Accurate assessment of Forest above-ground biomass is essential for sustainable Forest management. Estimation of Forest biomass is necessary for studies such as estimation of greenhouse gases, carbon stored in Forest resources and climate change models. Also, the Forest biomass represents the production rate per unit area. The optical image data of Sentinel-2 satellite was used to estimate the above-ground biomass of the Forest in the area of 285 hectares of the Forests in Ilam province. 124 square-shaped sample plots with a 20×20 m dimension were located on the ground using a cluster method. Some characteristics of a total of 508 trees (both single stems and coppice forms), including the major and minor crown diameters were measured within each sample plot. Depending on whether the trees are single stem and multi-stem clumps, suitable allometric equations were used to calculate the above-ground biomass based on the measured characteristics. Finally, the total above-ground biomass was calculated for all trees in each sample plot. In order to estimate the above-ground biomass, MSI sensor images of Sentinel 2 satellite were used at the level of L2A corrections. Using spectral ratios, vegetation indices were calculated. In the next step, the corresponding spectral values of the sample plots were extracted from the main bands, and vegetation indices. A Random Forest Regression model was used to estimate Forest above-ground biomass. 70% of the samples were used for training the model, and the models were validated using the remaining 30% of the data. The results with R2=0. 80 and RMSE=28. 70 t/ha showed the acceptable performance of model for estimating the above-ground biomass of the Forest. By using the Gini statistic, it was shown that RVI, GNDVI, NDVI, and DVI vegetatuin inices played a larger role in the estimation of biomass. Extended Abstract 1-Introduction Accurate assessment of Forest above-ground biomass is essential for sustainable Forest management. Estimation of Forest biomass is necessary for studies such as the estimation of greenhouse gases, carbon stored in Forest resources, and climate change models. Also, the Forest biomass represents the production rate per unit area. Estimating Forest biomass through direct measurements and cutting and weighing trees in the Forests provides an accurate estimate of biomass, but it is a destructive, difficult, and time-consuming method. Therefore, the use of remote sensing methods is very important in the estimation of biomass. 2-Materials and Methods The optical image data of the Sentinel-2 satellite was used to estimate the Forest above-ground biomass in the area of 285 hectares of the Forests in Ilam province. 124 square-shaped sample plots with a 20×20 m dimension were located on the ground using a cluster sampling strategy. Some characteristics of a total of 508 trees (both single stems and coppice forms), including the major and minor crown diameters were measured within each sample plot. Depending on whether the trees are single-stem or multi-stem clumps, suitable allometric equations were used to calculate the above-ground biomass based on the measured characteristics. Finally, the total above-ground biomass was calculated for all trees in each sample plot. In order to estimate the above-ground biomass, MSI sensor images of the Sentinel 2 satellite were used at the level of L2A corrections. Using spectral ratios, vegetation indices were calculated. In the next step, the corresponding spectral values of the sample plots were extracted from the original bands and vegetation indices. The correlation coefficient between the values of the original bands and vegetation indices with the amount of biomass calculated from the allometric equations in the sample plots was investigated. A Random Forest Regression model was used to estimate Forest above-ground biomass. 70% of the samples were used for training the model, and the models were validated using the remaining 30% of the data. 3-Results and Discussion The results of the descriptive statistics of above-ground Forest biomass measured in 120 sample plots which were calculated using allometric equations showed that the lowest biomass in the sample plots is 0. 