اکبر نجفی

Road construction projects involve among projects with a broad ranges of wide range of positive and negative effects on the economy, society and the environment leave. Environmental Impact Assessment is one of the ways to achieve positive and negative results from such projects. The purpose of this study is to evaluate and present environmental strategies in relation to forest roads of district 1 and 2 of Lavij forestry project located in Mazandaran province.

In this study, SWOT method was used to environmental impact assessment the before, during and after of the forest roads construction. For this purpose, a questionnaire was prepared based on internal factors (strengths and weaknesses) and external factors (opportunities and threats). The validity of these questionnaires was confirmed by professors and experts in the relevant field and was filled by 15 experts in the field of forestry and forest engineering to obtain appropriate results.

The results of research showed that the invasive strategy is the best possible strategy for environmental management of the region in all three stages. In addition, for the environmental management of the region, three strategies: diversifying the plant and animal species and tourist attractions, creating employment for local communities and increasing the income of forest dwellers and neighboring communities, and finally providing healthy and active life for local communities as the most important strategies in these three phases were introduced.

The present study, taking into account the strengths, weaknesses, opportunities and threats in the region, tried to present strategies that best turn weaknesses into strengths and threats into opportunities, which will be possible by providing invasive strategy. Also in this study it was found that the forest roads construction have a significant impact on the economy and social of local communities so that Creating employment, increasing income, development infrastructures, improve education, enhancement participation, prevent migration and raise awareness of local communities, including the positive effects of forest roads construction.

The surface layer of the road suffers various damages with the passage of time due to traffic and atmospheric factors, and their quality decreases. Therefore, quick and accurate extraction of layer-surface anomaly is very important for effective monitoring of road health status. In order to improve the efficiency of surface layer inspection, today drones are used as a useful tool to obtain reliable information in the field of road surface layer.

This research was conducted with the aim of revealing the top layer damage of forest roads using UAV images and image processing techniques on a 3.6 km long road in Dr. Bahramnia educational and research forest, Golestan province. The images obtained from the UAV were prepared using photogrammetric processing calculations and orthomosaic image and digital model of the ground height, then the resulting images were used to identify and check the top layer pits using three supervised learning algorithms: nearest neighbor, K-nearest neighbor And the supporting machine was examined and evaluated.

The results showed that the orthomosaic image obtained from photogrammetric calculations has high accuracy. Also, checking the accuracy of the algorithms used to classify and identify potholes showed that these algorithms have a good ability to identify the damage of the road surface layer. Nearest Neighbor, K-Nearest Neighbor, and Support Vector Machine estimated the top layer failures with accuracy of 92.04, 94.31, and 96.59 percent, respectively.

The support vector machine algorithm as a supervised learning algorithm with 96.59 percent accuracy had the highest classification accuracy compared to the other two algorithms and as a suitable method for classifying and identifying failures in this The research was introduced. The resulting UAV images and supervised learning algorithms can be used to identify the abnormality of forest roads including potholes.

One of the components of watershed water balance, which is very important in watershed management and water resources management, is runoff. Appropriate estimation of runoff value requires determining Runoff Coefficient (RC). This study was conducted to evaluate the effectiveness of multi-criteria decision-making methods in order to estimate the RC in Amameh watershed, Iran. To do this, at first, slope angle, vegetation (land use), hydrologic soil groups, maximum daily rainfall and area of the study area layers was entered into geographic information system (GIS). After performing the necessary processing on the layers, it were converted to raster formats based on the study area boundary. In the next step, the analytical hierarchy process (AHP) structure was established based on the research purpose. The weighted index values for each layer and their different classes were then determined based on the weighted index of the AHP by Expert Choice software. Based on these five criteria, three models were made. Therefore in model 1, slope angle, vegetation (land use) and  hydrologic soil groups; in model 2, maximum daily rainfall, area of the study area and hydrologic soil groups and in model 3,  soil infiltration and area of the study area were used. The estimated RCs were then estimated based on weight for each criteria in each model. The estimated RC with the observed RC, which have been measured using the Kamphorst rainfall simulator at 60 points in different land uses with an intensity of 60 mm hr-1 for 90 min, were compared. The obtained results showed that the second model with Nash-Sutcliffe Efficiency (NSE) coefficient of 0.59 and root mean squares error (RMSE) of 0.363 had a better efficiency than the other two models. In general, the results showed that the AHP method due to its simplicity, the application of qualitative and quantitative criteria simultaneously and the ability to assess compatibility in judgments can be used in the study of RC.

