Search

In order to mitigate/prevent the risks of landslides, one of the essential tools that can be used to manage and plan the development of human settlements is landslide susceptibility. The two metaheuristic algorithms explored in this paper are the SCE and VSA algorithms used to optimize the artificial neural network (ANN) model. By integrating the two algorithms with the artificial neural network model, we try to determine its optimum computational parameters in order to generate the landslide susceptibility mapping for the Kurdistan province in Iran. Sixteen causative factors of landslides are included in the spatial database. The landslide susceptibility maps were generated in a GIS medium, and in order to evaluate the employed predictive models, the criterion of the area under the curve (AUC) was employed. This investigation includes 1072 landslide events, which are divided as follows: one-third as testing data and two-thirds as training data (i.e., a 75:25 ratio). The results indicate that after using the abovementioned algorithms, the AUC increased noticeably from 0.708 to 0.788 for SCE-MLP and from 0.744 to 0.818 for VSA-MLP in the training phase. The criterion of the area under the curve was utilized in order to evaluate the accuracy of the employed probabilistic models. Incidentally, the comparable AUCs calculated for the VSA-MLP testing databases and the obtained AUCs were 0.818, 0.801, 0.791, 0.786, 0.784, 0.778, 0.777, 0.776, 0.754 and 0.744, respectively for population size in training databases equal to 50, 350, 500, 150, 450, 200, 400, 300, 100 to and 250. Also, in case of SCE-MLP, the training and testing AUC were found (0.878, 0.865, 0.851, 0.850, 0.819, 0.816, 0.815, 0.781, 0.760, and 0.756) and (0.788, 0.782, 0.759, 0.744, 0.727, 0.720, 0.718, 0.713 and 0.708). The best fit for swarm size conditions of the SCE-MLP and VSA-MLP model showed 150 and 350, respectively. The acquired results indicate that the VSA-ANN model has a better predictive capability in optimization of the structure of the artificial neural network model and computational parameters in comparison with the SCE-ANN. [ABSTRACT FROM AUTHOR]

This study explores the intricate integration and synchronization of supplier selection with the optimal scheduling of multi-mode resource-constrained projects, which is a genuine and complex challenge prevalent in the construction industry, by proposing new multi-objective mathematical modeling considering various items. Within this context, a multifaceted network of concurrent projects (multi-project) is examined with different suppliers' resources (multi-supplier) to minimize the overall projects' delay times and associated costs. The mathematical model formulation also incorporates diverse implementation modes (multi-mode) and the time value of money (TVM). To use and unravel the complexities of the proposed model, two distinct algorithms, including a multi-objective whale optimization algorithm (WOA) based on the Pareto archive and the well-known non-dominated sorting genetic algorithm II (NSGA-II), are employed. The algorithms were subjected to a comparative analysis of several sample problems and evaluated against multi-objective criteria, including quality metric (QM), diversity metric (DM), spacing metric (SM), number of solutions (NOS), mean Ideal distance (MID), and computational time. The evaluation reveals that the tailored multi-objective WOA outperforms NSGA-II, exhibiting greater solution precision and diversity. The WOA demonstrates an enhanced ability to efficiently explore the problem's feasible solution space, albeit at the increased computational time to pinpoint optimal solutions. Notably, the validity and practicality of the proposed model and method were field-tested within the context of construction projects in Iran, with the obtained results juxtaposed against the real-world data. The comparative analysis indicates that implementing the scheduling approach and solution methodology espoused by the multi-objective WOA led to significant improvements, with financial gains of up to 6% and time savings reaching 16%. Overall, this research substantiates the proposed model and algorithms' benefits in reducing project costs and delays, offering valuable insights for construction industry practitioners. [ABSTRACT FROM AUTHOR]

