فهرست مطالب mohammadjavad ghanei bafghi

Given the abundance of desert and desert areas in Iran, it is necessary to recognize the basic capabilities of these areas to eliminate restrictions, prevent migration of people and achieve their sustainable development. KhoroBiyabank region is the tourism hub of the central desert of Iran. The aim of this study was to identify the most suitable place for ecotourism in Khorobiabank city and to identify the potential of the region in the field of various ecotourism activities. 30 questionnaires were completed by experts in this field and its results were used using hierarchical analysis in Expert Choice software to achieve the set goals. The results showed that the six criteria studied in order of importance are: 1- biological environment, 2- special values, 3- physical environment (geology and geomorphology), 4- socio-economic and political environment, 5- physical environment ( Climate and climate) and 6- Cultural environment criteria, the options of each criterion are Iraj village for the first criterion, Egypt village for the second and third criteria, Khor city for the fourth criterion, Iraj village for the fifth criterion and again Khor city for the sixth criterion.

The implementation of combating desertification projects can not be considered apart from their socio-economic impacts. In recent years, many combating desertification projects such as mulching, planting resistant species and etc., have been implemented in the country. Many combating desertification projects have been implemented in Yazd province, including Meybod-Ashkezar. The purpose of this research is to investigate the impact of the implementation of combating desertification projects from the peoples’ point of view. To conduct the present research, the sample size was calculated using Cochran’s method, 286 samples. The questionnaire was designed based on the five-choice Likert scale and several items including 6 economic items, 5 social items and 7 management items were considered in the questionnaire and were randomly completed by people. Then, the priority of each item was checked using Friedman’s non-parametric test. The research results showed that the economic criteria, the effect of the implementation of afforestation and Meybod wind canal projects, reducing damage to agricultural lands and products with an average rating of 4.82, was placed as the first priority of people’s evaluation. In the next ranks, the reduction of damage to residential and industrial lands and the increase of agricultural and horticultural production per unit area were evaluated with an average 4.06 and 3.77, respectively. Regarding the people’s attitude towards the plan, the results showed that the people’s willingness to participate in the maintenance of the plan with an average rating of 3.30 is the first priority. Regarding the Peoples’ point of view to projects, the results showed that the people’s willingness to participate in the maintenance of the plan with an average rating of 3.30 is the first priority. Regarding the management criteria, reducing the influx of sands, reducing the production of dust and local storms, and Reduction of agricultural crop pests with average 5.52, 5.37, and 4.43 were placed in the next priority of public evaluation. Therefore, the positive effect of desertification projects has been well felt by the residents of the region, and it is appropriate to take measures to bring closer the officials of this sector and the local community for the purpose of better protection of natural resources.

Drought is a temporary disorder whose characteristics vary from region to region, therefore, it is not possible to define a complete and absolute definition of drought. Drought is one of the most important natural disasters that can occur in any climate regime. Since drought is unavoidable, it is important to know it in order to optimally manage water resources. Drought prediction can play an important role in managing this phenomenon. In other words, recognizing and predicting this phenomenon is one of the topics of interest for scientists who are interested in solving the problem of water shortage. More than 80% of Iran's area is covered by arid and semi-arid regions and lack of rain is a predominant phenomenon in this region. So far, several methods have been proposed to predict drought. Each method offers different results in specific conditions.  Therefore, identifying the best method for predicting drought in the climatic conditions of central Iran is essential. 

