the surface water movement towards the groundwater. Therefore, in this research, the soil factor has been investigated as one of the input factors to the models. Soils with high permeability are more suitable for groundwater recharge and vice versa. The soil texture of the area consists of sandy loam, loam, sandy clay loam, and clay loam textures, which cover 3.73, 90.72, 0.23, and 5.32% of the total area, respectively, with a rank of four to one for groundwater potential. In this study, geology map showed that Qft2 formation has the largest area (88.98%) and Plc formation is in the second rank (4.9%). Qft1, Qs.d and Mur units have an area of 2.22, 2.12 and 1.10% and the smallest area belongs to OMq formation (0.68%). Also, different types of land use in the study area were agriculture, garden, rangeland, bareland, and resendential area. The land use map showed that the largest area of this area was ariculture landuse (77.18%), while garden and rangeland covered 0.07 and 6.5% of the total area, respectively. Bareland and residential area comprise 2.94%, 13.31% of the total area, respectively. Among the different landuses, agriculture and residential area have the highest and lowest ranks in groundwater recharge. The rainfall map was categorized with four classes. The classes of 140-156, 156-168, 168-182, and 182-203 mm layers include 14.15, 48.92, 21.84 and 15.09% of the total area with the rank of one to four for groundwater recharge, respectively. The map of distance from the stream was divided into four categories: "0-659", "659-1480", "1480-2675" and "2675-4939" meters, which comprise 46.33%, 34.15%, 15.72% and 3.8% of the total area, respectively. In the method of multi influencing factor, the distance from the stream (8.33%) and the geological factor (25%) were the lowest and highest weights. In this regard, the factors of rainfall, slope, soil, landuse have 20.83%, 16.67%, 16.67% and 12.5% weights, respectively. Then, the groundwater potential map was prepared through overlaying in ArcGIS and the studied area was classified into suitable and unsuitable classes. The suitable class covers 75.15% of the studied area and the unsuitable class covers 24.85% of the total area. In the fuzzy method, the unsuitable class comprises 43.63% and suitable class covers 56.37% of the area. In order to evaluate the models, the statistics of the observation points were applied which the accuracy of the multi- influencing factor and fuzzy models was calculated as 71.42 and 78.57%, respectively.
Conclusion. Preparation of groundwater potential map is necessary to adopt management measures of rainfall storage and groundwater recharge in arid and semi-arid regions and it can be used for sustainable management of groundwater resources. The findings of this research revealed both model's accuracy in the studied area.
Geographic Data
Zahra Moradi; Mohammad Sadi Mesgari
Abstract
Extended Abstract-Introduction: The growing importance of housing is not hidden from anyone in terms of the profound and significant effects it has on the various social, political, and economic dimensions of countries; Therefore, accurate and reliable price estimation definitely facilitates policy-making ...
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Extended Abstract-Introduction: The growing importance of housing is not hidden from anyone in terms of the profound and significant effects it has on the various social, political, and economic dimensions of countries; Therefore, accurate and reliable price estimation definitely facilitates policy-making in this field. Hundreds of factors may affect property prices in different situations as a subset of structural, spatial, and socio-economic factors. Therefore, considering these factors, property pricing should be done efficiently. Due to the complex nature of the real estate market, research has used common deep learning algorithms such as DNN, RNN, CNN, etc., but these algorithms are not very suitable for tabular data. On the other hand, the deep learning models in property pricing are also completely definite and do not take into account data uncertainty.Materials & Method: In this article, we have tried to pay attention to the tabular structure of real estate data in applying deep learning methods. The TabNet deep new architecture is used for this purpose. In addition, at the same time as the learning process, it makes feature selection fully interpretable. In this study, also using existing combination techniques, fuzzy logic is combined with deep learning algorithms to learn complex problems faster and more accurately, to overcome the shortcomings of the certainty of deep learning models and not consider the inherent uncertainty of the data in this models. In this study, using the existing combination techniques, also using spatial information system (GIS) to provide a clearer evaluation to ensure full visualization of the spatial pattern of property properties as well as the relationship between these properties and pricing and spatial variables are included in the valuation model. In order to evaluate the proposed methods, real estate data of District 5 of Tehran were used.Results & Discussion: The order and prioritization of the impact of features on the pricing of Tehran residential properties by the TabNet algorithm indicate the significant impact of spatial factors. So that in this ranking, after the area, the two spatial characteristics of latitude and longitude have the second and third ranks, respectively. Basically, latitude and longitude indicate the criteria of neighborhoods and the type and prestige of different places in the city, and the social class of different streets and neighborhoods in the city, which is clearly a factor in influencing the price. Finally, TabNet, DNN, CNN, RNN, LSTM, Autoencoder algorithms as well as XGBoost machine learning algorithms were used for the Tehran data set, and RMSE, MA and evaluation criteria were compared, which according to the criterion, a 5% improvement in accuracy was achieved by using TabNet. Finally, the RMSE of the FuzzyTabNet hybrid algorithm for Tehran data decreased by 4/65% compared to the basic TabNet algorithm. The fuzzy Autoencoder network also improved by 5/52% compared to the common Autoencoder network.
