جستجوی مقالات مرتبط با کلیدواژه « رگرسیون » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Water and soil resources are the most important factors in the production of energy needed for humans. In order to protect water and soil resources, different management methods are applied according to the conditions of each region. The participation of all watershed residents and changing their behavior will be one of the basic tools of any sustainable management plan. Since norms affect the participation and behavior of local communities; the purpose of this research is to investigate the norms affecting the participation and behavior of watershed residents in the implementation of water and soil protection projects.

This research was done by descriptive-analytical method. For this purpose, Benchele watershed in Ravansar city was selected as the study area. A questionnaire was used to collect field data. The statistical population of this research was all heads of households of watershed residents in this area, numbering 569 people. Among them, using Cochran's formula and matching with Morgan's table, 229 people were directly questioned by simple random method and completed the questionnaires. Spearman's correlation tests and stepwise regression were used to analyze the data. The focus of the questions was focused on three independent variables (legal norms, normative beliefs and participatory norms) and two dependent variables (participation and behavior of watershed residents). In the data analysis section, descriptive statistics (frequency, percentage, average, etc.) and inferential statistics (Spearman's correlation test and step-by-step regression) were used. Before performing the regression to check the collinearity test, the degree of collinearity of the independent variables was examined. Also, standardized beta values were used to determine the relative importance of each independent variable in the participation and behavior of watershed residents.

The mean ranking results of each of the items of legal norms affecting the participation and behavior of watershed residents in water and soil protection projects show that the items of "people who damage water and soil resources should be fined" and "existing laws are useful to convince people in the field of protecting water and soil resources" have the greatest and least impact on legal norms with the participation and behavior of watershed residents, respectively. The results of the mean ranking of each of the items of normative beliefs affecting the participation and behavior of watershed residents in water and soil protection projects show that the highest ranking average is related to "society expects me to take care of water resources" and do not destroy the soil" and the lowest rating is related to "experts expect me not to destroy the soil". The mean ranking results of each items of the participation norm effective on the participation and cooperative behavior of watershed residents in water and soil protection projects show that from their point of view, the item "voluntarily participates in the construction of sediment dams" and the statement "I am willing to pay for the implementation of water and soil protection projects", respectively, have the greatest and least impact on the participation and behavior of watershed residents in water and soil protection projects. The results of Spearman's test showed that all three independent variables of legal norms, normative beliefs, and participatory norms have a positive and significant relationship with the participation and behavior of watershed residents. Also, the regression results and beta values showed that, respectively, the indicators of legal norms, normative beliefs, and participatory norms had the highest priority in the participation and behavior of local communities.

The results showed that about 66% of watershed residents have moderate to low participation and cooperative behavior. The mean ranking results of each of the watershed residents' participation and their behavior to participate in water and soil protection projects show that the watershed residents' care of the projects after its completion, participation in the mortar dam construction project Mortar-cement dam has the greatest effect on their participation and cooperative behavior. Also, the items of participation in the construction of gabion dam project and the desire and intention of watershed residents towards water and soil protection had the least effect on the participation and behavior of watershed residents towards water and soil protection. In general, paying attention to the norms, beliefs, behaviors and attitudes of local communities should be on the agenda of managers and planners to pave the way to protect water and soil resources.

Organic carbon is a crucial parameter for assessing soil fertility, productivity, and quality, particularly in dry and semi-dry lands. Rangelands cover approximately 50% of the Earth's land area and store over 33% of the global carbon reservoirs. Considering rangelands as significant terrestrial ecosystems and natural carbon reserves, understanding and quantifying carbon sequestration in these ecosystems is of great importance.

This research focused on rangelands in the southern domain of Damavand mountain. Four height groups were selected based on geographic conditions, regional plant cover, and slope. Each height group represented a different elevation range: group 1 (2504-2664 m), group 2 (2730-2896 m), group 3 (2923-3050 m), and group 4 (3119-3545 m). Soil sampling was conducted at two depths (0-15 cm and 15-30 cm) using 13 random points within each height group. Soil organic carbon and bulk density were measured, and carbon sequestration was calculated at the hectare level by multiplying organic carbon and depth. Regression equations and response surface models (RSM) were developed using design software to predict carbon sequestration and compare their accuracy.

