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Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits. To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis (PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60 km northeast of the Anarak city and 270 km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold, arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation. [ABSTRACT FROM AUTHOR]

This paper describes a detailed evaluation of the performance and characteristic behaviour of four equations for estimating reference evapotranspiration (ET) at eight meteorological sites in a subtropical climate. The equations assessed were: Makkink (MK), Turc (TC), Priestley-Taylor (PT) and Hargreaves (HG). The sites were distributed throughout the north of Iran and represent an intermediate humidity regime. The Penman-Monteith (PM) method was chosen as the standard for comparison and calibration of the above-mentioned four equations. Good correlation was found between the ET values estimated by each of the four empirical equations and the PM method for all the locations; however MK and TC equations produced considerable underestimations. The performance of the PT and HG equations improved slightly after region-specific coefficients were developed for each equation, and the TC and MK equations were improved greatly. The modified PT equation turned out to be the most precise method, demonstrating superiority over the other methods evaluated (0.48 mm d of root mean square error (RMSE)). Good performance from the modified HG equation (0.53 mm d of RMSE) must be emphasized, given the simplicity of that method, which only requires maximum and minimum air temperature data. [ABSTRACT FROM AUTHOR]

Dewpoint temperature (Tdew) plays a key role in agricultural issues as well as meteorological studies. This paper is aimed at developing and validating prediction and estimation models of Tdew values. Gene expression programming (GEP), multivariate adaptive regression spline (MARS), and random forest (RF) models were employed. Data from six weather stations (consisting of a period of ten years) in East Azerbaijan, northwestern Iran were utilized for establishing, testing, and validating the models. In the case of predicting models, chronological records of Tdew in previous time steps were introduced as models' inputs to predict Tdew values at daily and weekly prediction intervals. In the case of Tdew estimating models, daily records of mean air temperature, sunshine hours, relative humidity, and wind speed were utilized as inputs to estimate Tdew. Acquired results showed prediction-based GEP surpasses the MARS and RF models in both daily and weekly prediction intervals. Among the estimation models, the MARS models that relied on air temperature, relative humidity, and sunshine hours presented the most accurate results in all studied locations as well as the studied region. The current study proposes the use of MARS models in estimating Tdew magnitudes, while it criticizes the use of single data set assignment for both temporal and spatial analysis. [ABSTRACT FROM AUTHOR]

Kriging has some problems such as ignoring sample values in giving weights to them, reducing dependence structure to a single covariance function, and facing negative confidence bounds. In view to these problems of kriging in this study to estimate Cu in the Iju porphyry Cu deposit in Iran, we used a convex linear combination of Archimedean copulas. To delineate the spatial dependence structure of Cu, the best Frank, Gumbel, and Clayton copula models were determined at different lags to fit with higher-order polynomials. The resulting Archimedean copulas were able to describe all kinds of spatial dependence structures, including asymmetric lower and upper tails. The copula and kriging methods were compared through a split-sample cross-validation test whereby the drill-hole data were divided into modeling and validation sets. The cross-validation showed better results for geostatistical estimation through copula than through kriging in terms of accuracy and precision. The mean of the validation set, which was 0.1218%, was estimated as 0.1278% and 0.1369% by the copula and kriging methods, respectively. The correlation coefficient between the estimated and measured values was higher for the copula method than for the kriging method. With 0.0143%2 and 0.0162%2 values, the mean square error was substantially smaller for copula than for kriging. A boxplot of the results demonstrated that the copula method was better in reproducing the Cu distribution and had fewer smoothing problems. [ABSTRACT FROM AUTHOR]

Background: Examining past trends and predicting the future helps policymakers to design effective interventions to deal with myocardial infarction (MI) with a clear understanding of the current and future situation. The aim of this study was to estimate the death rate due to MI in Iran by artificial neural network (ANN).Methods: In this ecological study, the prevalence of diabetes, hypercholesterolemia over 200, hypertension, overweight and obesity were estimated for the years 2017-2025. ANN and Linear regression model were used. Also, Specialists were also asked to predict the death rate due to MI by considering the conditions of 3 conditions (optimistic, pessimistic, and probable), and the predicted process was compared with the modeling process.Results: Death rate due to MI in Iran is expected to decrease on average, while there will be a significant decrease in the prevalence of hypercholesterolemia 1.031 (- 24.81, 26.88). Also, the trend of diabetes 10.48 (111.45, - 132.42), blood pressure - 110.48 (- 174.04, - 46.91) and obesity and overweight - 35.84 (- 18.66, - 5.02) are slowly increasing. MI death rate in Iran is higher in men but is decreasing on average. Experts' forecasts are different and have predicted a completely upward trend.Conclusion: The trend predicted by the modeling shows that the death rate due to MI will decrease in the future with a low slope. Improving the infrastructure for providing preventive services to reduce the risk factors for cardiovascular disease in the community is one of the priority measures in the current situation. [ABSTRACT FROM AUTHOR]

