Predicting the decline of forest road pavement during the logging ban period in the north mountain forests of Iran

Document Type : Research Paper


1 Forestry and Forest Economics Department, Faculty of Natural Resources , University of Tehran, Karaj, Iran

2 Deptartment of Forestry , Faculty of Natural Resources and Marine Science, Tarbiat Modarres University, Noor, Iran


The lack of allocation of sufficient funds after the implementation of the forest rest law, the restoration and maintenance of forest roads has disrupted the forestry projects and caused them to deteriorate. The purpose of this study is to predict forest road deterioration during the 10-year period of implementation of forest rest law using hidden Markov chain model and evaluate its effect on the cost of road repair and maintenance. In this research, along the 11.6 km forest road network series 3 of Aghuzchal (Nowshahr) in the educational-research forest of Tarbiat Modares University, according to the road condition information using PCI and FRPCI pavement indicators in a consecutive period of 3 years (2016-2019) the investigation and road deterioration process was predicted by the hidden Markov chain model in a 10-year period. The results showed that the process of deterioration of the road from excellent to good is much faster than the process of its changes from medium to poor. The highest percentage of road sections are broken and the lowest number are sections in excellent condition. It is predicted that due to the implementation of the breathing plan and the lack of budget allocation during a 10-year period, the road will go down a steep slope to complete deterioration. The results emphasize the need to accelerate the allocation of funds for the repair and maintenance of the existing road, and the Markov chain performance prediction model with an RMSE error of 0.01 has the necessary capabilities to accurately predict the actual road deterioration process. The use of other prediction and evaluation models in more time periods will bring more accurate results.


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