Analysis of variance (ANOVA) showed that only sampling depth significantly affected soil carbon sequestration, while sampling height and the interaction between depth and height had no significant effect. Carbon sequestration was higher at a depth of 15-30 cm compared to 0-15 cm. Among the height groups, group 4 (3119-3545 m) exhibited the highest soil carbon sequestration, followed by group 1 (2504-2664 m). The highest carbon sequestration (6046.54 g/m2) was observed in height group 4 at a depth of 15-30 cm, while the lowest (2250 g/m2) was found in height group 2 at a depth of 0-15 cm. The quadratic model of the response surface methodology demonstrated higher accuracy (R2 = 0.7363) in predicting soil carbon sequestration compared to the linear model (R2 = 0.6014) and the less accurate regression model (R2 = 0.428). The response surface method successfully optimized the inputs, suggesting a height of 3500 meters and a specific depth to achieve a soil carbon sequestration amount of 8088.117 g/m2 with a satisfaction level of 0.976.

The response surface methodology (RSM) proved to be a valuable tool for predicting and optimizing soil carbon sequestration in rangelands, considering different sampling depths and heights. It can also be utilized for predicting various parameters in rangeland science, such as above and underground biomass volume and plant species coverage. RSM offers a novel approach for understanding and managing rangeland ecosystems.

Soil is the most important component of rangeland ecosystems and by preserving it and its characteristics, In the present study, the amount of potassium and phosphorus in the soil of Ghoshchi rangelands of Urmia located in West Azerbaijan province from 2019 to 2021 under the influence of grazing and grazing conditions was investigated. In addition, the development and evaluation of an adaptive fuzzy-neural inference model (ANFIS) was presented in order to predict the amount of potassium and phosphorus in the soil and compare its results with the regression model. The mean squared error (RMSE) and the coefficient of explanation (R2) were used to evaluate the regression and inference models. The results of analysis of variance showed that different years and conditions under confinement and grazing had a significant effect on the amount of potassium and phosphorus in the soil, but their interaction was meaningless. The highest amount of soil potassium is related to the year 2021 and the conditions under grazing. While the highest amount of soil phosphorus was related to 2020. In the phosphorus factor modeling section, the ANFIS model with higher accuracy (R2 = 59.5) and less error (RMSE = 0.087) than the regression model (R2=0.38) with more error (RMSE = 0.089) was able to determine the amount of P to predict. Regarding potassium factor, ANFIS model with higher accuracy (R2 = 0.62 and less error (RMSE = 0.017) than regression model (R2 = 0.42) with more error (RMSE = 0.097) was able to measure soil potassium.

