نشریه مدیریت جامع حوزه های آبخیز
پیاپی 2 (زمستان 1400)

Groundwater resources are one of the main natural resources that support socio-economic development of countries (Siebert et al., 2010). Management and protection of groundwater resources is of great importance in countries such as Iran, which are located in arid and semi-arid regions without surface water resources. Therefore, resource conservation, in addition to short-term and long-term planning, is essential to optimizing their productivity (Abu-Khalaf et al., 2013). These natural resources face a variety of issues that threaten their sustainability, such as the effects of climate change, human activities and natural processes (Alabjah et al., 2018; Baghvand et al., 2010; Burri et al., 2019; El Asri et al., 2019; Houemenou et al., 2020; Mountadar et al., 2018). Considering the great importance of recognizing the quality characteristics of groundwater in desert areas and the need for its proper management, predicting groundwater quality for the management and exploitation of water resources in the Yazd-Ardakan plain seems urgent. Therefore, the aim of this study was to evaluate and compare the efficiency of artificial neural network and random forest models in predicting EC, SAR, SO4- and TDS values. In this research, modeling will be done based on the relationship between environmental (auxiliary) data and groundwater quality parameters.

The study area is spread over ​​482900 ha of land, which in terms of position is 53° 08´ 36˝ and 54° 85´ 32˝ E longitude, is located in the central plateau of Iran and the central part of Yazd province. The maximum height of the area is 2677 m and the minimum height is 997 m from the sea level. To evaluate the quality of groundwater sample, 201 wells of the monitoring network of Yazd Regional Water Company in 2016, the measured parameters EC, SAR, SO4- and TDS were used. The factors of environmental (auxiliary) data in this research include geological data, land use, vegetation indices, soil salinity indices, derivation of digital elevation model, distance from mines, distance from road, distance from river, distance from residential areas, rainfall and population.After preparing the groundwater quality parameters and environmental data, the values of EC, SAR, SO4- and TDS were predicted using artificial neural network and random forest models. In order to validate the random forest model, the cross-validation method (10-fold) was used in R statistical software. In this method, the data is divided into 10 parts. 9 parts of the data are used for modeling and the remaining part is used to validate the obtained model. MAE, RMSE and R2 statistical indices were used to evaluate the efficiency of artificial neural network and random forest models for groundwater parameters.

In this research, in simulating EC, SAR, SO4- and TDS parameters, the best structure obtained from 100 repetitions of artificial neural network learning has 2 hidden layers and 10 hidden neurons in each layer. The results of random forest sensitivity analysis show the high importance of the parameters extracted from the DEM. The use of satellite image data to investigate the groundwater quality parameters is also a convenient and cost-effective method. Furthermore, combining satellite data with DEM to investigate groundwater quality parameters and their zoning makes the results more efficient and increases its accuracy. A very important point in the analysis of parameters is the important role of parameters such as distance from the road and distance from the mines. This indicates the direct effect of man-made environmental factors on the groundwater quality of the study area. The results of modeling the EC, SAR, SO4- and TDS parameters using artificial neural network and random forest models show the appropriate accuracy of the artificial neural network model in predicting these parameters. Accordingly, the neural network model has predicted the parameters of EC, SAR, SO4- and TDS of groundwater with coefficient of determination of 0.82, 0.92, 0.92 and 0.97, respectively, while the model Stochastic forest has predicted EC, SAR, SO4- and TDS parameters of groundwater with coefficients of determination of 0.37, 0.65, 0.80 and 0.68, respectively. Based on the obtained results, it can be concluded that the artificial neural network model has a higher accuracy than the random forest model in predicting groundwater quality parameters. EC, SAR, SO4- and TDS parameters have the highest values in the north, center and southwest, and the lowest values in the southeast and southwest.

It can be said that the technique is highly efficient for estimating EC, SAR, SO4 and TDS parameters in Yazd-Ardakan plain as long as a proper and a sufficient number of input elements, a proper and compatible artificial neural network, and an appropriate calibration are used.

