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• A novel approach for regional modeling and forecasting of PWV using LS-SVR is suggested. • The results of the new model are analyzed at control and radiosonde stations. • A comparative analysis between the results of new model, GPS and VBT is made. We propose a new model for spatio-temporal modeling and forecasting of precipitable water vapor (PWV) using least square support vector regression (LS-SVR) method. The LS-SVR uses simple linear equations. As a result, the complexity of the computational algorithm is reduced. In addition, the convergence speed and accuracy of the results increase. The evaluation of the new method has been done with the observations of the GPS networks at north-west and central Alborz in Iran. In the north-west GPS network, observations of 23 GPS stations in the period of 27 October to 10 November 2011 are used. However, in central Alborz network, the observations of 11 GPS stations in the period of 10 to 24 June 2016 have been used. The north-west GPS network is in the mountainous region and its observations are for the winter season. But, the second network is near the coastal area and summer season measurements are used. The latitude, longitude and height of GPS stations, DOY, time, relative humidity, temperature and pressure are considered as an input of the LS-SVR model. The output of the new model is the PWV (LS-SVR PWV). After the training step, the new model is used to estimate the spatio-temporal variation of PWV. The results of the LS-SVR model are compared and evaluated with the standard neural network (SNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), Saastamoinen, global pressure and temperature 3 (GPT3), voxel-based tomography (VBT) models, as well as with radiosonde measurements. Error evaluation of models has been done in control stations as well as by precise point positioning (PPP) method. For LS-SVR model, the averaged RMSE in the control stations of the north-west GPS network is 1.69 mm, while for the central Alborz GPS network, 1.88 mm is calculated. Also, the averaged relative error of LS-SVR model calculated in the Tabriz and Tehran radiosonde stations are 4.66 % and 6.12 %, respectively. The results of this paper show that the LS-SVR model has a very high capability in forecasting the spatio-temporal variation of PWV at the GPS network territory. The new model can be used for accurate estimation of PWV, meteorological applications and flood forecasting. [ABSTRACT FROM AUTHOR]

This paper focuses on a comprehensive solution for dry municipal solid waste (MSW) management with describes the scenario of the gasification process. For this purpose, the potential of implementing the MSW gasification process in Southeast Asia (Iran) with three agents of air, steam, and air-steam is investigated. In order to manage the MSW gasification process as accurately as possible in the study area, a thermodynamic, exergy, economic, environmental, carbon footprint, water footprint, ecological emergy, and risk assessment for the three mentioned gasification agents were carried out in the most populated cities of Iran. In the capital of Iran (Tehran) with the largest population and as a result of largest MSW production, the potential of the gasification process has been investigated at different times (10 consecutive years and 12 months of a year). Finally, by combining artificial neural networks and genetic programming, comprehensive relationships have been established to predict the thermodynamic conditions of gasification with an air-steam agent. The general results of the survey indicate that gasification with air agents has good potential for most regions. The use of the steam agent has created worse economic, environmental, and risk conditions than the other two agents. Investigating the potential of the gasification process in Tehran shows that this city has a good potential to implement this process compared to other cities in Iran, and the most suitable location for establishing the gasification process is the 4th region of this city. [Display omitted] • Potential of municipal solid waste (MSW) gasification for all Iranian cities. • Comparison of using air, steam, and air-steam agents for MSW gasification. • The payback period of air agent MSW gasification is lower than other agents. • Machine Learning used for prediction of the behavior of MSW gasification. [ABSTRACT FROM AUTHOR]

The Integrated Solid Waste Management (ISWM) is a recent effective tool to manage with the growing challenge of Municipal Solid Waste (MSW). The ISWM integrates all the system components (i.e. transfer, treatment, recycling and disposal of wastes) to enhance the sustainable waste management whilst reducing operational costs. In this paper, we investigate an integrated framework of the Fleet Size and Mix Vehicle Routing Problem (VRP) to optimize the cost-effective ISWM system. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is developed to concurrently minimize the transportation cost in the entire waste management system and total deviation from the fair load allocation to transfer stations. A complete ISWM system with all interdependent facilities and multiple technologies, is developed to address a tri-echelon Fleet Size and Mix VRP with a heterogeneous fleet of vehicles under multiple technologies and waste compatibility constraints. The model was solved for both the Preemptive and Non-Preemptive conditions using Lexicographic and Goal Programming optimization approaches. The model was tested on a case of ISWM in the Southern part of Tehran, Iran. [ABSTRACT FROM AUTHOR]