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.

With the decline of forest resources and the policy of forest logging ban in the north forest of our country, it is essential to find alternative sources to supply raw materials for the wood industry. On the other hand, finding a proper way to recover the remains of pruning in addition to the economic benefits to the environment is also very useful. The aim of this study was to evaluate the feasibility of using chips from annual pruning of grape trees in industry.

In this study, woody utilization of grape trees pruning residues was investigated by single-pass method and two-pass method. Continuous timing was used to identify and evaluate methods and calculate production and cost. The chips were transferred from the sources to the mills with trailers, indoor trucks, and vans with a capacity of 24, 16, and 3 tons, respectively. Then, the prediction modeling of the utilization time of the residuals was performed using multiple linear regressions.

The results showed that the amount of pruning residue of grape trees was 1.12 ton/ha and production cost varied from 986 to 1238 thousand Rial per ton, based on processing method. The rate of production per ton of wood chips per hour in two-pass method was higher. Also, the total cost in the two-pass method was lower than the single-pass method. The maximum profit (2263 thousand Rial per ton) was related to the two-pass method and Trucking. The use of a chipping machine in the landing (two- pass method) was more cost-effective than the processing operation in each garden (single-pass method) in terms of production and cost. Modeling results showed that were significant in the two- pass method the effect of tree diameter and in the single-pass method the effect of tree diameter and residual density in the field on the residuals processing time.

In addition to benefiting farmers, wood chips from grape branches can also provide part of the country's particle board industry, and will help reduce environmental pollution by stopping the burning of branches in fields.

Rapid, low-cost and accurate estimation of tree metrics in the forest ecosystem is one of the challenges facing forest managers and inventories. Therefore, given that the forest areas of northern of Iran, mainly deciduous and uneven-aged forests, are covered with various tree ‎species of different sizes, mensuration techniques could be developed and established to suit the particular ‎circumstances. Methods used in forest inventory should be user-friendly, repeatable, relatively low cost, and also have high accuracy and precession for forest inventory operation.

Thanks to technological advances in the photography industry, one of the attractive and proposed methods could be the use of digital cameras, in which the potential and performance of this method was evaluated to estimate diameter at different heights of trees. For this purpose, using a digital camera which is mounted on a pre-designed horizontal index and in different shooting distance, diameter at stump height, DBH, and diameter at top of trunk for 108 trees, in REFTMU and in the altitude range of 750 to 1100 meters above sea level and in autumn was estimated. The distance from each shooting station to each tree was captured using the audio and laser rangefinder of the Vertex device as well as the laser meter.

The overall results showed that at 95% significance level, there was a significant difference with real data only in estimating the diameter at stump height. It is also showing an underestimation in estimating the diameter at breast height and at the top of trunk, and in estimating the upper trunk diameter, it was overestimated. The lowest amount of %CV.RMSE was also observed in estimating breast diameter (4.99%).

The evaluated method showed that in the middle classes of metrics captured data, it was more accurate and less error than the upper and lower classes. The results also showed that with increasing the distance of the shooting point, the error rate was increased. Consequently, the results of time study showed that the average operational time for capturing data in digital camera and the traditional method were 3.88 min.tree-1, and 3.42 min.tree-1, respectively.