One of the most important components of the water requirement is the reference evapotranspiration (ET0), which is one of the most important components of the hydrological cycle that dependent to climate variables such as: wind speed near ground, air temperature, radiation Solar and relative humidity. Consequently, ET0 values can be estimated and simulated using meteorological models based on physical equations or the empirical relationship of meteorological variables. There are various methods for calculating reference evapotranspiration, each of which has different results depending on the different meteorological assumptions and data they consider. Worldwide, the FAO model is used as a reference method for estimating evapotranspiration. But this model requires a lot of input information such as: max air temperature, min air temperature, max relative humidity, min relative humidity, solar radiation and wind speedy, which sometimes is not possible to access all the information especially in arid and semi-arid regions like Iran and this necessitates the selection of models with less input data and appropriate accuracy. In this study, seven reference evapotranspiration estimation models that require less data including: Kimberley penman, FAO radiation model, Hargreaves Samani, Makkink, belany Kridle FAO 24, Turc and Peristly Teylor, were evaluated than the FAO model. The reference evapotranspiration was then modeled using Gene Expression Programming Model (GEP) and the results were compared with experimental methods. Multivariate regression was used to determine the model input patterns and to investigate the effect of climatic parameters on ET0. Multivariate regression in fact expresses the relationship between several predictor variables with the response variable in question. Such models have assumptions. The assumption that distinguishes multivariate regression from simple regression is that: 1) The number of predictor (independent) variables in the regression should be less than the number of observations. 2) There is a complete linear correlation between predictor and response variables. If the two assumptions are violated, the regression equation cannot be estimated. Gene expression planning is a combination of the GA and GP methods developed by Ferreira in 1999. In this method, linear and simple chromosomes of constant length, similar to the genetic algorithm and branch structures of different sizes and shapes, are combined, similar to the decomposition trees in genetic programming. In short, it can be stated that in this way the genotype and phenotype are separated and the system will be able to enjoy all the evolutionary benefits. Although the phenotype in GEP is similar to the branched structure of GP, the branched structure in GEP, also called tree expression, represents all independent genomes. In summary, it can be briefly stated that in GEP refinement takes place in a linear structure and then expressed as a tree structure, which will result in only the modified genome being transferred to the next generation. It does not need heavy structures to reproduce and mutate. For this purpose, daily maximum and minimum temperature, maximum and minimum relative humidity, Wind Speed and sunlight hours of the 35-year period (1983–2017) were used for the Aligodarz Synoptic Station. 70% of the data were used for training and 30% of the data were used for testing the model. Also, two types of mathematical operators including four-element operations and default model operators were used in the GEP method. The results showed that the Kimberley penman and FAO radiation models are more accurate than the other experimental models. Multivariate regression results showed acceptable modeling accuracy with R2 = 0.95. The analysis of model coefficients showed the highest effect of maximum Temperature with a coefficient of 0.58 on reference evapotranspiration. After that, respectively, wind speed, sunshine hours, minimum temperature, maximum and minimum relative humidity have the most influence on prediction and estimation of evapotranspiration. Therefore, six models for model inputs were determined. In Gene Expression Programming, model 2 with model default operators with RMSE = 0.843 and R2 = 0.932 at training stage and RMSE = 0.76 and R2 = 0.941 Performed better in the test phase. Comparison of the reference evapotranspiration estimation models indicated that the Gene Expression Programming model outperformed the other models. The results of this study showed that the GEP model has acceptable ability to estimate reference evapotranspiration under Aligodarz climatic conditions and introduced it as a usable model in this field.

Recognition of influential factors is required for management of gully erosion. The purpose of this study was gully classification and determining the most effective variables on the soil loss in the Fath Abad, Buin Zahra in Qazvin Province. Twenty four gullies were selected, surveyed and recorded by GPS, and their morphologic characteristics including length, width and depth were measured. Surface soil samples were taken from headcut and were analyzed for texture, EC, pH, organic matter and some anions and cations. Indices related to the form of watershed were extracted from the digital elevation model which derived from UAV image processing. The Ward method was used for cluster analysis of gullies and their properties. Factor analysis, the correlation between factors and multivariate regression were performed to determine important and effective factors on the formation of gullies in SPSS. The results of the research indicated that gullies have vertical headcuts, trapezoidal cross-sections and linear general plans. The average depth of headcuts, depth at 50% of the gully length and the average gully length were 0.4, 0.6 and 25 m, respectively. Soil texture was loam and average soil texture components including sand, silt and clay were 30, 46 and 24%, respectively. The mean EC and pH of the samples was 8 dSm-1 and 8.4 respectively. Linear correlation between the total volume of soil loss and other gully characteristics showed that the average cross section, average gully lower width and upper width, sand content, average gully depth and total length have meaningful correlation coefficients greater than 0.6 at 1% percent level. Also, multivariate regression results showed that the most effective factors on soil loss by gully erosion were saturation percentage, sand content, roundness coefficient, basin length and area respectively, which had more effect on soil loss prediction, which entered into the loss prediction equation.