This paper proposes a new Vehicle Routing Problem (VRP) with fair Carrier Collaboration (CC) which split multi-pickup and delivery services are considered for serving customers. This study has focused on VRP and serving customers with several commodity requirements from different geographically scattered suppliers subject to constraints on the vehicle capacity. A Mixed Integer Programming (MIP) model to maximize the total profit and the fair sharing of profit among the carriers by considering the travel time minimization is developed. Each carrier with its limited capacity can have reserved requests which must be served by itself and selective requests which can be served by itself or other vehicles or not served at all. There are various applications of the proposed model in the environment which can help reducing number of vehicles serving to the customers and eliminating empty back hauls. A Genetic Algorithm (GA) is proposed to solve this problem due to its Non-deterministic Polynomial-time hard (NP-hard) nature. In addition, Variable Neighbourhood Search (VNS) method is developed for improving the quality of initial solutions. Some instances are generated at different scales to evaluate the algorithm's performance by comparing the results of an exact optimal solution with that of the proposed algorithm. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time. The algorithm is also tested for an online shopping website in Tehran, Iran. The test outcome shows that the proposed model returns a better benefit compared to the manual methods. The results of sensitivity analysis suggest that increasing the fairness coefficient among carries can led to a decrease in the total obtained profit. • Develop a novel carrier collaboration routing problem model with multi-commodity service and sharing fair profit. • Extend the carrier collaboration to general problems with in many-to-one problem pickup and delivery category. • Solve the problem with genetic algorithm with a new specific chromosome representation. • Result of implementation of the model in the real-world and sample instances. • Sensitivity analysis of the model based on fair distribution between participants is conducted. [ABSTRACT FROM AUTHOR]

This paper aims to improve the spatial accuracy of urban growth simulation models and clarify any associated uncertainties. Artificial Neural Networks (ANNs), Random Forest (RF), and Logistic Regression (LR) were implemented to simulate urban growth in the megacity of Tehran, Iran, as a case study. Model calibration was performed using data between 1985 and 1999 whereas the data between 1999 and 2014 was used for model validation. First of all, Transition Index Maps (TIMs) were computed by means of each model to assess the potential of urban growth for each cell. Using the standard deviation, consensus between the TIMs was evaluated. Because the TIMs of the individual models manifested discrepancies, they were combined using a number of ensemble forecasting approaches including median, mathematical average, principle component analysis, and weighted area under the total operating characteristic. The individual and combined TIMs were then put into Cellular Automata (CA) to simulate the future pattern of urban growth in Tehran. The results were evaluated in two stages. At first, the TIMs were evaluated by means of Total Operating Characteristics (TOC), and then a set of statistical indices was used to evaluate the spatial accuracy of the simulated urban growth maps. The best result was obtained by median ensemble forecasting, whereas the LR model showed the lowest level of accuracy. In similar studies, it is recommended to implement and compare different ensemble methods when integrating individual models. • We compared the effectiveness of ANNs, RF and LR for urban growth modelling. • Standard error map indicated discrepancy among the results of individual models. • Various ensemble forecasting approaches were proposed to combine individual models. • Mean ensemble forecasting created more accurate simulated maps. [ABSTRACT FROM AUTHOR]

• A multi-criteria framework is proposed for bioethanol facility location. • The best worst method is used to calculate the weights of the criteria. • The provinces of Iran are evaluated as alternatives in this study. • A questionnaire answered by 41 experts from Iran is used for the BWM. • Province of Khuzestan (followed by Tehran) is selected for bioethanol production. One of the major factors in the success of renewable energy is finding a proper location for production facilities. At a national level, different parts of a country (e.g. provinces) can be seen as alternatives that can be assessed based on a set of criteria, and ranking them to identify the best location. The focus in this paper is on identifying the best location for the production of bioethanol. After a comprehensive literature review, an evaluation framework is proposed based on the three dimensions of sustainability (economic, environmental and social). Using data provided by a sample of experts in a developing country – Iran – and applying the best-worst method (BWM), a number of decision-making criteria are evaluated. Performance data involving the various provinces of Iran are collected from different sources. The performance data and the weights identified through BWM are used to calculate an overall score for each province, which is then used to rank the provinces, with the province of Khuzestan (closely followed by Tehran) being identified as the most suitable province for bioethanol production in Iran. [ABSTRACT FROM AUTHOR]