In recent years, the general policy of the country has been based on the decision to stop the exploitation of wood from the north industrial forests. While the population is increasing, the demand for wood is increasing. Therefore, it’s necessary to find alternative sources to supply raw materials for the wood industry. In Iran, every year, a lot of wood waste is burned and wasted due to a lack of development and promotion of appropriate exploitation methods. In present study has been investigated the exploitation and optimal allocation of wood residues from pruning apple trees as raw material for the particleboard industry. Exploitation was carried out in apple orchards of Damavand city with a chipper and transported to factories by truck. Evaluations of the work process and the productivity and cost have been done with continuous timing method and optimal allocation have been made with the linear programming model by transportation method and simplex algorithm. The results showed that the longest recovery time of residues is related to the collection and transfer to the depot and chipping them, respectively and the production of residues is 1.97 tons per hectare and the production of chips is 0.160 tons per hour. Also, the optimal allocation of 400,000 tons of resources from the exploitation of pruning residues of apple trees with a minimum cost of transportation (950 billion rials), while solving the problem of waste disposal and reducing environmental pollution will bring an annual profit of about 450 billion rials for the country and will be compensated part of the shortage of raw materials for the particleboard industry.

Forest road networks play an important role to achieve management goals in forest ecosystems and despite of implementation of the forest logging ban, their protection and maintenance cannot be stopped. Therefore, in order to allocate budget and prioritize the maintenance planning, designing a reliable model for predicting the condition of road pavement failure has great importance. The purpose of this study is to predict the performance of road pavement for planning its maintenance management using Markov chain theory. In the present study, 185 road sections in the forest roads of West Haraz were selected and the data of pavement, surface and roadside conditions were measured in different plots during 2015-2018. Then, the functional quality of the road was evaluated by the PCI index and the developed index for forest roads (FRPCI). The pavement quality was classified into five classes: excellent, good, average, bad and broken. After assessing the current situation, the road future situation was predicted over a period of ten years using the Markov chain. The results of this study based on the Markov chain model showed that the process of road decline from the upper class to the good is faster than the process of changing it from the middle class to the poor. Given that a large amount of capital is allocated in forest management plans to road construction and maintenance, need to speed up the protection and maintenance of the road, especially on better quality roads is vital. According to the study, the Markov chain model has sufficient capability in predicting the process of road deterioration and road network maintenance programs.

Forest road construction is one of the activities that not only cause a lot of changes in the plant and animal species of forest ecosystem, but also, due to its widespread use in forest management, it is one of the most important projects that should be assesed. The aim of this research was to evaluate the environmental, social, and economical impact of Dohezar road of Tonekabon in the three stages including; before, during and after the road construction. In this study, the Geocybernetic Assessment Matrix (GAM) method, which is the latest method of evaluating effects, was used. In GAM, the parameters selected or determined by the evaluation criteria were examined according to the three aspects of environmental, social and economic. The results showed that the total geocybernetic score before road construction, during road construction, and after road construction was similar (before road construction: -13.33; during road construction: -80.78, and after road construction: -36.47), indicating that the construction of the Dohezar road had negative effects, especially during construction, but social and economic services, especially in the after road construction, have kept the total geocybernetic score constant. By implementing the GAM evaluation method, a suitable tool is obtained to ensure the proper implementation of the project or to stop it, which can be considered as a way to determine, predict and interpret the environmental, social and economic effects of the road construction project on the environment of the study area.

Having accurate information about the efficiency of skidding machines in order to reduce transportation costs inforest engineering studies using modern statistical models is very valuable. In this study, the prediction of the skiddingtime in steel tracked skidder and agriculture tractor was performed using an artificial neural network and multiple linearregression model and then the efficiency of the models was compared. The variables of skidding distance, slope, andvolume in each skidding cycle as independent variables (input variable) and time of each skidding cycle as thedependent variables (response variable) were entered into the model. The results showed the prediction in skidding timeof steel tracked skidder, the explanation coefficient of the MLP neural network and regression model were 0.78 and0.55, respectively and the error rate of models was 0.19 and 0.42, respectively. Also, in the agricultural tractor system,the explanation coefficient of MLP neural network and regression model were 0.70 and 0.62, respectively, and the errorrate of models was 0.18 and 0.28, respectively. Therefore, in both skidding systems, MLP neural network is moreefficient in predicting skidding time than the multiple linear regression model. Sensitivity analysis of the artificialneural network and regression showed that the skidding distance variable in the steel tracked skidder chain wheel andthe skidding path slope variable in the agricultural tractor are the most important.