In this research, in order to introduce a suitable method for predicting drought for the next month, four methods of artificial intelligence including Deeplearning (using the Alexnet network, one of the convoluted networks), K nearest neighbor algorithm (KNN), multi-class Support vector machines (SVM-MultiClass) and decision tree have been used. Monthly rainfall data from 11 syntactic stations of Yazd province during the 29-year statistical period (1988 to 2017) were used as experimental data. Standardized precipitation index (SPI) was calculated to indicate drought status in terms of severity and duration on 1, 3, 6, 9, 12 and 24 month time scales. Precipitation data was used as neural network input and SPI classification as network output and 80 percent of the data was used for training and 20 percent for testing the networks.In this study, the Recurrence Plot method was used to interpret the time series to convert these series into images and RG and B pages were created. To convert rainfall data into photos at 1, 3, 6, 9, 12 and 24 month time scales, photo layers were created using a standardized rainfall formula, and by merging these three output layers into colored photos and black and white photos were obtained. Using three pages created in MATLAB software and merging them, the output was in the form of a photo, which was placed as the input of the Alexnet network. Combination of Recurrence Plot to create images and deep learning network for classification of drought data has been used for the first time in this research. To evaluate the effectiveness of the classification strategy, standard criteria of accuracy, micro-F1 and macro-F1 were used.

The results showed that all networks were able to predict drought. However, on short time scales such as 3 and 9 months, the accuracy assessment criteria for some classes are zero and the methods of learning from these classes are practically ignored due to their lack of data. But on a larger time scale, this issue has been addressed and the data of those classes are well categorized. Deep learning network with image input could not predict well in the short term due to lack of data, but in the long term due to increased data has improved its performance and had the best performance. The SVM method at different time scales has shown unreliable and variable behaviors that can not be said to be a suitable method for predicting drought at different time scales. Decision Tree and KNN methods have been able to predict drought better in the short term than in the long term. The two methods have been closely related. .Based on the Deeplearning network macro-f1 evaluation criterion, the one-month time scale with 22.71% was the most inefficient method and the Decision Tree with 64.65% was the most efficient method, But with the increase in time scale, the Deeplearning network improved its performance, so that at the 24-month time scale with 65.35%, the best performance for the Deeplearning network followed by the SVM-MultiClass network with 57.40%. For future research, it is suggested that Decision Tree and KNN methods be used to predict short-term drought. In this study, with increasing the time scale and increasing the data used, these two methods have lost their effectiveness compared to the short term.
 

In today's world, population growth and industrialization, regardless of the potential of the lands, have put pressures on the environment, upsetting the balance of nature, creating environmental challenges and affecting the quality of human life today.  Wind breaking is a complex process that takes place in various forms such as excavation, transfer, granulation, abrasion and finally sedimentation. One of the most important environmental crises in arid and semi-arid regions is the destructive phenomenon of desertification and windfall, the occurrence of which is the occurrence of dust storms and the movement of fine dust. The purpose of this study is to evaluate the effects of wind in Reagan County on economic, social and environmental sectors using the DPSIR model. Since wind is one of the major problems in the Reagan area, it is necessary to draw a chain of cause-and-effect relationships involved in the three economic, social and environmental environments of the region.

In order to conduct this research, three indicators were selected from three environments:  economic, social and environmental. Then, the available information was collected from relevant organization of each indicator, they were placed in the DPSIR framework. And then was shown a chain of cause-and-effect relationships in relation to those indicators.

The results of the research indicate that the damage to the agricultural sector due to the dust phenomenon has had an upward trend from 2007 to the first 6 months of 2018. In the social sector, according to the suspended particle measuring device (pm) in the two months of April and May 2018, there were few clean days and according to the statistics of the health center, the number of people referring to medical centers due to respiratory problems has been high. Also in the field of environment, in Reagan area, an area of 158,200 hectares of critical hotspots was identified with great intensity and has affected the biodiversity of the region, especially Persian Kahour trees.

The result of the research showed that the wind phenomenon in Reagan area with improper human management and exploitation of the environment as a positive feedback has fueled this phenomenon and has created adverse effects such as dust storms.