Estimation of reservoir inflow is of particular importance in optimal planning and management of water resources, proper allocation of water to consumption sectors, hydrological studies, etc. This study aimed to estimate monthly inflow (Q) to the Maroon Dam reservoir located in Iran utilizing climatic data such as minimum, maximum, and mean air temperatures (Tmin, Tmax, T), reservoir evaporation (E), and rainfall (R). The impact of any of the mentioned variables was analyzed by the entropy-based pre-processing technique. The results of the pre-processing showed that the rainfall is the most important parameter affecting the reservoir inflow. Therefore, three types of input patterns were taken into consideration consisting the antecedent Q-based, antecedent R-based, and combined antecedent Q and R-based input combinations. To estimate the monthly reservoir inflow, a random forest (RF) was firstly employed as the standalone model. Then, two different types of hybrid models were proposed via coupling the RF on complete ensemble empirical mode decomposition (CEEMD) and wavelet analysis (W) in order to implement the coupled CEEMD-RF and W-RF models. It is worthwhile to mentioning that six mother wavelets were used in developing the hybrid W-RF models. Four error metrics including root mean square error (RMSE), mean absolute error (MAE), Kling-Gupta efficiency (KGE), and Willmott index (WI) were used to assess the accuracy of implemented models. The attained results indicated the superiority of proposed hybrid models over the classic RF for estimating the monthly reservoir inflow. The most precise model during the test phase was W-RF(3) utilizing the Sym(2) as the mother wavelet under a lagged Q-based pattern with error measures of RMSE = 15.011 m3/s, MAE = 10.439 m3/s, KGE = 0.832, WI = 0.773. [ABSTRACT FROM AUTHOR]

Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m²). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: MultiLayer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted ‎values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R² = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user''s experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (Pb) and the oil formation volume factor at bubble point (Bob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions. [Copyright &y& Elsevier]

Objective: High salt intake is one of the leading diet-related risk factors for several non-communicable diseases. We aimed to estimate the prevalence of high salt intake in Iran. Design: A modelling study by the small area estimation method, based on a nationwide cross-sectional survey, Iran STEPwise approach to risk factor Surveillance (STEPS) 2016. The modelling estimated the prevalence of high salt intake, defined as a daily salt intake ≥ 5 g in all districts of Iran based on data from available districts. The modelling results were provided in different geographical and socio-economic scales to make the comparison possible across the country. Setting: 429 districts of all provinces of Iran, 2016. Participants: 18 635 salt intake measurements from individuals 25 years old and above who participated in the Iran STEPS 2016 survey. Results: All districts in Iran had a high prevalence of high salt intake. The estimated prevalence of high salt intake among females of all districts ranged between 72·68 % (95 % UI 58·48, 84·81) and 95·04 % (95 % UI 87·10, 100). Estimated prevalence for males ranged between 88·44 % (95 % UI 80·29, 96·15) and 98·64 % (95 % UI 94·97, 100). In all categorisations, males had a significantly higher prevalence of high salt intake. Among females, the population with the lower economic status had a higher salt consumption than the participants with higher economic status by investigating the concentration index. Conclusions: Findings of this study highlight the high salt intake as a prominent risk factor in all Iran regions, despite some variations in different scales. More suitable population-wide policies are warranted to handle this public health issue in Iran. [ABSTRACT FROM AUTHOR]

Background: To inform researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review. Methods: We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 131 studies and included 29 of them. Results: The included studies provided outputs for a total of 84 study-model/scenario combinations. Sixteen studies used 3–4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 1,777 (388,951) for cumulative deaths, 20,588 (2,310,161) for cumulative cases, and at the end of month four (2020-06-20), were 3,590 (1,819,392) for cumulative deaths, and 144,305 (4,266,964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418,834 on 2020-12-19 (and 41,475,792 on 2020-12-31). Model estimates predict an ominous course of epidemic progress in Iran. Increase in percent population using masks from the current situation to 95% might prevent 26,790 additional deaths (95% confidence interval 19,925–35,208) by the end of year 2020. Conclusions: Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed. Not accounting for under-reporting drives the models' results misleading. [ABSTRACT FROM AUTHOR]

Traffic accidents are the reason for 25% of unnatural deaths in Iran. The main objective of this study is to find a simple model for the estimation of economic costs especially in Islamic countries (like Iran) in a straightforward manner. The model can show the magnitude of traffic accident costs with monetary equivalent. Data were collected from different sources that included traffic police records, insurance companies and hospitals. The conceptual framework, in our study, was based on the method of Ayati. He used this method for the estimation of economic costs in Iran. We promoted his method via minimum variables. Our final model has only three available variables which can be taken from insurance companies and police records. The running model showed that the traffic accident costs were US$2.2 million in 2007 for our case study route. [ABSTRACT FROM AUTHOR]