Data Mining Approach in Climate Classification and Climate Network Construction – Case Study Montenegro

Abstract

Abstract:In this study, we present results of applying data mining techniques on meteorological dataset obtained from the Institute of Hydrometeorology and Seismology of Montenegro. The dataset covers the measurements taken from all 11 main meteorological stations in Montenegro for the period 2010-2015. We build new climate classification system based on decision tree. The system is simpler (i.e. uses fewer attributes) and more accurate than the well-known Köppen climate classification system. In addition, we propose a novel procedure for climate network construction. Finally, we identify the regions within the same climate type in Montenegro's climate network with the Girvan-Newman algorithm for community detection and achieve better results with respect to classical K-means and hierarchical clustering algorithms.

The Paštrovska House of the Montenegrin Coastal Area: Example of Sustainable Building in Traditional Architecture

“…In every vernacular architecture, there are specificities which are conditioned by the location, climate, materials available for construction, economic potential, historical circumstances and geographic position, all of which give rise to differences, i.e. to the creation of regional architectures [3][4][5][6].…”