Project time and cost are one of the issues and challenges that employers and contractors are facing whit them especially in the forest area. One of the most effective tools that used in prediction and risk analysis is Monte Carlo analysis that Based on the production of random numbers, the number of single components of a set is computed and find out their effect on the whole set. In the traditional approach to project control that still dominates in most national projects, estimating the probable time and budget of the project is still unpredictable to managers and experts. In this work, we tried to present a model for considering the probability and risk in estimating the probability of completion of the project by given time and cost of project termination in a forest area using a case study and software capabilities (Primavera Pertmaster). The results of the probability distribution revealed that the probability of reaching the predicted budget was approximately 13% according to the Monte Carlo simulation. However, the difference between the maximum and minimum values varies from 39 to 650 million IRR, which results in a high risk of the operation of the land operations, which indicates the high risk of the earthwork operation. The real cost of earthwork (570 million IRR) in the study area at the cost allocation chart is estimated at 90%, which also confirms the riskiness of the road earthwork project. Finally, the Monte Carlo simulation method, with the successful prediction of the risk of earthwork operation project, showed that there is an acceptable way for contractors and forest road designers to deal with the negative aspects of the project. The results of this research can be used as a guide to better management of forest for future operations.

For designing forest roads that in addition to minimize the cost of constructing, satisfy environmentalists, it is necessary to provide designers and contractors accurate data from surface and subsurface conditions of the road area due to the large-scale of data using of optimization methods (metaheuristic algorithms) with the help of computers is necessary. For this purpose, this research was carried out on a proposed forest road whit length of 1 km in the forests of the district 4 of Babakoh in the city of Siahkal in Guilan province. The cycle of all earthwork work was studied by continuous time study method and the information of the subsurface layers terms of digging was carefully recorded. The results of comparing the method of the ant colony with the hand-made design scenario showed that the multi-objective algorithm presented in this study has great potential for reducing the cost of earthwork operations and its volume. Based on the results, the algorithm is able to reduce 58.55 and 64 percent the cost of earthwork operations and the volume of operations, respectively. Finally, the results of this research can be used as a guide to better management of forest areas, especially forest roads with the development of efficient methods in all forestry forests.

In this research, the application of Artificial Neural Network or MLP method in the process Assignment of relevé-groups/ plant communities allocation was evaluated using Buxus hyrcana forests database. For this purpose, firstly, the ecological and sociological groups of B. hyrcana were determined using TWINSPAN and Braun-Blanquet method, respectively. The results of both numerical and expert based classification dendrogram of the B. hyrcana communities, which included seven levels of classification as primary groups/plant communities, were introduced to MLP. Then, with assignments in three sets of training (70%), test (15%) and validation (15%), the MLP classification was performed on each level of the two dendrogram. The results showed that by increasing the level of classification, the degree of adaptation of the MLP result to primary results of TWINSPAN (99% to 60%) and Braun-Blanquet (98% to 68%) from the cutoff level of 1 to 7. Results of sensitivity and kappa cross tab coefficients, except in 7 cut level, imply that the quality of MLP groups based on TWINSPAN primary ecological group is upper than primary Braun-Blanquet groups.Appropriate adaptation of the MLP results in Buxus hyrcana plant communities classification with the TWINSPAN and Braun-Blanquet ecological/syntaxa groups at the fifth cut level of both dendrograms indicate that application of MLP aim at a reliable results in plant community classification. So our result reiterate that MLP could be introduced as a suitable method in the assignment of releves to plant communities.