61 and the highest is 268. 88 tons per hectare. The average above-ground biomass per tree was measured as 657. 6 and 231. 2 kg in the single and multi-stemmed trees, respectively. The results of the correlation analysis of biomass with the investigated variables showed that among the main bands of the sensor, the red wavelength has the highest correlation (0. 402) with biomass due to the high chlorophyll absorption of green plants in this wavelength. Among the vegetation indices investigated in the research, RVI and NDVI indices have the highest correlation with the Forest above-ground biomass with a correlation coefficient of 0. 529 and 0. 525, respectively. The results of Random Forest Regression analysis to estimate the Forest above-ground biomass with R2=0. 80, RMSE=28. 70 t/ha show the acceptable performance of the model for estimating the above-ground biomass of the Forest. Since in this research, the amount of Forest above-ground biomass of the sample plots is calculated based on allometric equations in a part of Zagros Forests,but these equations are not exactly related to the studied area, part of the model error can be due to this reason. By using the Gini statistic, it was shown that RVI, GNDVI, NDVI, and DVI vegetation indices played a larger role in the estimation of biomass. RVI, NDVI, and DVI indices are calculated using red and near-infrared bands, and since they are influenced by the photosynthetic activity of plants, they are very important in estimating the amount of biomass. GNDVI, which is calculated using green and near-infrared bands, is an indicator of the level of greenness or photosynthetic activity of the plant and is highly sensitive to changes in the chlorophyll content of plants. 4-Conclusion The results of Forest above-ground biomass estimation using Sentinel 2 satellite images and Random Forest Regression method showed that using the non-parametric method of the Random Forest Regression model, which performs a large number of uncorrelated models,it has an acceptable ability to estimate Forest biomass. Also, the findings showed that vegetation indices are more important in the process of Forest above-ground biomass estimation model than Sentinel 2 original bands. The findings of the present research provide the possibility for the managers of Zagros Forests to estimate the Forest above-ground biomass and provide the basis for sustainable Forest management strategies.

This study aimed to estimate the genetic parameters of body weight traits in Markhoz goats, using B-spline Random Regression models. The data used in this study included 19549 records collected during 29 years (1992-2021) in Markhoz goat Breeding Research Station, located in Sanandaj, Iran. The model used to analyze data included fixed effects (year of birth, sex, type of birth and age of dam) and Random effects including direct additive genetic, maternal additive genetic, permanent environmental and maternal permanent environmental assuming homogeneous and heterogeneous residual variance during the time. Akaike (BIC) and Bayesian (BIC) information criteria were used to compare the models and bspq.4.4.4.4 was selected as the best model. The direct heritability values for birth, 3-month, 6-month, 9-month and 12-month weights were estimated to be 0.14, 0.16, 0.08, 0.28 and 0.26, respectively. Genetic correlation between body weights at birth and 3-month, birth and 6-month, birth and 9-month, birth and 12-month, 3-month and 6-month, 3-month and 9-month, 3-month and 12-month, 6-months and 9-month and 9-month and 12-month were 0.22, 0.38, 0.21, 0.56, -0.26, 0.30, 0.62, 0.86 and 0.77, respectively. The highest phenotypic correlation was between the weight of 9-month and 12-month (0.82) and the lowest correlation was between birth weight and 3-month and 6-month (0.12). The results showed that the 9-month weight is a good criterion for selection in Markhoz goats.