The base of current environmental problems can be addressed in irresponsible behavior humans around nature. These behaviors appear that are influenced by different factors in the environment. The natural and humanity are bedded of tourism activities. Then maybe behaviors and unsuitable tourism activities have negative impacts and subject to vulnerable them. This research aim to modeling the environmental behaviors of tourists in Birjand city was applied. In the method of this research In order to assess the behaviors of tourists (dependent variable) evaluate the influenced factors such as cognition, satisfaction, theory, and involvement activity (independent variables) using spread the questionnaires among tourist in Birjand natural and cultural attractions. So, after the query of the tourist’s community in Birjand city during the first six months of 1398, the results were analyzed by linear regression with SPSS 13.0 software. Analyze of Spearman and Pearson were appeared the high correlation among the studied factors. In achieved behavior model from regression analysis in tourist’s community showed the cognition of tourists and their satisfaction of tourism places and natural attractions have a high impact on behavior. While the theory and involvement activity have no impact on the behavior model. In this behavior model, the value of R and R2 achieved 0.99 and 0.98 respectively that shown the high prediction power of the model. The others of the result’s research are a significant effect on behavior in gender, age and education levels in the studied tourist community. It seemed the environmental behavior was due to subjectivism factors. So, it needs to execute to improve the responsible behavior in tourists and create the theory and involvement activity, education and promotion measures of the relevant organization.

Due to the negative effects of built up area temperature on energy consumption and human welfare, it is important to mitigate these negative effects by controlling the spatial pattern of green cover and its cooling effect. Therefore, in order to reveal the type and shape of relationship between landscape spatial patterns with land surface temperature (LST) in Rasht, the statistical modeling approach and landscape metrics were used. Land use- land cover and LST mapping were performed using Landsat sensor imagery classification and finally calculated landscape metrics of entered into the modeling process. Validation results showed that although all four linear, logarithmic, exponential and power regression models have good performance in predicting LST through land cover metrics, the highest performance was related to power model. In addition, this models indicate LST is strongly correlated with near distance to green patch and near distance to water patch metrics.

Evapotranspiration is one of the most important components of the hydrological cycle and hydrological models for planning irrigation system water. In this study, the amount of evapotranspiration using the FAO Penman-Monteith for synoptic weather stations of Isfahan and Rasht that located in parts of central and northern Iran in the period 1970-2011. Some of the most important meteorological variables affecting it in the scales of seasonal and annual was calculated. In addition, changes were also analyzed and compared. For this purpose, non-parametric test of Mann-kendall and parametric test of regression was used. The results showed that the annual scale evapotranspiration changes in Rasht, and Isfahan stations had the significant increasing trend (5% level). At these station, the temperature increased and the minimum relative humidity decreased but of course was greater than other variables. In the seasonal scale both positive and negative trends have been observed. Isfahan Station evapotranspiration trends in other seasons except in winter is negative (5% level). Rasht Station evapotranspiration trends in other seasons except in spring was positive (5% level). Among the studied variables, seasonal and annual of number of sunshine hours was increased (level 5%). Maximum and minimum air temperature trend at both stations in the annual scale was increased (5% level). Comparing the results of the review process, potential evapotranspiration and temperature changes by both tests showed contradictory phenomena in the process of evaporation variables in Isfahan Station.

To identify an appropriate index for monitoring and evaluation of drought, rainfall data obtained from meteorological stations of Ilam Province from 2000 to 2011 and MODIS satellite images with 16-day intervals were collected and processed. The Standardized Precipitation Index (SPI) was calculated based on rainfall data; therefore, the rainfall data were used for measuring SPI and satellite images were used for calculating NDVI. Also, the percentages of canopy cover in range types were selected from the information of the National Evaluation of rangelands in different climatic zones. The correlation between SPI and NDVI and also canopy cover and NDVI was examined. The relationship between vegetation index (NDVI) and SPI was determined by regression. The results of SPI showed that in 2000 a severe drought and in 2006 a medium wet occurred in rangelands of Ilam Province. NDVI value variations have as well confirmed it. The results showed that NDVI and life form (annual forb and annual grass) has the highest percentage of correlation. Also examining of result showed that most correlation of SPI and NDVI was in 3 and 6-months intervals. Evaluation of regression models performance in range types described that models in 3 and 6- months intervals was suitable for monitoring drought. The result of regression confirmed that NDVI was an appropriate index for monitoring and assessment of drought.