The Landslide is one of the natural hazards that causes a lot of mortal and financial losses in high-altitude and seismic mountainous areas annually. Considering the importance of roads as a pillar of sustainable forest management in this study, we have tried to estimate the sensitivity of landslide occurrence in 8 forestry series with using frequency ratio approach. Initially, the slip position in the series occurred with ground scrolling by GPS, then the effective factors in the occurrence of landslides were determined for the study area. Raster maps of the effective factors were prepared and classified. Considering the distribution of landslides recorded in the field and the categories of effective factors, the frequency ratio for each class of each factor was calculated and their relationship with the occurrence of landslide was investigated. The results of the relationship between positioning and slipping showed that the highest amount of landslides occurred on the lower side of the road, and by increasing the age of the roads, the amount of landslides is reduced. The relationship between soil and slip occurred on the class with the code 2.2.3, which represents the soil texture of loamy, loamy clay and clay that the highest number of slides was observed. Elevation class 300 to 600 meter has the highest number of landslides. The relationship between the slope and slip showed that with increasing the slope up to 40%, the number of landslides was increased, but then the trend was decreasing. The relationship between geological and landslide showed that the highest number of landslides was observed in the class with L-PLL2 ​​code representing relatively high peaks with moderate slope, average soil depth, stability and permeability. The relationship between rainfall and landslide factors showed that the greatest slip is occurring between 600 and 800 mm. In the northern, northeastern and northwest directions, the highest slip occurred. The evaluation results of the model with the ROC method indicated that the area under curve (AUC) was 0.688 (68.8%).

Tourism is one of the most dynamic economic activities in the present era, which plays an important role in the local sustainable development, and its development priority requires the recognition of affecting and limiting factors of natural, economic, social and cultural each region. Moreover, these areas areas reveal a special importance for tourists due to having a set of natural and historical attractions. Accordingly, the main objective of the present study is the selection and prioritization of tourism areas among the three areas "Banddareh-e-Birjand, Baghestan Abgarm Ferdows and Ghaen tourism area" in South Khorasan province by using the ANP method. Therefore, to obtain the appropriate results in the tourism areas prioritization in South Khorasan province a high level of ANP structure including subnet benefit, opportunity, cost and risk was used. The results of the ANP analysis demonstrated that Baghestan Abgarm Ferdows, Banddareh-e-Birjand and the Ghaen tourism area with having 0.59, 0.28, and 0.29 weights placed in the first to third priority, respectively. Among the subnets, the benefit subnet with a weight of 0.33 possesses more important as compared to the others. Furthermore, the strategic criterion of tourism potentials with an equivalent weight of 0.51 manifests a higher priority in comparison with the other ones.

Markets have been the places where earliest human communities have formed based on essential social needs. This survey evaluated and explained the function of weekly street markets in improvement of qualitative indexes of life and conditions of livelihood in the rural families. It was an applied study in terms of objective, and a descriptive-analytical and survey study in terms of methodology. On this basis, different questions were prepared in different measuring scales (names, orders, and relative scales) within questionnaires. The statistical population included the household heads settled in the villages that had weekly street markets in three sub-districts of the studied region. In order to analyze and measure the data, correlation analysis tests such as Chi-Square and Phi-Cramer as well as mean value comparison tests of Freidman and Kruskal Wallis were used. The study results showed that there was a significant relationship at a confidence level of 99 percent between formation and establishment of the weekly street markets in some of the villages and the improvement of economic and social indexes such as entrepreneurship, income increase, job variety, reduction of expenses, and increase in motivation to stay in the villages. In addition, the results from Freidman and Kruskal Wallis tests on the comparison of physical development indexes in different villages showed that establishment of the weekly markets had significant differences in improvement and elevation of some physical factors; accordingly, to an acceptable extent, improvement of physical situations in the villages depended on the formation of markets in the studied rural areas.

Knowing natural and human potentials of each area makes it possible for the development planning to be done based on the current situation and its potential. Typical tourism areas of the country are one of the key issues in policy making and tourism development, where attracting private sector investment, will provide sustainable regional development. The aim of this study is choosing a touristic area between the three regions Khranq, Saryazd and Narin Castle in Yazd province. The data was collected using documentaries and field research and data collection tool is a questionnaire with simple random sampling. In this study, to select and prioritize the tourism area, ANP (Analytic Network Process) of multi-criteria decision making techniques were used. Results obtained from AHP analysis showed that, taking into account the environmental criteria, tourism potential, economic potential and the role and impact of weight management, Mehriz Saryazd area is more adequate than the other two regions for investments in Yazd province. Narin Castle is second by a small margin. Also according to the technique used in the prioritization and the results, the pattern can be used for planning for the areas of other provinces.

