Modeling of temporal and spatial variation modeling with artificial neural networks: Kastamonu sample

It is very important to identify and use the most appropriate methods to achieve the objectives of the interpretation of the limited natural resources management and the relations involved in the environment and to achieve high-quality information, high speed and short time result by effective use of technology. Remote sensing techniques are in a position that is considered very effective in this respect. Obtaining information about the various parameters without touching the objects provides both time and cost advantages. For this reason, remote sensing is frequently used techniques. Remote sensing technologies use many different areas and are widely used by decision makers and/or practitioners in the construction of thematic maps in planning studies. One of the most important applications of these technologies is the monitoring of urban development with the help of satellite images. Detailed determination of urban land use is important for decision makers, planners, practitioners and researchers. In this study, the land use and land cover changes between 1999 and 2016 of the center of Kastamonu and county seat were investigated and land use and change groups were created. The classified satellite imagery is estimated by modelling the potential land use and change of the study area in 2033 with the Artificial Neural Networks (ANN) approach. According to the study area, as of 1999, the spatial distribution was 49,5% forested areas, 1,1% water areas, 33,2% agricultural areas and 16,2% built-up areas. As of 2016, regional allocations were 41.7% forested areas, 11.9% water areas, 19.2% agricultural areas and 27.2% built-up areas. The change between 1999 and 2016; 7.8% decrease for forested areas, 10.8% increase for water areas, 13.9% decrease for agricultural areas and 10.9% increase for areas with built-up areas.