فصلنامه مدیریت و برنامه ریزی محیط زیست
پیاپی 8 (تابستان 1392)

Determination of landslide hazard zones should be considered a primary activity in natural resource management that results in reduced landslide costs. Using landslide hazard zoning، one can detect susceptible and high potential landslide hazard areas. To address landslide hazard zone، GIS techniques were used to determine rapidly the sensitive areas. All important parameters such as slope، land-use، geology، rain and distance to fault lines were selected from satellite images of Landsat، topography and geology maps. Then، these layers were weighted according to Analytical Hierarchy Process and finally were combined based on Hazard Potential Index (HPI). Then، final layer were classified into 5 categories based on Natural Breaks Values. The results of this method، that are applicable and repeatable، indicated that in most of the landslide have occurred in southern part of Galugah to Kordkuy cities. These areas have lithological units including marl، clay، lime stones and slope classes 5 to 35 percent. Also، the comparison of generated landslide hazard layer with the actual landslide layer confirmed the accuracy of the determined hazard zone and the method. We suggest the use of this layer in planning activities like road and building construction to reduce the environment and economic costs. The method used in our analysis falls in the semi-complex approaches to landslide zoning، is repeatable and easily upgradable and matches with the pace of development in the area.

Coral ecosystems after tropical forests are the most productive ecosystems on Earth and on the other hand، Coral reefs are sensitive ecosystems due to both natural and human disturbances. As a result، reconstruction and rehabilitation of degraded reefs is considered one of the major reef management approaches. In this study، we were interested in the management of coastal ecosystems، particularly coral reef ecosystems. Degraded coral reef rehabilitation techniques were divided into three groups such as prevention، mitigation and restoration approaches. This is to protect a coral ecosystem of what approach should be used، depends on targets، timescale for reduction، intensity of degradation of ecosystems and fund managers in protecting their ecosystems. Finally، it can be concluded that among different management approaches، a combination of biological and physical protection of coral reef ecosystems are more successful. In this integrated approach، the first step is the introduction of degraded ecosystems as protected area then by establishment of artificial reefs in the vicinity of damaged ecosystems to plant coral pieces and colonies on these grounds.

One of the important factors for development in each region is the availability of appropriate water resources. In addition to water quantity quality is also of great importance. Water quality is a term used to describe the chemical، physical and biological characteristics of water with respect to its suitability for a particular use. One of the new methods in environment problems is using Artificial Neural Network (ANN). Main objective of this study is to evaluate capability of Artificial Neural Network MLP and RBF and Multivariate Linear Regression (MLR) models in order to predict qualitative indices (TDS) in Trajan river of Sari، located in Mazandaran Province. In this study، the information and data from Tajan River including 9 monthly parameters of water quality for a period of 40 years (1966-2005)، used to predict qualitative indices (TDS). Efficiency of the models was evaluated by some statistical criteria including determination coefficient (R)، root mean square error (RMSE) and Nash-Sutcliffe efficiency (NS). The results of MLR showed that qualitative indices (TDS) can be predicted based with correlation coefficient and determination coefficient of 0. 762 and 0. 872، respectively. The results indicated the ANN model could provide better prediction in comparison with MLR model. Also، comparison of these two neural network results specified that MLP neural networks had a relatively more accuracy and the only advantage of RBF neural networks was their much less time of training.

The purpose of the doing research is solve problems and improve the situation، making appropriate decisions، increase performance and productivity and access to confidence in the macro and micro level planning. Not research findings application is as a waste of financial and human resources spent for them، And not only cause creation adverse effects departments and finance officials minds is ratio to the utility invest in research field، Cause undermine researchers incentives provides for continue efforts in this fields. One of the primary steps in identifying factors reduction the needful and suitable collaborations between department of research and executive is search barriers and factors of research findings application. The purpose of this study is search barriers of the research findings application on natural resources and the environment. required information were collected with search in library sources، Internet and interviews with academic members of universities، employees and deputies department of Natural Resources and department of Environmental Conservation for the preparation of the questionnaire. Prepared questionnaires was distribution in face personal and was send to e-mail to academic members of universities، related offices and agencies. 40 questionnaires were completed that 25 from them by employees of the department of environment and natural resources، 15 other by academic members of natural resources universities. SPSS software was used for data analysis. Results showed gaps of attitude، communication and responsibility between researchers and decision-makers، such a lack of direction and purpose of research projects and plans in universities are the most important barriers to the research findings application.

The importance of green space in the urban environment to the extent that it is considered as one of the indicators of developed community. Unfortunately، developing of urban green space always faces with threats and opportunities. The object of this study is Composition of management strategies and investigation of effective variable on urban green space in region 8، Tehran Municipality. So، On the base of Cochran formula was prepared 384 questionnaires which have validity. This study was done after finding resident opinions (as a sample) and analyzing of questionnaires results in 2 steps. in the first step، introduced some variables which affected by green space management in region 8، Tehran Municipality and presented 4 hypothesis about green space management influences on emotional، mental and physical health of citizens، increasing of life conditions، improvement of environmental conditions، decreasing of pollution (like noise pollution which is pollution indicator in region 8). In the second step، investigated some threats، opportunities، strengths and weaknesses points and presented strategies on management and improvement of environmental conditions by using of SWOT، then investigated relationships between variables and hypothesis by Pearson correlation in SPSS and Excel software that results showed significant relationships between variables and hypothesis prove.

Water pollution in rivers is a complex process. Transmission of pollutant in river is one of the most important problems in Environmental Science. The governing Equation of transmission of pollutant in river is Advection Dispersion Equation (ADE). The ADE is Partial differential equation (PDE) that it’s one of the most important equation that used in fluid engineering Special water engineering، this equation named in general Convection equation. This equation has two part، Advection and dispersion term. To modeling of pollution transport in rivers needed to developing Numerical model for solving the ADE and calculating of Longitudinal Dispersion coefficient (LDC). LDC is one the important parameters in the Pollution transport simulations. Accurate estimates of this parameter increase the accuracy of the computer modeling. There are many methods to calculate the diffusion coefficient of the experimental and mathematical methods. The results of the experimental has significant errors because it’s based on indirect parameter in the simplification of modeling. So it needs to be calibrated with observational data or Methods that the Dispersion coefficient can be calculated directly. In this study، the routing method was used to calculate the Dispersion coefficient and the following a careful review was done on the empirical formulas. Final result shows that the intelligent method (ANN and ANFIS) is more accurate in compare with empirical formulas.

This paper examines the traffic noise in Golestan National Park road that has passed through the Golestan National Park. The increasing number of vehicles that travel in the country’s highways has led to an increase in the noise level. Therefore، traffic is considered a main source of noise pollution in this area. In this road to determine factors affecting on noise modeling was performed. Because the models are useful tools to identify changes in current and future traffic conditions. At first، for evaluation of noise and recording independent variables، sampling stations were selected using a Systematic-Random method in 76 points at various distances from the road. In each sampling point environmental variables، geometry of road surface and traffic conditions was measured for 15 minutes during 8 am to 8 pm. then modeling was performed among 18 independent variables and one dependent variable using stepwise regression. To verify the model، data of 26 stations not used in the previous modeling effort were randomly selected. In this way، the best multivariable regression based on correlation coefficient (R) and coefficient of determination (R2) was achieved. The result of modeling showed that the Lmax model have 6 variables including distance from the road، slope of plot، temperature، relative humidity، number and speed of high vehicles that have the greatest impact on sound propagation. The regression equation were 68% respectively. Therefore this model was as the final model. Thus، one model was obtained for a maximum sound level (Lmax (1h)) in the Golestan National Park road.