The land use and land cover (LULC) classification has great potential to contribute to the monitoring of land degradation and climatic disasters. The purpose of this study was to assess the performance of parametric and nonparametric classification methods using remotely sensed Landsat satellite data of arid and semiarid areas, based on the computed producer's accuracy, user's accuracy, overall accuracy, and Cohen's kappa coefficient. Three LULC classes were identified, and supervised classifications were applied to Landsat 8 imagery. The results show that the support vector machines (SVM) classification method produced more accurate results, using two different kernel functions, compared with the maximum likelihood classification (MLC) and the minimum distance classification (MDC). The basis radial function affords the highest overall classification accuracy of 91.20% and a mean kappa coefficient of 0.87. This classification method is very well suited to accurately map LULC in arid and semiarid regions where the main vegetation type is oasis or steppes.
The scarcity of rainfall data is one of the main problems affecting the use of hydrological models. Several model satellite-based rainfall estimates (SREs) have been developed to provide an alternative to poorly or ungauged basins. The aim of this work was to evaluate the suitability of SREs for hydrological modeling using a semi-distributed model in the transboundary basin of Medjerda, shared by Tunisia and Algeria. Two satellite-based rainfall products (PERSIANN-CDR and CHIRPSv2) were first compared to rain gauge observations based on sub-basin and point-to-pixel analysis. The selected SREs products were then used as inputs to simulate discharge at a daily time-step over the 1996-2016 period. The simulated streamflows were compared to data measured at four runoff gauging stations and at the outlet of two dams. It was first shown that both SRE products perform weakly at daily scale but that the CHIRPSv2 product performs better at monthly scale. Second, comparison at sub-basin scale led to a better correlation with rain gauge observations than point-to-pixel analysis. Third, direct sampling can be reliably used to fill gaps in discharge time series by using auxiliary stations highly correlated with the target station. Finally, the CHIPRSv2 daily satellite rainfall product is more efficient and more suitable than the PERSIANN-CDR product for hydrological modeling. Thus, CHIRPSv2 can be used as an alternative or as a complementary source of information to simulate hydrological models in arid and semi-arid regions and can successfully solve the issue of missing rainfall data in transboundary catchments.
In today’s competitive business environments, organizations increasingly need to model and deploy flexible and cost effective business processes. In this context, configurable process models are used to offer flexibility by representing process variants in a generic manner. Hence, the behavior of similar variants is grouped in a single model holding configurable elements. Such elements are then customized and configured depending on specific needs. However, the decision to configure an element may be incorrect leading to critical behavioral errors. Recently, process configuration has been extended to include Cloud resources allocation, to meet the need of business scalability by allowing access to on-demand IT resources. In this work, we propose a formal model based on propositional satisfiability formula allowing to find correct elements configuration including resources allocation ones. In addition, we propose to select optimal con- figurations based on Cloud resources cost. This approach allows to provide the designers with correct and cost-effective configuration decisions.
The growing interest in cloud-based data warehousing is driven by the high return on investment. Nonetheless, the adoption of cloud computing for data warehousing faces security challenges given the proprietary nature of the enclosed data. This paper first, presents an adaptive security solution for sensitive data in cloud-based data warehouses. Second, it illustrates how the security solution can be implemented as a service through a model-driven approach.
Cloud computing offers generous computing resources needed to deploy data warehouses efficiently. However, security remains a key challenge for a widespread adoption of the Cloud for data warehousing. In this paper, we extend BPMN to provide for modelling secure Extract-Transform-Load processes deployed as web services in a Cloud environment. In addition, following a model-driven engineering approach, we outline how the ETL model can be transformed into a set of services to be deployed in the Cloud.
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