Assessment of national parks affected by naturbanization processes in Southern Europe

This paper assesses land-use changes related to naturbanization processes on three biosphere reserves in Southern Europe. A comparative analysis has been done on the National Parks in Peneda-Ger^es in North Portugal, C_evennes in South France and Sierra Nevada in South Spain, using Corine Land Cover data from 1990 until 2006. Results indicate that the process of land-use intensification is taking place in the frame of naturbanization dynamics that could jeopardize the role of Protected Areas. Focusing on the trends faced by National Parks and their surrounding territories, the analysis demonstrates, both in quantitative and spatial terms, the intensification processes of land-use changes and how it is important to know them for coping with increasing threats. The article concludes that in the current context of increasing stresses, a broader focus on nature protection, encompassing the wider countryside, is needed if the initiatives for biodiversity protection are to be effective.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks