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Pistachio is one of the most important and valuable orchard products in Iran and some other places in the world. Because it is adaptable to adverse environmental conditions, especially drought and salinity, more land is being used for pistachios. In an increasingly resource constrained world, producers, researchers and policy makers need to clearly identify suitable land to optimize production. The present study modelled the relationship between pistachio yield and soil variables by regression, linear and non-linear (MLR and NMLR), feed forward back propagation artificial neural network (FFBP-ANN), adaptive neural fuzzy inference system (ANFIS) and random forest (RF) models in areas under pistachio production from Iran. For this purpose, 124 pistachio orchards were selected and sampled in Kerman, Fars, Khorasan Razavi, Isfahan and East Azarbaijan provinces of Iran. The results indicated that MLR and NMLR could explain 72 and 77% of the pistachio yield variation, respectively, whereas prediction accuracy increased when the data of pistachio orchards were entered in intelligent models: ANFIS, ANN-based and RF to 86, 92 and 96%, respectively. The RF model was found to be most parsimonious. As the models were highly sensitive to gravel, electrical conductivity, exchangeable sodium, CaCO3, gypsum, and available phosphorus and potassium concentrations, special attention should be paid to the measurement and management of these properties. [ABSTRACT FROM AUTHOR]

Given the importance of carbon monoxide (CO) in acute health threats, studies have been conducted in different countries and cities (272 cities in China and 337 cities on a global scale) on the relationship between daily mortality and CO while the spatial analysis of factors affecting CO pollutant in cities, especially in developing countries, has rarely been done. Accordingly, this research has measured the effect of five environmental‐social variables (ESVs) on the spatial distribution of CO pollutants in the metropolis of Mashhad, Iran. CO concentration data were collected in 23 air pollutant monitoring stations in an area of 356 km2 in 2019. Then, the relationship between five variables and CO pollutant were measured using linear and multiple regression by Sentinel 2A and 3 satellite images in ArcGIS and TerrSet software. The results show that the mean CO concentration averages at 1.56 ppm in the whole city. But its range varies between 0.171 and 2.907 ppm, which is a low figure compared with presented standards and does not indicate a critical situation. The results of multiple regression indicate that 42% of the variance in CO concentration is explained by independent variables. Among five independent variables, the beta value of the land surface temperature (LST) and digital elevation model (DEM) variables is negative and positive for the other three variables, including population density, normalised difference vegetation index (NDVI) and normalised difference built‐up index (NDBI). It should be noted that the strongest correlating variable is population density. Prediction of the spatial distribution of CO pollutants shows the division of the city into three areas: (1) the south and southwest slopes of the city with a low concentration; (2) central areas with a medium concentration; and (3) northern and northeastern areas of the city with a high concentration where low‐income groups reside and there are more worn‐out vehicles, motorcycles and industrial workshops. Areas with high CO concentration need more attention from urban managers. [ABSTRACT FROM AUTHOR]

In the present research, SPT results were adopted to establish relationships for estimating of the relative density in a sandy soil. SPTs were performed in 20 boreholes in Astaneh-ye Ashrafiyeh city in Iran. Laboratory tests including mineralogy, relative density, soil gradation, Atterberg limits, and unit weight were performed on the soil samples. The SPT numbers were then properly corrected. In particular, the correct procedure for correction of the SPT number against the existing overburden effective stress was discussed when the SPT is performed in the vadose zone where the soil may be unsaturated. The soil in the study area was dominantly classified as SP-SM. The relationship between the relative density as one of the most important parameters for dominantly granular soils, with the corrected SPT number (N1(60)) and fines content was then investigated and discussed using artificial neural network and statistical analysis. Results showed that for the soil of interest, the developed relationship performs better than those established in the literature, hence demonstrating the value of developing locally tuned relationships for important projects. [ABSTRACT FROM AUTHOR]