In order to achieve sustainable development and conservation of natural resources, it is necessary to promote the participation of individuals, especially users of natural resources. In fact, people's participation is of paramount importance in the implementation of environmental resource protection programs, and new trends in the global arena are based on strengthening the role of people in the protection of natural resources. Experts believe that the involvement of local individuals and groups in rural communities in watershed management projects can bring many benefits. It is believed that involving local people in the watershed management process can improve the technical and analytical skills of the users. In general, the success or failure of watershed management projects should be sought in the presence or absence of people. However, paying attention to the role of people in these projects and finding ways to encourage their participation is an inevitable necessity. Therefore, the general purpose of the present descriptive-correlational research is to prepare a causal model of the elements that influence the willingness of watershed residents to participate in watershed management projects of the Great Nain River Basin. Watersheds are influential and can promote the participation of watershed residents.

Naein Great River Basin with an area of ​​about 130,000 hectares between 32 degrees; 45 minutes and 18 seconds to 33 degrees, 09 minutes and 20 seconds north latitude,52 degrees and 29 minutes and 18 seconds to 53 degrees, and 05 minutes and 41seconds is located on the east of the Greenwich meridian. The minimum height of this basin is 1552 meters and the maximum height is 3094 meters. This watershed is located in Naein city of Esfahan province in terms of national divisions and includes two villages of Koohestan and Baharestan and 41 inhabited villages with a population of about 2298 people. The statistical population studied in this study includes watershed residents living in Naein river basin of Isfahan province (N = 2298), among whom 476 people were selected as a statistical sample using Cochran's formula. due to the large size of the statistical population, random sampling method was used and information was collected. Finally, 476 questionnaires were collected and analyzed (performing stepwise regression, performing path analysis technique and preparing a causal model of the elements that influence the willingness of watershed residents to participate in watershed management projects).

The results of stepwise regression showed that social factors account for 69.1% of the willingness of watershed residents regardless of other variables. Also, social, economic, and communication factors as well as information resources and education item indicate a total of 74% of the changes in the willingness to participate in watershed management projects. The results obtained from the table of regression coefficients to determine the relative importance of variables and their effect on the willingness to participate showed that social factors have the largest share in the willingness of watershed participants to participate in watershed management projects and education item has the lowest share in their willingness to participate. Also, the causal model of the elements that influence the willingness of watershed residents to participate in watershed management projects showed that the factors mentioned above also have many indirect effects on the willingness of watershed residents. Therefore, according to the stepwise regression and the causal model prepared, planners and natural resource specialists should pay considerable attention to social, communication, and economic factors as well as information resources and education, For example, local councils can be used to foster the participation of local people in watershed management projects. Also, providing low-interest facilities and credits can greatly increase the motivation of local communities to participate in watershed management projects. Relevant organizations and the government can motivate local communities to participate in watershed management projects by creating jobs and improving the economic status of people. It can also be said that by promoting mass and social media, holding training classes, visiting other successful projects, etc., the level of participation in watershed management projects can be increased.

In general, the findings of this study indicate the great effects of three factors (economic, social and communication as well as information resources) and an individual item (education) on watershed participation, which was presented as a causal model of watershed participants' willingness to participate. Therefore, planners and natural resource specialists should pay considerable attention to these factors in order to increase the success of watershed management projects by encouraging more participation of watershed residents.

Noise pollution is an environmental issue that threatens the human health and the survival of other living organisms along with air, soil, and water pollution. It is also considered as one of the important criteria in determining the quality of life in the world. Various sources such as growing urbanization, transportation, spatial heterogeneity of land use types and improper planning can increase noise pollution. Therefore, assessment and zoning of noise pollution, which is one of the effective ways of identifying the areas having noise pollution, can play an important role in controlling this pollution.Accordingly, interpolation approaches are an appropriate tool for identifying and evaluating areas with different pollution ranges. Although interpolation approaches are successful in zoning environmental parameters, they do not specify how these parameters change. In other words, they do not determine the type of spatial distribution patterns of these parameters. Therefore, spatial statistical methods are a suitable tool for determining the spatial distribution patterns of environmental parameters. In recent years, spatial statistical approaches have been widely used in the environmental sciences, and in recent studies, researchers have used the capabilities of these methods to identify and examine the spatial correlation of homogeneous areas. Hence, the purpose of this study is to investigate and evaluate the spatial correlation patterns of noise pollution in the central part of Zayandeh-Rood watershed using spatial statistical approaches.

The research area is the central part of Zayandeh-Rood watershed, which is located between 32°19′ to 32°56 north latitudes and 51°12′ to 51°59′ east longitudes, and includes parts of Isfahan, Shahinshahr, Khomeini Shahr, Najafabad and Falavarjan. This region covers an area of 1181 Km2. The average annual temperature in this region is 16.7 ° C and the average rainfall is 116.9 mm. This region, like other areas of Isfahan province, has a traffic problem due to the uncontrolled increase of personal vehicles, which has caused people to turn to highways and sidewalks to avoid traffic. The problem of traffic in this region lies in the lack of proper infrastructure to control urban and suburban transportation. In this research, to evaluate and zone noise pollution, 36 stations were randomly selected in the study area in the first place. Then, the sound pressure level was measured at certain hours of the week using the Bruel & Kjaer sound level meter while observing the principles of noise measurement including distance from building, the stability of sound meter at a certain height, and reduction of the error caused by vibration of air molecules.. In the next step, spatial correlation patterns of noise pollution were evaluated using spatial statistical approaches including Global Moran Index, Hotspot analysis, Anselin Local Moran's I, and Cluster/Outlier Analysis with Rendering. In the end, the spatial accuracy of the prepared maps was investigated using Receiving Operator Characteristic (ROC) for different approaches. In order to evaluate the spatial accuracy by this index, in addition to the map obtained from different approaches of spatial statistics, a reference map is also needed. In this study, one third of the points used for noise pollution zoning were randomly selected and were used as the reference map to assess the spatial accuracy of the maps obtained from the previous step.

The result of noise pollution zoning in the study area indicated that the noise intensity changes range from 47.59 to 77.48 decibels. According to the noise pollution zoning map, the highest noise level belongs to the eastern and northern parts of the region. These areas include important urban areas, such as Isfahan, Khomeini-Shahr, Shahinshahr and Najafabad. The southern and central part of the study area, which has a high density of agricultural lands and green spaces, has a lower noise level. This issue has an effect on the results of spatial correlation assessment of noise pollution in the target area; areas with high noise pollution levels follow the correlation pattern of high clusters, while the areas with low noise pollution involve a low clustering pattern. The application of the Receiving Operator Characteristic (ROC) showed that the Hot-spot analysis approach has higher accuracy than the other two approaches in evaluating the spatial correlation patterns of noise pollution. The area under ROC revealed that the Hotspot analysis approach is more than 96% reliable in identifying focal areas with high noise pollution. Given that the probability for the other two approaches is 92% and 89%, respectively, and considering that the rate of the area under the ROC is very close to 1, the efficiency of these two approaches in identifying and determining areas with noise pollution is acceptable. Furthermore, considering the Receiving Operator Characteristic (ROC) of all three methods, the accordance degree of these methods with ground reality is spatially high.

The results of this study revealed that evaluating the accuracy of different approaches in examining the distribution patterns of various phenomena (such as noise pollution) has an effective role in planning and scientific management of the land based on the land-use planning principles. According to the findings of this study, the simultaneous study of the spatial statistical approaches provides a fuller understanding of how noise pollution is distributed in the study area.

Land use change is one of key factors in global environmental change, which affects a wide range of environmental characteristics and natural resources (processes and functions of ecosystems and climate systems, water quality, land and air resources). Excessive destruction and exploitation of forests and pastures, inadequate cultivation patterns, land degradation due to population growth and lack of proper management of natural environments have endangered nature and destructed habitats. Planning in this respect demands a clear understanding of how the land should be used and the type of activity done in each part of it in order to obtain accurate information about different communities and terrestrial phenomena. Also, it is difficult to access information to evaluate land degradation and land use change on both global and regional scale. One of the most important ways to determine land use change is satellite imagery. Because protected areas play an important role and are the core of biodiversity conservation, their management and conservation require further field research. Therefore, having information about land use and its changes over time is one of the important issues in planning and policy-making in this region. Therefore, the present study was conducted to predict and evaluate the rate of natural land degradation by emphasizing land use change in the Dinarkoh protected area.