Mohammad Ghasem Torkashvand; Mostafa Mousapour
Abstract
Extended Abstract
Introduction
The snow cover is one of the quickest changing phenomena on the earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and biogeochemical as well as human activities. Precise estimate ...
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Extended Abstract
Introduction
The snow cover is one of the quickest changing phenomena on the earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and biogeochemical as well as human activities. Precise estimate of snow cover is regarded as one of the fundamental operations in precipitation. Thus, monitoring the snow-covered surfaces hasspecial importancefor the perspective of climatic, ecologic and hydrologic studies. The researchers believe that remote sensing data can lead to better assess from the snow-covered areas than traditional topography methods. Therefore, nowadays, in efficient management of water resources, remote sensing data aims to achieve exact information on snow-covered areasis applying operationally. Satellites are suitable tools to measure the mentioned areas since high snow reflection creates a good contrast with other natural surfaces except clouds. This research is conducted to compare the performance of Cornell functions of support vector and object-oriented Fuzzy operators in estimating the desired areas in Almabolaq Mountain, Asadabad.
Material & Methods
The data used in this research are the bands with 10 m spatial segregation of 2B Sentinel satellite including bands 2, 3, 4 and 8 on 6th March 2020. To classify Cornell functions of support vector machine and compute their accuracy, ENVI software was implemented. The eCognation software was used to partition and categorize those with the same object-oriented Fuzzy operators. Separating similar spectral sets and classifying those with the same spectral behaviour are regarded as satellite information classification. In other words, categorizing the photo pixels, and allocating one pixel to one class or phenomenon are the mentioned classification. Support vector machinethat is one of the most common classifiers in learning machine, which divides data using an optimum separation super plate. One of the important advantages of support vector machine is the ability to deal with high dimensional data using almost less training samples for remote sensing applications. Objective analysis is an advanced technique of image processing which is used to assess the digital images and typical conflicts of basic pixel classification based on different methods. Traditionally, pixel-based analysis can be done by available data of each pixel whereas object-based analysis considers a set of similar pixels called objects or image objects. It regards adjacent pixels with the same information value as one distinct unit called piece or segment. In fact, pieces are the areas produced by one or few homogeneous criteria in one or few dimensions of a specific space, so that the pieces have extra spectral information in each band, mean, maximum and minimum amounts, variance, etc. as compared to single pixels. Combining the object-oriented and Fuzzy methods provides the classification of image pieces with a specific membership degree. In this process, image pieces with different membership degrees are classified in more than one class, so according to the membership degree, image piece classification is done leading to the increased final precision.
Results & Discussion
In this research, after preparing satellite images in SNAP software using Sen2Cor, radiometric correction was conducted on the images. To prepare the classification map of Cornell functions of support vector machine, TIFF satellite images were called by ENVI software. Using the shape file of the case study, the area cutting operation was done. Afterwards, two classes of snow and non-snow regions were created to pick up the training points, so based on imagery processing, training points were specified for each class. To classify support vector machine algorithm, linear, polynomial, radialand sigmoid Cornell functions were applied,soclassification maps were separately produced. To draw the classification map of object-oriented Fuzzy operators, satellite images pre-processed in previous stages were called by eCognation software, then they were defined as a project. Afterwards, two mentioned classes were defined to do the classification process, for each class, the desired Fuzzy operator was determined. For suitable classification, it was done in various scales and weight coefficients of shape and compactness. Scale 75, shape 6.0 and compactness 8.0 presented suitable classification. The training samples, parameters of lighting, mean and standard deviations were chosen as distinct features of classes for object-oriented classification. Using the nearest adjacent neighbor algorithm, object-oriented classification was done for each of the Fuzzy operators. After drawing the snow-covered areas through Cornell functions of support vector machine and object-oriented fuzzy operators, the accuracy of classification was computed.
Conclusion
The results indicate that AND algorithm showing the logic share and minimum return value out of Fuzzy values is the highest accuracy (98%) and to classify digital images,the object-oriented processing methods of satellite imagery enable more precision due to the data related to texture, shape, position, content and geometrical features as compared to Cornell functions of support vector machine.