This study develops a set of models to investigate the water budget of Lake Urmia in Iran, a permanent hypersaline lake that has suffered a declining water level since the late 1990s. The models are of the regressive-stochastic type, a combination of multilinear regression and autoregressive integrated moving average stochastic models. The multilinear regression models were used to construct the core of the relationship of lake water level to streamflow, precipitation, evaporation and groundwater depth. Afterward, stochastic models were used to generate data for each independent variable to estimate the oscillation in the lake water depth. Several criteria were used to compare the performance of the models in the aggregated and disaggregated cases with which the pre- and post-encroachment periods are considered, respectively. The regressive-stochastic models are found to be competitive with the existing models developed so far for Lake Urmia water level. [ABSTRACT FROM AUTHOR]

Plant breeders require a classification of the limitations and the capabilities that exist in plants by appropriate statistical methods; this issue leads to the concept of the ideotype. Therefore, the aim of this research was to determine the ideotype of improved rice cultivars. The experiment was carried out based on a randomized complete blocks design with three replications and 13 improved rice cultivars in northern Iran in 2016 and 2017, the required data for determining ideotype was collected. The results demonstrated that the optimal values of six important traits which selected by the multiple regression model explained 64% of the paddy yield (PY). The determined ideotype would increase PY from 6,623 kg ha−1 to 8764–9685 kg ha−1. Selection of desirable phenological traits revealed the highest correlation with the first canonical variable, can cause of selection of superior cultivars with the best agronomic traits. The most genetic distance was observed between cv. 'Kados', 'Shiroodi' and 'Dasht' using mahalanobis method. It was concluded that the methods used in this study, owing to its concern with the genetic variations between cultivars, can be used in determining plant ideotypes in conjunction with other methods and it can guide plant breeders to move through ideotype crops. [ABSTRACT FROM AUTHOR]

The weighted mean and the multiple regression techniques are two methods that are employed to estimate elemental background concentration of lithologies upstream of stream sediment samples and the background value of elements at catchment basin outlets due to the lithology. However, these techniques have not previously been validated against real background values by sampling from lithological units. A geochemical survey was completed in central Iran by collecting samples from stream sediments along with rock samples from upstream lithologies. The accuracy and the precision of both techniques were examined graphically and statistically. The results demonstrated the advantage of multiple regressions over the weighted mean method. However, estimated background values from both techniques show high deviations from the analyzed values for some elements including Cr, Ni, Pb and Zn. While the weighted mean method is not as sensitive as the multiple regression approach to small areas of lithological units (independent variables), the estimated background values emerging from the weighted mean technique follow narrow ranges among different lithological units and cannot highlight the differences between elemental concentrations in various lithologies. However, multiple regression analysis is more successful than the weighted mean method in estimating expected background values of sediments at the catchment basin outlet. The multiple regression method uses the areas of all lithologies to estimate the background value of a particular lithology, whereas in the weighted mean method, only the area of the lithology is involved in the background estimation of that lithology. The existing equations essentially take into account only mechanical dispersion but overlook hydromorphic dispersion of elements in fluvial systems. Moreover, these equations only use area of rock units as the weighting factor or independent variables. Several parameters such as rate of weathering and erodibility, transportation mode, scavenging effects, the sediment delivery ratio of a catchment basin and each lithological unit control sediment chemistry at the basin outlet and should also be taken into account. [ABSTRACT FROM AUTHOR]

Limestone aggregates are considered as construction materials with special suitability for use as a part of base courses, asphalt and Portland cement concrete. The suitability of aggregates for different applications is determined by aggregate parameters including the Los Angeles aggregate abrasion value (LAAV), aggregate impact value (AIV), and aggregate crushing value (ACV). In this paper, a combination of all these parameters is presented as an aggregate index (AI); Hamedan Limestones were classified based on this new index. The physico-mechanical properties of aggregates can also be used to evaluate their suitability for any specific engineering application; therefore, these properties of rock samples were measured. The AI was modified using p-wave velocity parameters, and changed into an aggregate modified index (AI/Vp = AMI). Statistical analyses, including simple and multiple regressions, were applied to offer some rational approaches and identify those physico-mechanical parameters that are more appropriate for prediction of AMI. Consequently, based on these analyses, p-wave velocity, uniaxial compressive strength, dry density and Brazilian tensile strength parameters show significant correlations with AMI at α-level = 0.1. [ABSTRACT FROM AUTHOR]

Backbreak is an undesirable side effect of bench blasting operations in open pit mines. A large number of parameters affect backbreak, including controllable parameters (such as blast design parameters and explosive characteristics) and uncontrollable parameters (such as rock and discontinuities properties). The complexity of the backbreak phenomenon and the uncertainty in terms of the impact of various parameters makes its prediction very difficult. The aim of this paper is to determine the suitability of the stochastic modeling approach for the prediction of backbreak and to assess the influence of controllable parameters on the phenomenon. To achieve this, a database containing actual measured backbreak occurrences and the major effective controllable parameters on backbreak (i.e., burden, spacing, stemming length, powder factor, and geometric stiffness ratio) was created from 175 blasting events in the Sungun copper mine, Iran. From this database, first, a new site-specific empirical equation for predicting backbreak was developed using multiple regression analysis. Then, the backbreak phenomenon was simulated by the Monte Carlo (MC) method. The results reveal that stochastic modeling is a good means of modeling and evaluating the effects of the variability of blasting parameters on backbreak. Thus, the developed model is suitable for practical use in the Sungun copper mine. Finally, a sensitivity analysis showed that stemming length is the most important parameter in controlling backbreak. [ABSTRACT FROM AUTHOR]

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests. [ABSTRACT FROM AUTHOR]

Multivariate statistical techniques and geostatistical methods are among the important tools used in surface water quality management. They are widely used in interpreting data, identifying the pollution sources, understanding the spatial variation of parameters, and determining the places of monitoring stations. Therefore, in this study, spatial variation of water quality and pollutants in the Anzali Wetland water (Iran) was evaluated using multivariate statistical and Kriging methods. The values of different water quality parameters measured in six stations in the wetland water were subjected to cluster analysis (CA) and principal component analysis (PCA). Cluster analysis reduced the number of stations from six to four. The results of PCA showed that industrial and agricultural pollution sources could be responsible for the Anzali Wetland water quality. Then, the spatial variation maps of the PCA scores were generated using Kriging geostatistical method in the geographical information system (GIS) to investigate the pollution sources affecting the wetland parts. These maps illustrated that a great part of the wetland body was under the effect of agricultural sources, while the industrial sources affected the outlet and central parts. Finally, a comparison between two models (multiple linear regression (MLR) and Kriging) was made to assess their ability in predicting water quality parameters in the study area. The results showed the improvement of prediction using MLR, which was by 25%-97%, compared with Kriging. The results of the present study can be effectively used in the planning and implementation of future monitoring networks in the Anzali Wetland and other similar aquatic systems.

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disease with complex causes, manifestations, complications and management. Understanding the wide range of risk factors for T2DM can facilitate diagnosis, proper classification and cost-effective management of the disease., Aims: To compare the power of an artificial neural network (ANN) and logistic regression in identifying T2DM risk factors., Methods: This descriptive and analytical study was conducted in 2013. The study samples were all residents aged 15-64 years of rural and urban areas in East Azerbaijan, Islamic Republic of Iran, who consented to participate (n = 990). The latest data available were collected from the Noncommunicable Disease Surveillance System of East Azerbaijan Province (2007). Data were analysed using SPSS version 19., Results: Based on multiple logistic regression, age, family history of T2DM and residence were the most important risk factors for T2DM. Based on ANN, age, body mass index and current smoking were most important. To test for generalization, ANN and logistic regression were evaluated using the area under the receiver operating characteristic curve (AUC). The AUC was 0.726 (SE = 0.025) and 0.717 (SE = 0.026) for logistic regression and ANN, respectively (P < 0.001)., Conclusions: The logistic regression model is better than ANN and it is clinically more comprehensible., (Copyright © World Health Organization (WHO) 2018. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license (https://creativecommons.org/licenses/by-nc-sa/3.0/igo).)