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Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

Purpose: The problem of wheat production sustainability is an important issue that quarantines the availability of people's food at present as well as the nutrition of the next generations. Food is the essential human's need and has been used from human beings' creation until the end of its life. The existence of systems satisfying such requirements and the sustainability of them is always essential for the survival of the human race. Therefore, this study aims to investigate the sustainability of the wheat production system in Iran. Design/methodology/approach: In this research, using the system dynamics approach, the sustainability of Iran's wheat production has been studied. The literature review section of this paper concentrates on several research papers in this context, considering the systems dynamics approach. In this study, an introduction has been expressed to the human's need for food and the position of wheat as the primary source of food in meeting this need in Iran. System dynamics is one of the most commonly used approaches for modeling and simulating environmental and socio-economic phenomena. The wheat production system covering environmental, economic, and social subsystems has been taken into consideration as a case to model the problem. Key factors affecting wheat production have been collected based on a literature review. 50 years historical data for essential factors such as rain, wheat harvesting area, agricultural technology, fertilizers, population, wheat imports, and exports have been used in modeling and hence in the mathematical formulation of the problem. Based on the history of these factors and related research, the dynamic hypothesis of the problem has been defined, and the causal diagrams of the relationships between critical factors and the wheat production have been developed. After modeling and formulation, the problem has been simulated and validated. Then, various scenarios have been proposed and simulated for the sustainability of the wheat production system, and the results have been addressed. The scenarios for changing the machinery level, reducing chemical fertilizer's use, increasing organic fertilizer use, and rainfall fluctuations have been simulated one by one, and their combinations have been simulated, respectively. Findings: The simulation results indicated that the production of wheat is highly sensitive to precipitation and technology levels in the field. Therefore, it is better to focus more on such two factors and to have more concentration on them. Since water is the most crucial resource in wheat production, it would be better to concentrate the research and development efforts on water management technologies in the field. Due to the high sensitivity of wheat production to precipitation fluctuations and technology levels, more investment and better plans should be provided for the better and more efficient use of the two sources. Research limitations/implications: While numerous factors such as pesticides, seeds, planting and harvesting, irrigation methods, management, human resources, and related requirements affect wheat production, due to the large scale of this research, only the most critical factors were selected for the study. Examining the behavior of each of the above-mentioned factors will result in a better awareness of the existing reality and better planning for wheat production. Practical implications: The wheat production system includes environmental, economic, and social subsystems as well as numerous and complex relationships between the human and the environment. The systemic nature of such interdependencies and interactions needs systematic approaches and integrated assessment tools. Identifying and modeling correctly the intrinsic characteristics of the wheat production system assure preserves or increases its essential results over the time and help governmental organizations and institutes to move towards sustainable development and to set policies that encourage positive changes. Social implications: Since the proposed model is expected to help the government and agricultural institutions in planning wheat production efficiently, it will make the country move towards selfsufficiency in wheat production, which in turn results in psychological and social security in terms of food and increases social sustainability. Originality/value: To the best knowledge of the authors, there is no comprehensive investigation on the sustainability of wheat production in Iran. The literature review indicates that the agricultural context is almost limited to a particular zone and this problem has not been addressed on the national scale. Thus, this is the first research that examines such a problem. [ABSTRACT FROM AUTHOR]

This paper aims to identify and evaluate the bottlenecks and wastes in the loading, unloading, and in-transit processes of iron ore railcars using simulation. A field survey was conducted in one of the largest steel factories and a mine, and each process time was extracted using the work-study and time-study methods. The data for more than 34 thousand railcars were collected from the database of the steel company, Iran Rail Transport company, and a private rail transport company. Seven scenarios have been developed according to (a) operations management, (b) equipment procurement, and (c) infrastructure development. It was found that utilization of the automated loading system would improve performance by 20%. This causes a 24% increase in railcars' turnover, which means obtaining US$10,000 additional income per railcar. It was expected, it would improve performance. Conversely, adding a railcar tippler and developing the tracks reduces performance. [ABSTRACT FROM AUTHOR]

In this paper, the monthly long-term precipitation of the city of Ardabil from 1976 to 2020 is simulated by a modern hybrid learning machine. To this end, the Wavelet and Outlier Robust Extreme Learning Machine (ORELM) models are integrated to produce a hybrid model called "Wavelet-Outlier Robust Extreme Learning Machine (WORELM)". First, the observed data are normalized, and the best normalization coefficients for the study are acquired. About 70% of the observed data are employed to train the artificial intelligence models and the remaining (i.e., 30%) to test them. After that, the optimal mother wavelet and the best decomposition level of the wavelet model are computed. Then, the optimal number of the hidden layer neurons and the best activation function of the ORELM model is chosen by performing a trial and error procedure. In this study, the regularization parameter of the ORELM model is also optimized. Moreover, using the autocorrelation function (ACF), the most influencing lags of the time-series data are detected, and 14 WORELM models are developed. By analyzing the WORELM models, the best model and the effective lags of the time-series data are introduced. The outcomes of the hybrid WORELM model are finally compared with other machine learning (ML) algorithms to prove the superiority of the WORELM model. [ABSTRACT FROM AUTHOR]