Dinarkoh forest protected area is one of the mountainous areas of Ilam province. This study collected the required information about the study area and similar studies using library and field studies. Then, satellite images were extracted for data analysis and software methods were used to process them. In this way, after examining the geometric and atmospheric errors in the images used, using the maximum probability method, a map of land use classes related to the period 2001-2020 was created. Then, using two post-classification comparison methods, the difference in vegetation index (NDVI) was used to detect and study land use changes in the study period.

The results of evaluating the accuracy of the map obtained from the classification with the maximum probability algorithm on TM and OLI images showed that the accuracy of each of the TM and OLI classified images in the supervised classification method with the maximum probability method is high and acceptable. Considering the overall accuracy of 92.09% in TM image classification and 92.66% in OLI image, it can be said that these two images can prepare the desired area map at any time and can be used to identify changes. The results showed that factors such as similarity of the reflection of some lands which causes an inaccurate segregation of classes reduce the accuracy and are classified as a source of error. Therefore, in general, it can be said that according to the accuracy obtained from TM and OLI images, these two sensors can detect changes. The results showed that forest exploitation and a sharp increase in the population of villages and cities and their need for housing and economic factors have reduced the level of forest. Comparison of the classification maps obtained from the methods used in this research with the ground reality map showed that these methods have different accuracy in identifying areas of change and non-change. The results of satellite image processing showed that in 2001, compared to 2020, more than 6000 ha of forest lands in the region during the 19-year period have been converted into non-forest lands such as agriculture and rangeland, which indicates poor protection of the region.

The results showed that 8000 ha lands in the study area in terms of NDVI index had a decrease in vegetation cover, which indicates the trend of degradation in the region and the need for more serious conservation measures.

Ecosystems, if properly managed, provide a vital variety of services such as the production of goods for human beings. Therefore, they need to be protected and developed as a fundamental solution to achieve sustainable development and ensure the well-being of generations. Ecosystem services are natural activities and functions necessary for human well-being. Understanding the value of natural property as one of the preconditions for sustainable development reveals the significance of valuation. In fact, valuing ecosystem services is one of the main tools for protecting the environment and natural resources. Using economic valuation ways in actual policy making is a rare phenomenon. Analyzing the valuation studies shows the need in future to strengthen the sectors that affected by economic valution regading decision making.

Topics presented in this article are based on a review of the scientific literature on the economic valuation of ecosystem services, concepts of ecosystem services and classification of services based on the Millennium Ecosystem Assessment (MEA, 2005), interviews with experts from various academic and research centers and library studies and Internet sites.

Monetary arguments for recognizing the relative importance of different forms of ecosystem services and natural capital may not be universally accepted, but they can be useful and convincing for decision makers. In other words, if they do not value the benefits provided by nature, they are considered worthless and the current trend in the decline and destruction of natural systems will continue. In environmental policies and natural resources, economic evaluation involves determining the monetary value of changes in services and the functions and shares of natural capital. A valuation of natural resources and the environment by economists contributes to understanding the benefits of the environment, helps decision makers and planners become cognizant of the environmental issues, and specifies the relationship between economic policies and natural incomes. If the valuation of environmental services and natural resources is based on units that are not comparable to other goods, it is supposed that these services are free.In factoverlooking the value of these services, at the level of decisions, leads to the adoption of unsustainable policies. Thus, the economic valuation of natural resources helps managers plan the protection and sustainable use of the environment and natural resources by raising the quantitative value of ecosystem goods and services.