The seismic hazard assessment of Tehran, the capital of Iran with a population of over 10 million, is necessary in the decision-making process for risk reduction. The main objective of this paper is to present a procedure for obtaining maximum hazard maps in seismically active regions such as Tehran with a history of destructive earthquakes, but where the number of the instrumentally recorded large magnitude events is small. The deterministic and probabilistic seismic hazard methods are two quantitative approaches with some practical limitations. To overcome their limitations, the Bayesian statistics are used to estimate maximum earthquake magnitude by combining prior seismological information with the available limited data. The Bayesian approach is used to estimate maximum earthquakes corresponding to different individual ground-motion scenarios at a specific hazard level. The maximum hazard maps are obtained by merging different individual worst-case ground-motion scenarios at a specific hazard level. The hazard analyses under different scenarios are conducted by the finite-fault procedure which uses geological and seismological parameters for each seismic source. The obtained results are in agreement with the observed intensities of historical earthquakes. Moreover, the conventional approach of probabilistic seismic hazard analysis is presented to show the difference between the two procedures. The findings provide information about the seismic design or retrofit of structures and infrastructures. [ABSTRACT FROM AUTHOR]

Abstract: Train timetabling with minimum delays is the most important operating problem in any railway industry. This problem is considered to be one of the most interesting research topics in railway optimization problems. This paper deals with scheduling different types of trains in a single railway track. The primary focus of this paper is on the periodic aspects of produced timetables and the proposed modeling is based on the periodic event scheduling problem (PESP). To solve large-scale problems, a hybrid meta-heuristic algorithm based on simulated annealing (SA) and particle swarm optimization (PSO) is proposed and validated using some numerical examples and an Iranian case study that covers the railway line between two cities of Isfahan and Tehran. [Copyright &y& Elsevier]

Abstract: The availability of reliable and ubiquitous urban services data is a key component of successful city management. The inability of traditional bureaucratic public administration to meet citizens’ needs effectively has led to a search for new methods that grant more efficient, effective and reliable provision of services to citizens. Because there are many users with distinct computing platforms and preferences, urban services data must be accessible using interoperable solutions. Numerous solutions for providing interoperability are proposed by the Geospatial Information (GI) community, as well as the Information Technology (IT) community. The most widely used technologies for providing interoperability in the GI community are OGC services framework. In the IT community, Web services technologies provide full potential interoperability among heterogeneous computing platforms. However, these two kinds of services are not directly compatible. To provide an interoperable solution, OGC services and Web services should be integrated efficiently. The underlying issue in integrating OGC services and Web services is the gap between the predefined functionality inherent in the OGC services, and the lack of any predefined functionality in the Web services technologies. This paper identifies the implementation issues in integrating OGC services and Web services and then proposes standards-based approaches to overcome these integration issues. Afterwards, the paper explains the design and development of Standards-based Interoperable Services for Accessing Urban Services Data (SISAUSD). [Copyright &y& Elsevier]

Abstract: This paper treats with K-shortest viable path problem in a transportation network including multiple modes. The considered modes are metro, rapid-bus, private and walking. In this network, a viable path is one that the number of mode changes is limited and the traverse time and also the walking, metro and private usage are restricted subjected to some constraints. The traverse time is defined dependent on congestion level. Because constrained shortest path is NP-hard, we extend two metaheuristic algorithms namely GASA and BACS for the given problem. GASA is a Greedy Algorithm Simulated Annealing and BACS is a bi-direction searching Ant Colony System made by two colonies of ants. We evaluate the validation of these algorithms applying several multimodal random networks. In addition, their results are compared with CPLEX 12.1. We find that GASA is appropriate for small networks and BACS has better performance in median and large-scale networks. Our results on Tehran network also demonstrate that BACS provides better objective values than BACS ones because Tehran public transportation is sparse. [Copyright &y& Elsevier]