Loyalty and customer loyalty is one of the major challenges of companies and institutions that provide services and goods to their customers. The social security organization has faced the challenge in recent years in the issue of loyalty of its insured individuals to continue insurance. In this research, information and important indicators of 21407 people were extracted from 27 databases of this organization. Then, using the NSGA-II algorithm, seven important characteristics with the least degree of classification error were selected. In the first step, the classification of data with multi-layer neural network was performed on existing data using 27 characteristics and the classification accuracy was 97.6%. After selection of the best class that was neural network, classification operation with neural network was performed on the same data and in the form of 7 characteristics and accuracy of 96.8%. At the selection stage, the best stratified strain was used to perform the required predictions from three multi-layer neural network algorithms, backup vector machine (SVM) and KNN algorithm. Finally, the multilayer neural network had the best degree of accuracy with a 96.8% got. Then, for the neural network, the training was carried out using 7 characteristics related to the data of years 1367 to 1395. The trained neural network was used to predict loyalty and customer rejection of 1396 and 1397, with 8364 records. Finally, it was concluded that considering all probabilities, about 27.65% of the insured persons who were born in 1396 and 1397 would be categorized in a reversal class.

Considering the role of organic carbon in soil management and sustainability, the prediction of the organic carbon of soil at regional, national and global scale is of great importance. Along with the prediction of organic carbon, zoning is also effective as a way to select the appropriate strategy for controlling this element and make appropriate decisions inmanaging and preventing environmental disasters.The purpose of this research is to predict and zonethe organic carbon of soil based on primary and secondary topographic variables using artificial neural network. For this purpose, 127 circular sample plots,with an area of 1000 square meters,were harvested from the educational and research forest of Natural Resources Faculty of TarbiatModarres University following a systematically randomized procedure. At the center of each sample plot, a soil sample was taken from a depth of 0-10 cm. In addition to measuring the organic carbon from thecollected samples, the primary and secondary topographic variables of the area were calculated on the basis of the digital elevation model and finally, using artificial neural network and multivariate regression, the storage model of the organic carbon was created. The aforementioned models were evaluated using R2 and RMSE criteria. The results showed that the artificial neural network model with a capacity to justify 76% of the changes in the organic carbon of soil has a better performance in comparison to the multivariate regression model with a capacity to justify 26% of the amount of changes. In addition, plan curvature, curvature and slope indices have the greatest impact on the changes in the organic carbon of soil and together justify 90% of the changesin organic carbon.

In order to better management of existing forest roads maintenance in the country in addition to economic indicators, it is essential to contribute other effective factors such as improvements in road pavement, road effects on economic conditions of forestry plans, cost of forest operation, users and natural conditions. Considering the above-mentioned factors as well as their impact on the prioritization of options requires multi-criteria decision-making, which has been investigated in this study. For this purpose, factors affecting the maintenance were first determined by expert's opinion. Then 50 km of forest road under the management of Mazandaran wood and paper company was selected. Due to the wide range of quantitative and qualitative factors and a large number of alternatives, Analytical Hierarchical Process (AHP) was developed to prioritize the repair and maintenance of forest roads. According to the results, the mentioned method is able to prioritize the forest network in term of the maintenance operation. The result of the current study also showed that the decision makers are able to prioritize the forest roads from different spatial and temporal point of view using the generated model according to expert knowledge and effectiveness of these factors.

Despite the expense of construction and maintenance, forest roads are still essential to provide land accessibility and management. Quality of roads depends largely on the pavement conditions. The heavy machines directly induce stress on pavement as first layer of a road. The current study aims at investigating the equivalent axle load, the devastation wrought by the axis to the devastation wrought by the standard axis (8.16 tons), on pavement deterioration. To develop the equivalent axle load model, 50 km of forest roads, under management of Wood and Paper Company, was selected with respect to the trucks and cars and critical values and growth rates of pavement failures were identified using the log of changes failure to the equivalent axial load. The results showed that highest proportion of failure involves trucks 15 tons (growth rate 10.2), nine-ton trucks, Nissan truck and cars with the values of 5.77, 1.8, 0.64 and 0.45 respectively. According to the obtained results, less equivalent axle load vehicles with lower the volume of timber and wood out of the forest with enough time should be utilized.

