Mapping forest fires by nonparametric clustering analysis

Fires have a noteworthy role to play with regards to ecological and environmental losses in Mediterranean forests. In addition to ecological impacts, fire may create economic, social as well as cultural changes. The detection of fire-scars has critical importance to help decrease losses.In the present study, forest fires recorded in Antalya, one of the most important ecological and tourist regions within the Western Mediterranean, were clustered and mapped. Since the dominant factors and devastation records derived from the cases had nominal-scaled properties, a categorical databased nonparametric clustering algorithm was performed in this evaluation. The proposed tool, k-modes algorithm,uses modes instead of means for clustering. The algorithm may be implemented quickly and does not make distributional assumptions concerning the available data. It uses a frequency-based method to update the modes of the fires.The derived modes from the maps may be useful information for local authorities to manage. In conclusion, the proposed nonparametric clustering procedure may be employed to build a decision-support system to monitor and identify fire activities and to enhance fire management efficiency.

An overview of Turkish lignite qualities by logistic analysis

As an indispensable energy source, lignite is almost exclusively used in power generation m TurKey, To assess me quality level of Turkish lignite, a multivariate statistical analysis was conducted. The relationship among the lignite quality parameters has been investigated using a response method that is the logistic regression method. The analysis determines the effect of multiple predictor variables such as moisture, ash and sulphur presented simultaneously to predict membership of the two calorific value categories. By this way, a reliable binary response regression structure was constructed considering all the lignite fields in Turkey. Both the experiments on identifying the influential measurements and the measure of goodness of fit indicated that the overall model has a big capability to exhibit the relationship among the parameters of the Turkish lignite.

Learning distance effect on lignite quality variables at global and local scales

Determining scale and variable effects have critical importance in developing an energy resource policy.This study aims to explore the relationships in heterogeneous lignite sites using different scale models,spatial weighting as well as error-based pair-wise identification.From a statistical learning framework,the relationships among the quality variables such as geochemical variables and the contributions of the coordinates to quality measures have been exhibited by generalized additive models.In this way,the critical roles of spatial weights provided by the coordinates have been specified at a global scale.The experimental studies reveal that incorporating the geological weighting in the models as the additional information improves both accuracy and transparency.Because relationships among lignite quality variables and sampling locations are spatially non-stationary,the local structure and interdependencies among the variables were analyzed by geographically weighting regression.The local analyses including spatial patterns of bandwidths,search domains as well as residual-based areal dependencies provided not only the critical zones but also availability of pair-wise model alternatives by calibrating a model at each point for location-specific parameter learning.The results completely show that the weighting models applied at different scales can take spatial heterogeneity into consideration and these abilities provide some meta-data and specific information using in sustainable energy planning.

Lithological classification of cement quarry using discriminant algorithms

As such in any industrial raw material site characterization study, making a lithological evaluation for cement raw materials includes a description of physical characteristics as well as grain size and chemical composition. For providing the cement components in accordance with the specifications required, making the classification of the cement raw material pit is needed. To make this identification in a spatial system at a quarry stage, the supervised pattern recognition analysis has been performed. By using four discriminant analysis algorithms, lithological classifications at three levels, which are with limestone, marly-limestone (calcareous marl) and marl, have been made based on the main chemical components such as calcium oxide (CaO), alumina (Al2O3), silica (SiO2), and iron (Fe2O3). The results show that discriminant algorithms can be used as strong classifiers in cement quarry identification. It has also recorded that the conditional and mixed classifiers perform better than the conventional discriminant algorithms.