Salinity is an important environmental stressor that affects plant growth. In saline soils, plants are subjected to some toxic ions and water shortages. Crop residues are considered major organic agricultural wastes that can be useful for the soil and the plants. Organic matter has positive effects on the soil's physical and chemical properties. Converting agricultural wastes to Biochar is an appropriate way to achieve a valuable amendment. Biochar is a carbon-rich decomposition-resistant product obtained by heating biomass in an oxygen-free chamber, being used for carbon sequestration by applying large amounts of carbon.Moreover, as a highly stable residue in the soil, it has been proven to store atmospheric carbon for hundreds to millions of years. According to different studies' findings, the application of Biochar can increase the soil's water-holding capacity, microbial activity, and nutrient availability. However, its effect on soil properties depends on its source and the extent of its application.  As the dependency of Biochar's effects on saline soils' properties in such soils has been under-researched, this study attempted to investigate the effect of palm-tree residues' Biochar on the physical and chemical properties of saline soils.

the required Biochar was prepared from palm trees' residues at 500°C under limited oxygen conditions. This experiment was conducted in a completely randomized design with three replications. Biochar was mixed at 0, 0.5, 1, 1.5, and 2% rates with saline soils and incubated with humidity at field capacity for 90 days. Then, the samples were air-dried, and some physicochemical characteristics of the soil were determined. Moreover, the comparison of the means was performed using Duncan's test at a confidence level of 95%.

The results showed that applying the Biochar obtained from palm trees to the soil significantly increased the soil's chemical properties such as electrical conductivity (EC), organic carbon, calcium, magnesium, sodium, and potassium while decreased the sodium adsorption ratio (SAR), and the sodium/potassium ratio. Moreover, while adding Biochar to the soil increased the porosity by 10% and reduced the bulk density by 4%, treatments did not significantly affect pH, available phosphorus, field capacity, and the soil's particle density. Furthermore, adding 2%, Biochar increased calcium concentration in soil by 2.08 times compared to that of the control treatment. It was also found that the changes in the soil's magnesium calcium were similar and that using more Biochar increased magnesium concentration by 2.88 times compared to what was observed in the control treatment (more than the control treatment). Therefore, the study's findings indicated that the available potassium concentration in soil increased significantly following the application of different biochar levels. It was also found that compared to the control treatment, the amount of potassium increased about 12.33 and 18.79 times, respectively, with an increase of bio-dates in 1.5 and 2% treatments. Moreover, the highest increase in sodium was related to applying a 2% biochar date, which was 49.35% more than that of the control treatment. Finally, using 2% biochar reduced the sodium adsorption ratio by 7.6%.

This study showed that palm trees' Biochar improved some saline soil parameters such as SAR, organic matter, and nutrients. Increased EC is due to increased sodium as a destructive factor and other cations such as calcium, magnesium, and potassium as helpful plant nutrients. On the other hand, the sodium to potassium ratio was decreased. Moreover, it was suggested that the potassium was increased more than sodium, and that was why the index was improved. According to the study's findings, Biochar increased porosity due to its high specific surface area.Moreover, using 2% biochar treatment increased the saline soil's organic carbon by approximately three times. Due to the Biochar's low specific gravity, it can be expected that the soil's density is decreased by applying Biochar in soil. However, the increase in Biochar did not significantly affect the soil's particle density, which is probably due to the low weight of biochar particles versus the high weight of the soil's minerals and the amount of Biochar applied. It seems that applying Biochar as an amendment can improve the saline soil's properties and that the size of biochar particles may have different effects on changing the soil's properties, which can be investigated in future researches.

Proper management and use of agricultural waste is one of the important strategies for sustainable development of agriculture in Iran. Given that most of the country is covered by arid and semi-arid lands, rapid decomposition of organic matter and its deficiency in soil is one of the problems of agriculture and natural resources, preventing the waste from being burnt and making optimum use of it can both compensate for the lack of soil organic matter and reduce environmental pollution. Considering the high durability of biochar in soil, a useful strategy for organic waste management is use of biochar on agricultural land to provide organic matter for plant growth and as a modifier to improve soil properties. Due to the expansion of pistachio cultivation in central Iran, pruning of its branches results in considerable amounts of waste annually, which is usually burned to prevent pest spread. The aim of this study was to investigate the effect of pistachio waste on growth characteristics of safflower.