The appropriate design of the pond coast structures and the reservoir supplement management entails an accurate pond water surface simulation. A hybrid Genetic Algorithm - Artificial Neural Network (GA-ANN) is presented and applied in this research to estimate the next 3- and 5-day water surfaces. Training and validation of the GAANN is performed using the 4-year daily water surface measurements performed on Chahnimeh reservoir located on the eastern side of Iran. Various input combinations are applied to the GA-ANN method. According to the results, for both the next 3- and 5-day estimation models, the input combination, consisting of the past 2-day water surface data, contributes to the optimal yield. Root Mean Squared Error of the optimum next 3- and 5-day prediction GA-ANN models were obtained to be 0.1798 and 0.3102, respectively. This paper found that in modelling the 3- and 5-day ahead pond water level, the best input variables are the information on the one and two previous days. [ABSTRACT FROM AUTHOR]

As one of the most critical points of Iran, Lake Urmia has always been subjected to ecosystem changes due to severe water level drops. Many basins serve to feed the lake, e.g. the Shaharchay River basin, located west of the lake. In this paper, a new learning machine named "Weighted Regularization Extreme Learning Machine (WRELM)" is integrated with the Wavelet Transform (WT) (W-WRELM) to predict runoff-precipitation amounts of the Shaharchay River basin within an 18 year period from 2000 to 2017. Using the autocorrelation function, lags affecting runoff-precipitation time series are detected, then seven models are developed for both WRELM and W-ERELM by the mentioned lags. After that, all mother wavelets are testes and the best member of their family is identified. Also, the best models for the simulation of precipitation and runoff amounts are introduced through a sensitivity analysis. These models forecast target function values with acceptable exactness and high correlation. For example, the determination coefficient (R2) and Mean Absolute Error (MAE) for the best model of W-WRELM for the estimation of precipitation amounts are obtained to be 0.962 and 0.128, respectively. Moreover, the variance accounted for (VAF) and Scatter Index (SI) for the approximation of rainfall amounts are computed to 0.919 and 0.236, separately. Analyses showed that more than half of the precipitation amounts predicted by the W-WRELM 7 model have an error of more than 10%, but this value is about 64% for the predicted runoff. The lags (t-1), (t-2) and (t-12) are detected as the influencing lags in the simulation of the precipitation amounts of the Shaharchay River basin. Furthermore, the error and uncertainty analyses are conducted on the best models and it is concluded that the performance of W-WRELM in the prediction of precipitation amounts is underestimated, while its performance in the estimation of runoff values is overestimated. [ABSTRACT FROM AUTHOR]

Chernobyl and Fukushima nuclear accidents showed that nuclear accident is a global concern. Dispersion and faraway transportation of radioactive materials under atmospheric conditions are the main goals of this paper. Radioactive materials have a lot of damages, undesirable and dangerous long-term effects on the environment and human health. Therefore, in this paper, the simulation of transport, dispersion and deposition of radioactive materials released from a postulated accident in Bushehr Nuclear Power Plant have been studied using HYSPLIT. The postulated accident for Bushehr Nuclear Power Plant is designed based on world's largest nuclear accident, the Chernobyl accident. In this study, it is assumed that the postulated accident in Bushehr nuclear plant occurred on March 21, 2015. The simulation results show that in the period of one month, nuclear pollution resulting from this accident would affects almost the whole of Iran. The amount of radioactive materials deposition is very high in some cities of Iran. The most deposition of radioactive materials would occur in Bandar Bushehr and Ahvaz. In these cities, the maximum deposition is related to the second day of the accident, which are: 1.55 × 10 8 B q / m 2 and 1.06 × 10 6 B q / m 2 , respectively. During this period, in addition to Iran, even most Asian countries, North America, Europe continent and Africa are affected by nuclear pollution while the most pollution is related to Asia, North America and Europe, respectively. Radioactive materials after passing from the continents come back to the East Asian again. In Asian countries after Iran, the maximum deposition of radioactive materials is related to Turkmenistan that the deposition value is 1.78 × 10 5 B q / m 2 for the second day of the accident. In continent of America, the most nuclear pollution is related to the state of Arizona. In this state, the maximum deposition is 1.00 × 10 5 B q / m 2 on the fifteenth day of the accident. In Europe, the maximum deposition is in Ukraine and it is 4.16 × 10 3 B q / m 2 on 12 days after the accident. In this accident, radioactive materials would contaminate the most of Northern hemisphere and also would influence the southern hemisphere in very negligible amount. [Display omitted] • Simulation of postulated accident helps managers to find the best solution. • Dispersion and faraway transportation of radioactive materials under atmospheric conditions are very important. • The simulation of radioactive materials released from a postulated accident have been studied using HYSPLIT. [ABSTRACT FROM AUTHOR]