Abstract: This paper presents the seismic vulnerability and rehabilitation of a steel building with semi-rigid connections in Tehran. This 19-storey building with an asymmetric plan was constructed 30 years ago in three blocks. The qualitative vulnerability of the building was evaluated in the first step of the study, indicating its high seismic vulnerability. In the next step of the study, the quantitative vulnerability of the structure was investigated. The results show that the building was strong enough to resist gravity loads but the strength was not adequate for seismic loads. Finally, three seismic retrofitting methods consisting of concrete shear wall, steel shear wall, and steel bracing were proposed. The comparison of the three retrofitting alternatives in terms of architecture, implementation, and seismic performance showed the superiority of using the concrete shear walls over the two other alternatives. [Copyright &y& Elsevier]

Abstract: In this paper, a hybrid model which combines Extended Kalman Filter (EKF) and Genetic Programming (GP) for forecasting of water demand in Tehran is developed. The initial goal of the current work is forecasting monthly water demand using GP for achieving an explicit optimum formula. In the proposed model, the EKF is applied to infer latent variables in order to make a forecasting based on GP results of water demand. The available dataset includes monthly water consumption of Tehran, the capital of Iran, from 1992 to 2002. Five best formulas based on GP results on this dataset are presented. In these models, the first five to three lags of observed water demand are used as probable and independent inputs. For each model, sensitivity of the results for each input is measured mathematically. A model with the most compatibility of the computed versus the observed water demand is used for filtering based on EKF method. Results of GP and hybrid models of EKFGP demonstrate the visible effect of observation precision on water demand prediction. These results can help decision makers of water resources to reduce their risks of online water demand forecasting and optimal operation of urban water systems. [Copyright &y& Elsevier]

Abstract: In this paper, the results of an investigation on a two-stage cooling system have been studied. This system consists of a nocturnal radiative unit, a cooling coil, and an indirect evaporative cooler. During the night in summer, requisite chilled water for a cooling coil unit is provided by nocturnal radiative cooling and is stored in a storage tank. During the next day, the water in the tank provides chilled water for the cooling coil unit and hot outdoor air passes through two-stages: the cooling coil unit and an indirect evaporative cooler. Three sources provide secondary air for the indirect evaporative cooler. The sources are outdoor air, the air leaving from the cooling coil, and the air leaving from the indirect stage (regenerative). The investigation has been conducted in weather conditions in the city Tehran. The results obtained demonstrate that the first stage of the system increases the effectiveness of the indirect evaporative cooler. Also, the regenerative model provides the best comfort conditions. Therefore, this environmentally friendly and energy-efficient system can be considered as an alternative to the mechanical vapor compression systems. [ABSTRACT FROM AUTHOR]

Abstract: Energy consumption in buildings is heavily affected by climate change. It causes significant variations of the outdoor design conditions, cooling and heating loads. In this paper, a total of 40 years (1967–2006) of hourly temperature data were considered in four decades and outdoor design conditions for each decade were calculated. It was found that wet-bulb temperature (WBT) has increased by 3.28°C during the mentioned period, which impacts on design and selection of the cooling equipment. The results show that cooling hours is projected to increase around 15% over the whole 1967–2006 period. The most important result found in this study is the inability of direct evaporative cooling (DEC) systems in preparing comfort condition after 1997, whereas these systems provided thermal comfort in Tehran''s buildings from 1967 to 1996. The prediction of climate change impacts on the outdoor design conditions and cooling systems performance until 2020 demonstrates dry bulb temperature (DBT) and WBT increase 1.05°C and 2.42°C respectively. Hence, indirect–direct evaporative cooling systems (IDEC) may not be responsible for the years after 2016. Besides, the additional energy for absorption and vapor compression (VC) cooling systems will grow quickly in the future. [Copyright &y& Elsevier]

Abstract: The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria that often are vague and have uncertainty conditions. Therefore, we are faced with a special Multiple Criteria Decision Making (MCDM) problem. The purpose of this paper is to develop a fuzzy expert system for selecting superior stocks in order to encounter the uncertainty of stock portfolio recommendation and model the recommendation rules which experts at Tehran Stock Exchange (TSE) use for portfolio recommendation. The results of implementing the designed fuzzy expert system at TSE were affirmative. [Copyright &y& Elsevier]