In order to investigate the effect of biochar on growth characteristics of safflower, an experiment was conducted in a completely randomized design with three replications. Biochar treatment prepared from pistachio wood at 500 ° C under low oxygen conditions was studied at four levels of 0, 1, 2 and 4%. by weight. At the end of the growth period, plants were collected and leaf area, leaf length and width, number of yellow, green leaves and total, internodes distance, fresh and dry weight of shoot and root, stem height, germination time from planting, relative leaf water content, Ion leakage percentage, leaf chlorophyll index, sodium, potassium and phosphorus concentration and sodium to potassium concentration ratio were measured. Data were analyzed in SAS software and mean comparisons were made using Duncan test at 5% level.

The results showed that the effect of biochar treatment on leaf area, fresh and dry weight of plants, root dry weight, plant height, leaf chlorophyll, ion leakage percentage, leaf relative water content, sodium, phosphorus and potassium concentration and sodium to potassium concentration ratio were significant. However, the effect of this treatment on leaf length and width indices, number of green and yellow leaves and their total, root fresh weight, internodes distance and germination time was not significant. Mean comparison showed that the highest leaf area (413.25 mm 2), root dry weight (1.17 g), fresh weight (3.79 g) and plant dry weight (0.89 g), plant height (0.25 g). 52 cm), relative leaf water content (89.2%), potassium concentration (2.81%) and phosphorus (0.24%) were obtained at the highest biochar level (4% by weight). Also, the highest leaf chlorophyll index content (53.8%) was observed in one percent biochemical treatment and the highest ion leakage (81.75%), sodium concentration (3.35%) and sodium to potassium ratio (1.63) were observed in control treatment. .

This study showed that the use of biochar can improve the morphophysiological properties and concentration of essential elements such as phosphorus and potassium in the aerial parts of safflower, and in this regard, the amount of biochar consumption is important. According to Biochar analysis in terms of nutrients and carbon content, it seems that Biochar application may improve the nutritional status of these elements in soil and also increase Soil moisture storage capacity due to high content of Biochar organic carbon, it Provide a more suitable environment for plant growth and thereby improve growth indicators. Therefore, if the pistachio and other agricultural products waste are used properly as biochar, compensates a part of the lack of organic matter in the soils of dry areas and environmental pollution can be prevented.

As a serious threat for human societies, especially in arid and semi-arid regions, dust storm is an irreversibly destructive phenomenon with irreparable effects. Being caused by dust storms and the movement of fine soil particles, desertification and wind erosion are two important environmental crises in arid and semi-arid areas which may bring about some social and economic consequences including an increase in migration of people, especially the elites, unemployment, sharp reduction of revenues in various urban and rural jobs, decrease of crops production, tourism downturn, disruption of air transportation, closure of training and recreation centers etc. In this regard, considering the detrimental effects incurred by dust storms on the health and economy of a society and the climate change, it should be noted that understanding the nature, origin and effects of dust storms could play an important role in identifying and setting its control methods. This study, therefore, sought to investigate the causal relationships between the influential factors on dust storms as identified by the DPSIR model, weighing the identified criteria and indices by the experts via the AHP technique, and ranking the specified strategies by TOPSIS in Rigan city, Kerman province.

Based on the review of the related literature and the data collected form field observations, the cyclic causes of wind erosion were examined, using the DPSIR framework. The criteria and indices identified by administering a standard questionnaire were confirmed by experts (selected from those working in departments of Environment, Natural Resources, Agricultural-Jahad, Environmental Health, Urban Planning, and some University lecturers and faculty members). Validity and reliability of the data were measured by SPSS software. The criteria selected for analyzing the data are the same as the four main elements of the DPSIR model: the driving force, pressure, state, and effect, each containing a number of indices which are as follows: the driving force indices include the population growth, inward immigration, land use, natural resources management, increase in water demand, public awareness and, urban development; the pressure indices are health, security, immigration, employment status, income status, investment, agriculture, and the environment; the state indices comprise of the land use change,  environmental degradation, utilization of water resources, rising diseases, inward immigration, social welfare decline, destruction of agricultural products, soil degradation, and air pollution; the effect indices include Destruction of cities beauty, immigration from villages, rise of respiratory diseases, destruction of natural resources, expert migration, landform change, increase of Particulates, drought, and the downfall of agricultural productivity. The criteria and indices were then weighted by AHP method through Expert Choice Software and finally, the proposed strategies were ranked according to TOPSIS method.