Background and Aim: Soil is one of the important natural resources of any country, which plays an important role in preserving the environment and producing food. Increasing and decreasing the amount of total soil nitrogen due to various agricultural methods, the entry of industrial wastewater into water and other factors, leads to microbial contamination of soil, reduced vegetation and deficiency in agricultural products needed by humans. Mapping soil nutrient distribution helps mangers in decisions. Since laboratory analysis of these parameters is time consuming and costly across large scales, attempts have been made in recent years to study soil nitrogen based on remote sensing techniques. In this regard, the present study investigated the applicability of remote sensing predicting soil total nitrogen in the east of Lenjan city. Method: Nitrogen reference points were identified by analyzing 50 Randomly-selected surface soil samples from 0-20 cm depth. Nitrogen of soil samples was measured by Kjeldahl method after drying soil at 25 ° C, passing through a 2 mm mesh sieve and transferring to the laboratory, to compare the final results obtained from field sampling and remote sensing technology. Landsat 8 OLI Satellite Image of 2019 (Path 164/Row 37) was obtained and geometric and radiometric correction were applied. Cloud cover for image provided was less than 10%. To reduce the effect of atmospheric diffusion on the quality of image, radiation and atmospheric correction were performed using the FLASH model. the Landsat-8 satellite image (rows 164 and 37) taken on 15 Sep. 2019 and along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the multiple linear Regression and Random Forest models. Results: The digital elevation map of the area showed elevation values between 1100 and 2050 meters. The slope of the study area was less than eight percent. Numerical values of TWI index near water bodies were found to be 0. 77. DVI and EVI index values increased with increasing vegetation cover. NDVI index showed values higher than 0. 3 and NDWI index as a water index showed a maximum value of 0. 77. The SAVI index showed high differences from areas without cover to sparse cover and areas with strong vegetation. SBI index and SI salinity indices showed very high variability in terms of soil parameters in barren lands. Nitrogen Regression model was built using three indices RVI, DVI and TWI with p-values of 0. 049 and 0. 00, respectively. In the nitrogen Random Forest model, however, plant and soil indices played a more important role in model construction with an of r2 value of 0. 44. Conclusion: Total soil nitrogen in soil parameters showed correlation with density and sand and clay from soil texture and in topographic parameters with elevation and spectral indices with EVI RVI, SAVI, NDWI, NDVI and DVI at the level of 0. 01 and with SI3 of salinity indices at the 0. 05 level. In soil parameters, silt is correlated with sand and clay at the level of 0. 05 and sand with clay as well as density with clay are correlated at the level of 0. 01. The results of this study showed that the topographic condition of the region along with red and near infrared-based indices had a significant role in predicting soil total nitrogen. Results also showed a slight difference was observed between the two models in predicting soil nitrogen.

Background and Objectives: Assessment of suspended sediment load is very important. Water quality and environmental is under impression of sediment load. As well as the design of hydraulic structures and other water supply facilities, watershed management, soil conservation programs and another major problem caused by sediment yield is dependent on the accurate estimation of sediment load. As a direct estimation of sediment load is very difficult and time consuming, so this led the researchers to estimate sediment load as indirect that it is possible to resort to various methods. One easy way to indirectly estimate the sediment load is sediment rating curve. It can only represent invariable amount of sediment in flow and due to various factors in nature may be there is several sediment load for a known flow rate. On the basis of this study quantile Regression and Random Forest methods was used that can estimate sediment load for a given flow rate in the various probability. The use of these two methods can be analyzed sediment load in great flood and special events. Materials and Methods: In this study, sediment rating curve models, quantile Regression and Random Forest was used to estimate sediment load in four stations Gorganrood River Jangaldeh, Nodeh, Arazkoose and Ghazaghli in Golestan province. For this purpose, flow and sediment data was collected at four studied stations and separated into two parts, 75% for training and 25% for testing. The rating curve was obtained using fitted power equation between discharge and sediment load. Quantile Regression and Random Forest algorithms were implemented using R statistical software. The optimal values of the variable parameters of the two methods were determined using trial and error method. By running the model, the amounts of sediment associated with specified flow were calculated in different probability level (1% to 99%). Results: Using these two methods, sediment load was estimated in quantiles 2. 5, 50 and 97. 5%, respectively and range of uncertainty was determined in each station. In Jangaldeh and Nodeh stations Random Forest were selected as best method with RMSE criterion 96 and 210 tons per day and quantile Regression were selected as best method with RMSE criterion 6453 and 24886 tons per day in Arazkoose and Ghazaghli stations. Classic rating curve method estimate sediment load in Jangaldeh, Nodeh, Arazkoose and Ghazaghli stations with RMSE 199, 288, 7505 and 25811 tons per day respectively. Conclusion: The results showed that classic sediment rating curve not only unable to estimate the sediment load in the range of uncertainties in specified flow rate but also estimates sediment load with more error. Quantile Regression and Random Forest methods can be estimate sediment load in various probabilities for a specified flow and this has contributed greatly to accurate and comprehensive planning for the construction of hydraulic structures and in this way, the dangers of the destruction of the facility reduction due to the great flood.