In western and southern watersheds of Isfahan province, combinations of natural and human factors have caused numerous landslides related damages. One of the main strategies for restricting the damage caused by the landslide is to avoid these regions. For this purpose, it is necessary to prepare precise landslide hazard zonation map for such areas. For this purpose, by aerial photos, satellite images, geological maps and field studies, landslide inventory map was prepared in of the upstream watersheds of Karoon Basin called Marber River Basin with an area of 800 square kilometers. Then, nine factors including lithology, slope, land use, rainfall, vegetation cover, aspect, and lineaments elements such as road, fault and drainages were studied as 54 parameters. To enhance accuracy, speed and ease of analysis, all spatial and descriptive data were interred into GIS and 27466 homogeneous units were obtained by overlapping of the mentioned map layers. Analytical Hierarchy Process (AHP) and Multivariate Regression (MR) were used for multi criteria decision analysis and the results showed that both methods have the same accuracy in the separation of zones (lines) with the specific index of landslide risk.. But AHP approach of regression data, based on total quality index as an indicator of the accuracy of the learning has higher acceptability. This is related to this fact that the method has considered all 54 effective parameters due to the inherent performance of natural phenomena and events involved with the landslide. Based on multivariate regression method, only 30 of 54 variables were significant at 95% and 99% levels and r coefficient of regression equation was 57% which is quite acceptable

Iran is located in dry belt of the earth and always involved with drought in different sections. Drought has already caused many losses to natural plant cover, agriculture and human society. For drought monitoring, we can use some drought indecies. In this research, the Standard Index of Annual Precipitation)SIAP),Sandardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) were used for assessment of drought effects on rangeland plant production. The research area is located in Qom province that contains eight rangeland sites. Plant production and soil factors were measured in rangeland readiness period from 1997-1998 to 2005-2006 annualy. Regression techniques were used between drought indices and total production and also production of different vegetation forms in seven time scales (early March to late July (growth season) and early February to late July (growth season and the previous month), March to June, March to May, March to April and March (start of growth season). The best drought index was then selected based on the highest correlation coefficient and lowest standard error. The result showed that the best drought indices in Qom rangelands are SPI-3, PDSI, SPI-24 and SPI-6, respectively. Also the most significant time step was resulted growth season and specially early stage of growth season.

Spur dikes are used widely to prevent outer bank river bends and path-modification river projects. Local scouring in spur dile nose is due to contraction and strong eddy is among the important problems occurring in designing Super dikes. Experimental study has been done on effect of length and alignment of minor spur dike to reduction of main spur dike scouring. In order to do this study a shorter spur dike was used in two stages، one perpendicular and one angled to flow channel direction. By non dimensional parameters، contraction ratio L/B=0. 3، 0. 35، 0. 4، 0. 45، (L=length of spur dike، B=width of channel)، Length ratio L’/L=0. 5، 0. 6، 0. 7، (L’= length of minor spur dike) and distance ratio x/L=0. 5، 1، 1. 5، 2، 2. 5 have been studied. Scour depths at nose of minor and main spur dike have been measured. Data collection were fitted by regression analysis and a relation for reducing scour depth at nose of first spur dike is provided، with and without minor spur dike.

One of the most important event in hydrology and flood management is a daily flow predication water different characteristic. There are several methods for estimating amount river discharge. One of these methods is nonlinear mathematic model of neural network. Artificial neural network is a useful technique which allows the user to detect nonlinear complex interaction between output and input data without considering natural phenomena. In addition، due to spiral changes in precipitation، complexity is great. Artificial neural network is a flexible method which helps us to distinguish nonlinear relationship between input and output data. The aim of this research was to investigate application of ANN in prediction of daily discharge. Then، estimated data of this method were compared with estimated data of regression method algorithm was used in ANN was Back Propagation and function was sigmoid results showed that estimation of ANN was accurate that regression method. Determination Coefficient between data estimated with ANN and observed data was 62. 94 percent and Residual Mean Square Error (RMSE) and Mean Absolute Error (MAE) of estimated data of this method were 11. 88، 3. 7 respectively. Thus، the neural network model is recommended because its structure is simple، the speed of presses is high and the required data are available.