Evaluating ecosystem services is an effective way for managers to evaluate how costs are allocated to valuable ecosystem services. Given that many natural ecosystem services are not traded in the market and the willingness of people to pay for receiving these services is not clear, methods of valuing natural ecosystem services and goods have always been considered by experts as a central issue in natural resource economics. According to the review of economic valuation methods, it can be said that researchers are focusing on the Contingent Valuation (CV) method due to its simplicity and flexibility to apply. Nevertheless, one of the chief criticisms of the CV method is that results are generally inflated as respondents do not face a real budget constraint and tend to say yes. This often leads to loss of validity and exaggeration. Therefore, the use of Choice Experiment (CE) and Benefit Transfer methods are preferable. The benefit transfer method is an effective method for developing countries. In fact, this method will be suitable when there is sufficient time to conduct economic evaluation or studies on the cost of environmental damage. Although the purpose of economic valuation, regardless of how it is done, is to assist decision makers in implementing public policy and environmental and natural resource planning, However, due to the new approach of the selection test method and its optimal ability to evaluate ecosystem services, it is suggested that more studies be carried out with this method. Therefore, due to the importance of economic valuation of ecosystem goods, the scientific community in Iran should continue further research on economic valuation. These studies can help shift from hypothetical to real markets and highlight the vital role of ecosystem services in society.

Land suitability is the coordination of land characteristics with existing land use that aims at development without degradation, so that the natural talent of the land along with economic potential determine the type of land use (Babapour & Khorasani Alamdari, 2013). The importance of assessing land status and its potential becomes apparent when, if land potentially lacks potential for a particular use, its implementation would lead to environmental degradation, even in spite of the socio-economic need for that use (Brazier & Greenwood, 1998). Also, the challenge in the field of sustainable agricultural development is the issue of food security, whose importance, due to the growing trend of population in the country and increasing food needs, doubles (Halabian & Ismaili, 2018); Therefore, in this regard, it is necessary to evaluate the ecological potential for agricultural use based on the correct principles, patterns and instructions, compare it with current uses, and determine their compliance and non-compliance in order to use it properly based on the natural capability of the region and prevent further degradation and erosion. It seems (Kamyabi va Khosh Agha, 2017). The purpose of this study was to investigate the spatial status of rainfed arable lands in the Chehelgazi Sub-basin according to the proposed models and instructions and to use the GIS in order to assess land suitability, provide planning models and make management decisions in the field of development, in line with natural resources and sustainable agriculture, and in accordance with the reality of agricultural system in Iran.2-

In this study, first, a map of rainfed arable lands under the Chehelgazi Sub-basin was prepared and adapted according to the proposed patterns and instructions in the spatial adaptation of arable lands. Finally, using GIS software, the status of alignment or non-alignment of agricultural lands in the region with the proposed patterns and instructions, as well as the dominant cultivation of the region was determined. The map and percentage of agricultural lands were also determined.3-

After examining the spatial status of arable lands in Chehelgazi sub-basin according to the proposed patterns and instructions in the field of adaptation, the results show that, in terms of slope status, 64.15% of the total land is in line with the proposed instructions, on a slope of less than 16%. In terms of altitude, 37.78% of the lands with a suitable slope are located at the proposed height. In terms of river condition, 29.43% of lands with suitable height and slope are located at a suitable distance from the river. In terms of radiation status, 8.29% of the lands in line with the proposed slope and height are located in the flat, north and west directions. In terms of rainfall status, the total area of ​​the basin is in line with the proposed instructions in rainfall of more than 350 mm.In terms of communication status, most of the lands are located at a direct distance of less than 10 km. Finally, out of the total area of ​​31117.94 hectares of rainfed arable lands of Chehelgazi Sub-basin, assuming low impact on the slope or sunshine, 29.43% of the total lands have the necessary conditions in terms of slope, height and distance from the river and road.4-

The results show that the the Chehelgazi Sub-basin is generally in poor condition in terms of the development of rainfed agricultural land use; therefore, considering the importance of adapting the user to the relevant goal in order to increase proper performance and increase production, it is suggested that the patterns and guidelines introduced in this research for principled planning and correct decision making as well as training, promotion and implementation of useful projects related to land management and prevention of the destruction of natural resources and the environment should be considered and developed based on the reality of the agricultural system of Iran.