Having applied DPSIR model, the cycles of the influential factors on dust storms were examined, followed by the weighing of the identified criteria, and the indices confirmed by the experts, using AHP technique through the Expert Choice software. The results of the study indicated that the driving force with the weight of 0.379 was, from among the four criteria, ranked first, followed in turn by pressure with the weight of 0.257, the state with the weight of 186, and the effects with weight 160/0. On the other hand, the findings showed that out of the final indices weights, Population growth with the weight of 0.75 ranked the first, and increase of water demand, and the environment were ranked second and third with weights of 0.67 and 0.059 respectively. Finally, the TOPSIS method was used to rank the responses whose results were turned out to be as follows: 1-Proper management of water resources (592/0); 2- Preventing dam construction (5/175); 3-Pebble mulch (0, 75%); 4. Not applying inefficient models, especially in agriculture (519/0); 5. Enforcing existing laws regarding environmental protection (494/0); 6- conducting seedling operations (427/0); 7- Increasing public awareness (0.415); 8. Mulching (0.440) 9. Constructing sediment trap (0.339); 10. Using Biological wind-break systems (0.346).

As a potential threat to human survival, dust phenomenon is considered as one of the most important environmental crises in arid and semi-arid regions. Kerman province includes more than 17.6 percent of the Iran’s desert lands, the most significant part of which are located in its eastern regions including Rigan, Fahraj, Norshshir, and Bam that comprise of 1302,000 hectares desert lands, accounting for more than 55 percent of the province's total desert areas. Due to such causes as frequent droughts, seasonal drying of rivers, lack of suitable vegetation, and high evaporation, Reagan city experiences a large number of dust storms annually. In present study, DPSIR model was found to be effective in identifying, from among the various factors suggested by the experts, the most important factors affecting local dust. It is consistent with the results found by Khatibi et al (2015) who emphasized the necessity of using this model in solving environmental problems. In the same vein, Salehi and Zebbardast (2016), with the cooperation of the Tehran environmental organization, identified and categorized the factors affecting air pollution in Tehran city in the current state of affair, using the causative-effect chain model (DPSIR). The findings of the current study showed that the driving force with the weight of 0.397 had the highest weight, followed by the pressure (0.275), state (0.186), and effects (0.160) respectively. In fact, it was found that accurate identification of the driving forces in each region could be a great help in solving their problems which is consistent with the results found by Jafarzadeh Haghighi Fard et al. (2013). As for the final weighting, the findings suggested that population growth with a weight of 0.5 was the most important index, a fact that corresponds with the findings reported by Shahi et al. (2018) and Nemati and Sardari Leather (2017). The results of this study also indicated that the proper management of water resources in different areas and the utilization of nature elements were of high importance.

Wind erosion is one of the main factors of soil degradation and air pollution. Roughness by creating an obstacle to wind flow, decreases wind speed and consequently reduces the amount of wind erosion, hence its measurement in wind erosion is important. Roughness is one of the most effective factors. In soil erosion by water and wind by accuracy measurement of soil surface roughness, can be used by them as indicators of ecosystem healthMethods for measuring of soil surface roughness using Roughness meter can be classified into two large classes. Contact methods which mechanical devices are directly in contact with the soil. Non-contact methods that the measuring instruments are far away from the soil surface and do not touch with surface of the soil.
Pin meter includes a row of pins with equal distances which is placed in a frame that can be placed according to high and low levels of roughness. Yazd - Ardakan Plain with a total area of 7,15950 square kilometers Is located in the northern part of Yazd province.and Is located in   Longitude 748337 to 296893 East and in latitude 3559572 to 3566743 North