Abstract: This paper studies the energy balance between the input and the output per unit area for greenhouse cucumber production. For this purpose, the data on 43 cucumber production greenhouses in the Tehran province, Iran, were collected and analyzed. The results indicated that a total energy input of 148836.76MJha−1 was consumed for cucumber production. Diesel fuel (with 41.94%) and chemical fertilizers (with 19.69%) were amongst the highest energy inputs for cucumber production. The energy productivity was estimated as 0.80kgMJ−1. The ratio of energy output to energy input was approximately 0.64. Results indicate 10.93% and 89.07% of total energy input was in renewable and non-renewable forms, respectively. The regression results revealed that the contribution of energy inputs on crop yield (except for fertilizers and seeds energies) was significant. The human labour energy had the highest impact (0.35) among the other inputs in greenhouse cucumber production. Econometric analysis indicated that the total cost of production for one hectare of cucumber production was around 33425.70$. Accordingly, the benefit–cost ratio was estimated as 2.58. [Copyright &y& Elsevier]

Abstract: The well-known fuzzy rule-based Takagi–Sugeno–Kang (TSK) model is combined with a set of fuzzy regressions (FR) to investigate the impact of the climate change on the electricity consumption duration. The electricity demand forecasts in the short-terms have a vital application in electricity markets. Knowing that the energy is a product of the relation between the climate change and the average consumption duration of the peak load. The paper introduces a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors in the consequent part to model effects of the climate change on the electricity demand. However, a simplified version of the model is applied to daily data of the average temperature in Tehran, 2004. First, based on an initially fitted nonlinear curve, an optimization model is employed to cluster data into three groups of cold, temperate and hot. The fuzzy data have been expanded to reduce the temperature volatile property. Then the relation is estimated by the fuzzy regressions in company with the TSK model. Numerical results show high efficiency of the proposed combined fuzzy model, as well as a minor decrease in the average absolute error. [Copyright &y& Elsevier]

Industry 4.0 Predictive Maintenance (PdM 4.0) architecture in the broadcasting chain is one of the taxonomy challenges for deploying Industry 4.0 frameworks. This paper proposes a novel PdM framework based on advanced Reference Architecture Model Industry 4.0 (RAMI 4.0) to reduce operation and maintenance costs. This framework includes real-time production monitoring, business processes, and integration based on Design Science Research (DSR) to generate an innovative Business Process Model and Notation (BPMN) meta -model. The addressed model visualizes sub-processes based on experts' and stakeholders' knowledge to reduce the cost of maintenance of audiovisual services including satellite TV, cable TV, and live audio and video broadcast services. Based on the recommendation and the concept of Industry 4.0, the proposed framework tolerates the predictable failures and further concerns in similar related industries. Some empirical experiments have been conducted by using the Islamic Republic of Iran Broadcasting's (IRIB) high-power station (located near the capital city of Iran, Tehran) to evaluate the functionality and efficiency of the proposed predictive maintenance framework. Practical outcomes demonstrate that interval times between data collection should be increased in audio and video broadcasting predictive maintenance because of the limitation of the internal processing performance of equipment. The framework also indicates the role of the Frequency Modulation (FM) transmitters' data clearance to reduce the instability and untrustworthy data during data mining. The proposed DSR method endorses using a customized RAMI 4.0 meta -model framework to adapt distributed broadcasting and communication with PdM 4.0, which increases the stability as well as decreasing maintenance costs of the broadcasting chain in comparison to state-of-the-art methodologies. Furthermore, it is shown that the proposed framework outperforms the best-evaluated methods in terms of acceptance. [ABSTRACT FROM AUTHOR]

This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. [Display omitted] • A Forward Feature Selection - Random Forest (FFS-RF) model describes urban growth. • FFS-RF found temporal non-stationarity of drivers in Iran's Tehran-Karaj region. • The Total Operating Characteristic evaluated the transition index map (TIM). • FFS-RF produced a TIM that has predictive power no better than a baseline TIM. • FFS-RF and Cellular Automata extrapolated urban growth to the year 2060. [ABSTRACT FROM AUTHOR]