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.

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.

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.

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.

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.

Accurate estimation of reference evapotranspiration has great importance in irrigation scheduling. Moreover, the lack of availability of lysimetric data has led researchers to use indirect methods, including data-driven approaches. In the present study, the ability of Gaussian process regression (GPR), support vector regression (SVR) and random forest (RF) data-driven methods was investigated to estimate the evapotranspiration of the reference plant. For this purpose, meteorological data on average temperature, wind speed, relative humidity and sunny hours in the period 2013-18 were collected in nine northern stations of Iran including Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr, Sari, Turkmen port, Gorgan, and Gonbad Kavous. Evapotranspiration calculated using FAO-Penman-Montith method was considered as the target output and four combined scenarios of meteorological parameters were considered to calibrate and validate the studied methods. The accuracy of the mentioned methods was compared using the statistical parameters of correlation coefficient, scatter index, and Wilmott’ s coefficient. The results showed that GPR4 model with scatter index in the range of 0. 132 to 0. 179 in Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr and Sari stations, SVR4 model with dispersion index of 0. 116 to 0. 120 in Turkmen and Gonbad Kavous stations and the Hargreaves-Samani method with a scatter index of 0. 509 at Gorgan station had much more accurate estimates of the evapotranspiration of the reference plant.

Land surface temperature, as one of the important and fundamental parameters in climatology, indicates the relationship between the atmosphere and the Earth. Considering the environmental issues of cities, including the intensification of urban heat islands, accurately estimating LST and identifying its influencing factors play a significant role in urban thermal management and adopting adaptive strategies for heat islands. In this regard, this study compares two regression methods: multiple linear regression and random forest in order to estimate the LST. Daily nighttime MODIS images were used to extract the LST of Tabriz city during the summer. These images were processed in the Google Earth Engine platform and averaged for the period from 2018 to 2022. According to the results, the random forest showed significantly better performance with a coefficient of determination of 0.924 (RMS = 0.009) compared to multiple linear regression. The random forest was also used to determine the importance of the indices. Based on the index importance results, night lights (51/06%), sky view factor (48/01%) and frontal area index (45/27%) were the most important factor affecting the nighttime summer LST in Tabriz city, respectively. The findings of this study, in addition to revealing the strength of the random forest regression in estimating LST, also highlight the importance of various indices in the LST. In this context, the study's results will be practical for managing the thermal environment of Tabriz city and adopting mitigation strategies for its heat islands.