Nowadays, soil salinization is one of the world’ s major threats that reduce soil productivity by intensifying the process of desertification and land degradation. Since laboratory analysis is mostly time consuming and costly, especially in large scales, attempts have been made to study soil salinity using remote sensing techniques in recent years. The present study assessed the potential of remote sensing in predicting soil surface salinity in the east of Lenjan City. Salinity reference points were identified based on analyzing 50 Randomly selected surface soil samples. Satellite indices including DVI, NDVI, EVI, MSAVI, SAVI, RVI, NDWI, SI1, SI2, SI3 and SBI were derived from a Landsat-8 satellite image (path and row of 164 and 37) acquired on September 13, 2019. These indices along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the Multiple Linear Regression and Random Forest models. The linear Regression model was built using band 6, RVI, NDVI, elevation and TWI with a p-value of 0. 049. In the Random Forest model, band 7, slope, band 5 and elevation were among the most important parameters. The r2 value of this model was 0. 21. The results of this study showed that topographic indices had also great importance in salinity prediction. Moreover, comparison of the results indicated that Random Forest had a higher accuracy than the Regression model for determining salinity in the study area.

Recognition equal units and segregation them and upshot planning per units most basic method for management Forest units. Aim this study presentation and comparison classification and Regression tree (CART) and Random Forest (RF) algorithm for Forest type mapping using ASTER satellite data in district one didactic and research Forest's darabkola. In start using inventory network 500* 350 m, take number 150 sample plat in over district. After accomplish Geometric correction and reduce atmospheric effect on image processing bands rationing, create general vegetation indices, principal component analysis and tesslatcap index. After extraction spectrum values relevant by sample plats fabric and processing bands, classification values other pixel accomplish using investigating algorithms. Evaluation accuracy results classification accomplish by some sample plat that not participate in process classification. The result showed preparation map using RF with overall accuracy 66% and kappa coefficient 0.57 than classification and Regression tree with overall accuracy 58% and kappa coefficient 0.49 has superior accuracy. Totality result showed using above algorithm may increased accuracy Forest type map.

Accurate travel time prediction is one of the important issues in the field of traffic and transportation that can significantly affect the daily life of people and organizations. In this research, four different machine learning methods including linear Regression, multivariate Regression, Random Forest and deep artificial neural network were trained to predict travel time. The purpose of this research is to predict travel time for use in intelligent traffic systems and to use and compare several new methods, including deep neural network and Random Forest Regression, as well as considering new parameters in the computations such as weather conditions, traffic flow, travel time, and accidents and the traffic locking points compared to other studies are the innovation and comprehensiveness of this study compared to other studies. In the design and implementation of this research, real traffic data taken from Google map was used and analyzed. This data includes information such as traffic conditions, season, time of day, weather conditions, and route characteristics. The results of this research show that the deep neural network (DNN) model with R2 equal to 0.833 has a very good performance among the investigated models. This model explains 0.833% of the variance of the data and the distribution of the residuals in it is relatively central with a mean of zero and a distribution close to normal. The linear Regression model with R2 equal to 0.615 has a poorer performance than DNN and explains 0.615% of the data variance. But the Random Regression model with R2 equal to 0.955 has one of the best performances in competition with DNN and explains 0.955% of the data variance. MSE and RMSE parameters were also used to evaluate the performance of the models, and as a result, a multidimensional comparison was made between the models, and the Random Forest model resulted in the lowest error values. Since in the collected traffic data, traffic accidents and consequently traffic locking points are also used in the models, and considering that the Random Forest model is more effectively adapted to the data despite the presence of noise and anomaly, the R2 value of this model is higher than R2 of Deep neural networks, due to the overfitting nature of Deep Learning methods.