Assessment of forest road pavement condition and its optimal cost management is backbone of a forest road network. In addition, an optimum budget allocation is a challenge during road life cycle. The precise assessment requires special equipment that is often neglected due to its absence or the associated high expenses. Therefore, it is recommended to use techniques that can provide cheap and fast linear and nonlinear modeling of pavement deterioration. The aim of this study was to investigate the performance of Genetic Algorithm (GA) to detect the warning level of forest road pavement as well as to plan the pavement maintenance. The GA was evaluated to identify the warning level of pavement and provide a plan for the maintenance of the road network over a period of five years based on annual plans across a test site of 5 series located 50 km west of Haraz forest road network. The results showed the optimum protrusions warning level for five centimeters damage and rutting 10 cm. Moreover, it was shown that an optimum planning combined with regular repair and maintenance work enables reducing the associated costs from 25 to 73 percent during the first years. It showed the importance of regular maintenance activities at the network level of pavement and application of novel techniques to allocate budgets for pavement maintenance.

The maps of potential flooding areas can be the appropriate tool for urban planning in the future. In this research, potential flooding areas criteria viz. the urban land use, distance of river, elevation, slope and ground water level were considered. The weight of decision criteria were evaluated by paired comparison matrix. The final potential flooding map was divided to five different risk classes. About 15 percentage of the study area were belongs to very high and high risk classes. Including areas located in high risk near to the river, impermeable areas, low topography and slope less than two percent in these areas. Focus on areas with historical flood events showed that areas with slope less than two percent are more affect by flood. The results of the Analytical Hierarchy Process showed that ten percentage of the area with historical flood events is located in very high and high classes. The results show that percentage of the class area with high and very high risk according to historical flood events are 43, 34 and 26 percent for gaussian, triangular and generalizedbell-shaped membership functions, respectively. The result of Gaussian membership function is more similar to historical flood events reports.

One of the new approaches in forest road network and skid trail planning is to find suitable terminals in the harvest units and after that planning a road network that optimally connects the terminals. The paper describes a methodology for finding the most economical landing location.
The methodology formulates each harvesting unit as a network problem. Each grid cell that identified as an individual entry node in the network, containing two attributes of the elevation and the timber volume. The exit points of the network are the landing nodes where are candidated based on the terrain slope by the planner. Each entry node were connected to the supposed landing nodes by a direct link. In the present research an economical place for landing location was defined as a point that makes the most volume of stock in access through the shortest distance and slope. The heuristic algorithm, then, calculated two parameters for each landing node: 1) the degree of attractivity as a function of the number of covered nodes, their volume of stock and their distance to the landing, and 2) the average slope of the links. The economical location for landing then was selected. The entire area of the compartment was supposed as the harvesting unit. In addition, the uphill and downhill skidding were included in the program.
The program was implemented for four candidate landing location in the compartment 423 of district 4 (Shiva dareh) in the educational and research forest of Tarbiat Modares University in the northern Iran. The results showed that the point “B” with an attractivity value of 5585.79 and an average slope of 10.41% was designated as the most economical point for landing the timbers.
The present code could be used as an appropriate tool to propose the economical places for landing location where are assumed as desirable terminals in forest road planning within cut blocks.

Estimating of forest equipment productivity is an important aspect of managing cost in forestry, which leads to reduction of operations expenses. In other words, high capital cost in forest harvesting, is a good reason to argue forest engineering research and time modeling. This paper applied one of the Artificial intelligence subsets, which are called Artificial Neural Networks (ANNs), to predict winching time of wheeled skidder Timber jack 450C in Neka Choob forests. Continuous time study was performed to collect winching data during skidding operations. Winching time and related effective factors including slope, distance, number and volume at every winching cycle was measured simultaneously. Two neural networks type- Radial Basis Function and Multi Layer Perceptron- were used to develop winching time model. Moreover, in order to compare accuracy of ANNs and mathematical model, the regression analysis method was developed. Results showed that RBF network provided more accurate results in winching net time estimation compare to MLP neural network. The most effective variable in both networks was determined distance to the center of the skid trail. The results showed that the model developed by neural networks has more precision than the linear regression method.