Background and ObjectivesDirect methods of measuring soil water retention curve (SWRC) are time-consuming and expensive, so they are not easily applicable to large scales. Therefore, researchers use pedotransfer functions (PTFs) to obtain it. Various point and parametric pedotransfer functions have been used so far, with numerous methods to estimate the SWRC, each of which has its drawbacks. However, rare methods have been used to develop pseudo-continuous pedotransfer functions. The random forest (RF) method has not been utilized in any study so far, to create pseudo-continuous pedotransfer functions. Some variables have not been used as predictors in pseudo-continuous pedotransfer functions in any research. Therefore, the objectives of this article include investigating the potential of the RF method in creating pseudo-continuous pedotransfer functions, comparing its performance with linear regression, and examining the probability of improving the performance of these functions using the geometric mean and standard deviation of particles diameter and field capacity (FC) and permanent wilting point (PWP) as predictors. MethodologyA total of 120 disturbed and undisturbed soil samples were collected from two provinces of Tehran and Hamedan. Soil texture, bulk density, and soil water retention curve in the range of 0 to 15000 hPa were measured. Then pseudo-continuous pedotransfer functions were created using two methods of linear regression and random forest. The soil water matric suction, soil texture, percentage of silt and sand, bulk density, geometric mean, standard deviation of particles diameter, and moisture content at FC and PWP were used in various combinations to estimate the soil water retention curve. The accuracy and reliability of the generated functions were compared between the two methods and within each method. ResultsUsing soil water matric suction as the only input variable for estimating moisture at different matric suctions was not effective in the RF method, and no model was created. However, in the linear regression method, a model with acceptable results was developed (with R2 values of 0.675 and 0.674 for training and validation stages, respectively), which can be utilized in situations where additional information is not available. The inclusion of soil texture in the linear regression method significantly improved the accuracy of estimates by 5.4% and 5.3% in both training and validation stages, respectively. In the third function, incorporating the percentage of clay and sand alongside soil water matric suction as predictors improved SWRC estimation by 1.5% to 25.0% in both training and validation stages for both RF and linear regression compared to the second function. In the fourth function, using bulk density as an additional predictor led to a significant improvement in accuracy by 6.9% to 13.1%, because bulk density serves as an indicator of soil structure, enhancing the estimation of the soil water retention curve. Utilizing FC improved estimation accuracy by 3.5% to 24.4%, because FC is a point on the SWRC and enters direct information to the models. However, using the PWP as a predictor did not significantly improve estimation accuracy. Using geometric mean (dg) and geometric standard deviation (Sg) instead of percentage of clay and sand in pseudo-continuous pedotransfer functions did not lead to noticeable improvements. Error distribution across soil texture triangles in the linear regression method showed no dependence on soil texture. Because, in pedotransfer functions 1, 2, 4, 7, and 8, the highest error values were obtained in coarse-textured soils, while in pedotransfer functions 5, 6, 9, and 10, the lowest error values were associated with coarse-textured soils. Error distribution across soil texture triangles depended on the type of input variables and the method used to create pedotransfer functions. In all pseudo-continuous pedotransfer functions created by both methods, the accuracy of estimates in both training and validation stages in the RF method was significantly and noticeably higher, ranging from 22% to 46% more than those in linear regression.  ConclusionUsing the regression method and solely relying on soil water matric suction as a predictor, an acceptable pseudo-continuous pedotransfer function was developed. Investigating the potential of establishing a similar relationship using the state-of-the-art estimation methods may lead to independence from relying on numerous soil water retention curve models. Utilizing more detailed information such as particle size distribution and FC for estimating the SWRC through pseudo-continuous pedotransfer functions is recommended. The dependence of error distribution on soil texture triangles on the type of input variables and the method used to create pedotransfer functions underscores the importance of selecting an appropriate combination of input variables and method for creating pseudo-continuous pedotransfer functions for estimating the SWRC. Given the significant superiority of the random forest method over linear regression, using soil water matric suction, percentage of clay and sand, bulk density, and FC as predictors in pseudo-continuous pedotransfer functions with the RF method yielded the best results in estimating the SWRC.Data Availability StatementData is available on reasonable request from the authors. AcknowledgementsThis paper is published as a part of a Ph. D. thesis supported by the Vice Chancellor for Research and Technology of the Bu-Ali Sina University, Iran. The authors are thankful to the Bu-Ali Sina University for financial support. Conflict of interestThe authors declare no conflict of interest. Ethical considerations The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Land surface temperature, as one of the important and fundamental parameters in climatology, indicates the relationship between the atmosphere and the Earth. Considering the environmental issues of cities, including the intensification of urban heat islands, accurately estimating LST and identifying its influencing factors play a significant role in urban thermal management and adopting adaptive strategies for heat islands. In this regard, this study compares two regression methods: multiple linear regression and random forest in order to estimate the LST. Daily nighttime MODIS images were used to extract the LST of Tabriz city during the summer. These images were processed in the Google Earth Engine platform and averaged for the period from 2018 to 2022. According to the results, the random forest showed significantly better performance with a coefficient of determination of 0.924 (RMS = 0.009) compared to multiple linear regression. The random forest was also used to determine the importance of the indices. Based on the index importance results, night lights (51/06%), sky view factor (48/01%) and frontal area index (45/27%) were the most important factor affecting the nighttime summer LST in Tabriz city, respectively. The findings of this study, in addition to revealing the strength of the random forest regression in estimating LST, also highlight the importance of various indices in the LST. In this context, the study's results will be practical for managing the thermal environment of Tabriz city and adopting mitigation strategies for its heat islands.