Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has bec...
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Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world's population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant's growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer's Accuracy (PA), User's Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of "rural to urban transition" and socioeconomic development with
This research was conducted to study assemblages of selected ground-living arachnids of the chemical waste dump in Vrakuna (Bratislava, Slovakia). Arachnids were sampled between October 2018 and October 2019 at three ...
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This research was conducted to study assemblages of selected ground-living arachnids of the chemical waste dump in Vrakuna (Bratislava, Slovakia). Arachnids were sampled between October 2018 and October 2019 at three study sites using pitfall traps. In total 3,261 spider individuals, belonging to 92 species and to 24 families, and 1,284 harvestman individuals, belonging to eight species and to four families, were captured. The identified spider species were divided into five groups according to their different habitat type preference and on the basis of association with the originality of habitats and their expansion/invasion potential. The studied assemblages of arachnids were compared according to their composition and analyse the variation of species occurrence across the study sites was done. In order to evaluate the relationship between the assemblages of the spiders and harvestmen at the sampling sites, we used multivariate analysis. The biplot of correspondence analysis formed groups of species with different preferences for humidity and light conditions at individual study sites. The research found the species Zodarion italicum, recorded for the first time in Slovakia. The characteristic features of the spider species found, photos of habitus and genitalia, notes on phenology, habitat, an overview of the presently known distribution, and the dominant species of spider assemblages are here presented.
Missing values are ubiquitous in energy datasets, and therefore, generative-based imputation networks have attracted extensive research interest because of their strong imputation performance. However, these networks ...
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Missing values are ubiquitous in energy datasets, and therefore, generative-based imputation networks have attracted extensive research interest because of their strong imputation performance. However, these networks have limited accuracy when explicit class labels are unavailable and single imputation cannot fully address the uncertainty surrounding the true values of the imputed variables. This article proposes an unsupervised data-mining-based conditional generative adversarial multiple imputation network that exploits implicit categorical information and multiple imputation to improve the robustness of the final imputation results. First, a pretraining algorithm is added to develop an auxiliary classifier combined with the corresponding implicit class labels. Then, a "fuzzy-clustering-based ordering points to identify the clustering structure" algorithm is proposed to learn the implicit categorical information. Thereafter, multiple imputation is applied to an original energy dataset to improve the reliability of the final imputation results. Experimental results demonstrate the superiority of the proposed network compared to other networks.
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