After determining the studied area, 10 randomly selected samples were selected in the Yazd-Ardakan Plain and the sampling was carried out in both methods. Roughness data of soil surface were taken randomly by using laser distance measuring and pin meter in 10 transects. In each transect, roughness height was measured of length 50 cm. The distance between the pins in pin meter method is 2 cm and measuring distance in Laser Distance Meter is 2 cm. Thus, the height of the points was recorded separately by both methods, in each transect,
Pin meter
In this way, with the help of pins on the soil surface, Height of pebbles, is measured at several points to the base level. That way, Roughness altitude information was recorded. According to the definition, this index (roughness) is the standard deviation of the height of the points, which is obtained using the following equation:That in, SD, , , and n are respectively, standard deviation of points Height, the height of each of the points (mm), Average height and the number of sample points in the transect

Laser This method, measures roughness using the laser meter model (X310) from the Leica Company. The laser meter is A small and handy device, that calculates the exact distance, by using laser technology and by calculating round-trip time of laser lightworking method is as follows: The laser beam is emitted to the target and the calculation is performed with an error of less than 3 mm. Thus, the height of the points is measured at 50 cm up to the base level

(Result)
The maximum amount of the calculated average by the method of the pin meter and laser distance meter is 2 and 1, respectively. The minimum amount of calculated average for both methods is in the point 10. The highest and lowest amount of standard deviation derived from laser distance meter and pin meter methods are for points 2 and 10 respectively.

In Table 1, are presented the results of the comparison of the two methods of pin- meter and Laser rangefinder. As can be seen: 
Table (1): Statistical comparison of roughness index of two methods at 2 cm distance


t

Degrees of freedom


Mean ± Invalid Criterion


Groups2cm

characteristics


438/1
24
70/1±83/21
pin meter
Point 1
The roughness index

28/2±66/23
Laser

120/1-
24
37/2±50/23
pin meter
Point 2

53/2±50/22
Laser

646/0-
24
99/1±58/17
pin meter
Point 3

30/2±87/16
Laser

040/1-
24
1±50/10
pin meter
Point4

08/1±08/11
Laser

924/0
24
91/1±62/19
pin meter
Point5

69/1±50/18
Laser

793/0-
24
30/1±66/9
pin meter
Point 6

54/1±37/10
Laser

221/1-
24
10/1±29/14
pin meter
Point 7

95/0±12/15
Laser

175/0-
24
11/1±79/11
pin meter
Point 8

16/1±12
Laser

135/0
24
10/1±9
pin meter
Point 9

23/1±12/9
Laser

809/0
24
59/0±04/7
pin meter
Point 10

09/1±83/7
Laser



Figure (1): shows the Matching charts, two methods of laser distance meter and pin meter.
These two methods are Good match to all points, But this match, have more matches at points 2, 3, 5, and 6 Compared to other points.

ure (1): Charts for the matching of the two methods at the sampling points.
Discussion and Conclusion The Calculated Statistical indicators by the two methods, are almost the same at all harvested points. The results of the t- student test also showed that, the mentioned two methods, in all sampled points, there is no significant difference at 95% level. Which indicates the ability of two devices For measuring roughness Which, depending on the conditions, can replace each other.the results of this study, with the results of the studies of Zhang et al. (2012), that investigated the methods of measuring the roughness of the soil to provide a Universal and comprehensive method, and concluded that the best method for determining the roughness is laser scanning method and followed by it is pin meter method. labideh (2015), measuring the aerodynamic roughness length of the harvested soil using اa pin meter. It was concluded that a pin meter could be used to estimate aerodynamic roughness. Which is accommodation with the results of our studies.so it may be concluded that so it may be concluded that using the pin meter method as a traditional tool in pavement roughness measurement, similar results will be obtained using laser method. Therefore, according to the cost of laser distances, pin meter method can be used with similar results.