In this study, the optimization of the location of oil production and injection wells, the number of production and injection wells, and the economic evaluation in the water injection process has been carried out using three algorithms: PSO, GA, and HGAPSO. Optimizing the oil extraction process by optimizing well locations, the optimal number of production and injection wells, and economic evaluation can improve the performance and efficiency of the process. In this study, the optimization of the water injection process into oil reservoirs using the PSO, GA, and HGAPSO algorithms has been addressed. Additionally, economic evaluation, considering costs and profits from oil production using the best-optimized parameters, has been conducted, and the best Net Present Value (NPV) has been calculated. Initially, the optimal well location problem is modeled, taking into account reservoir characteristics and geological conditions. Using the PSO and GA algorithms, the best well location and number of water injection wells have been obtained, leading to improved efficiency and cost reduction. Then, the GA algorithm is used to combine and modify viewpoints and optimize the number and location of wells. This algorithm explores optimal solutions in the well location space. Economic evaluation is performed using the best-optimized parameters by the algorithms. Costs of water injection, oil production costs, and profits from increased oil production are considered. By calculating the best Net Present Value (NPV), optimal decisions regarding the water injection process and enhancing the performance of oil reservoirs are made. Simulation results indicate that the GA algorithm outperforms other algorithms and can be used as a robust and effective method for optimizing well locations and the number of production and injection wells in the oil injection process. Ultimately, this study assists the oil industry in making better decisions about optimizing water injection and improving the efficiency and profitability of the process using the introduced optimization algorithms. [ABSTRACT FROM AUTHOR]

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI. [ABSTRACT FROM AUTHOR]

Accuracy in grade estimation and 3D modelling of epithermal gold deposits are critical for efficient resource management and economic decision-making. This research presents an optimal approach that combines support vector machine (SVM) and metaheuristic algorithms to improve the accuracy of grade estimation and 3D modelling in the Zailik area, located in the northwestern part of Iran. The proposed method applies the capability of SVM to predict the Au grade in a block model based on a comprehensive set of geological, geochemical and geophysical data. To enhance the performance of SVM, it was integrated with metaheuristic algorithms such as firefly algorithm (FFA) and particle swarm optimisation (PSO), which optimise the model parameters distance (w) and error values (ξ) to achieve the best possible results. Accordingly, the hybrid methods (SVM-PSO & SVM-FFA) were found to be more accurate than the single SVM method. The accuracy of both hybrid models was evaluated using multiple statistical functions such as determination coefficient (R2) and root mean square error (RMSE), which showed the highest determination coefficient (R2 = 0.839) and the lowest error function (RMSE = 0.088) for SVM-FFA compared to other methods, providing the most accurate modelling and appropriate for estimating Au grade with the minimum error value. To validate the effectiveness of the proposed method in the area, we compared its performance with conventional grade estimation and 3D modelling techniques with the results of a geophysical method, induced polarisation-resistivity survey (IP/RS), as well as lithology and alterations related to Au mineralisation in the area. Using geophysical methods the grade estimation of Au in various zones was facilitated by discriminating the siliceous rock-associated Au zones from sulphide-associated ones, which was also confirmed by the created hybrid model indicating the east and northern parts of the vein as high-grade Au zones. [ABSTRACT FROM AUTHOR]

Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and − 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to − 344, − 332.8, and − 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction. [ABSTRACT FROM AUTHOR]

Optimizing resource utilization and train scheduling is essential to satisfy passengers and reduce operating costs. This study develops the train schedule under scenario-oriented stochastic conditions. The proposed approach is a multi-objective mathematical-based mixed integer linear programming (MILP) approach; the objective is to minimize the average passenger expectation and the total number of train operation cycles. The non-dominated sorting genetic algorithm (NSGA-II) has been developed with multi-crossover and multi-mutation operators, then hybrid with simulating annealing (SA) operator (NSGA-II-SA). The model with four meta-heuristic algorithms has been technically analyzed. In a case study, the train schedule in the double-track rail network of the Tehran–Mashhad railway of Iran has been compared with the golden point. Experimental results show that a proposed approach can suitably fit the problem considering important metrics with an improvement of %7.34 and %6.89 for the average passenger waiting time and the total number of train operation cycles, respectively. [ABSTRACT FROM AUTHOR]

The unplanned development of agricultural land and urban areas poses threats to water quality, which can lead to the death of the aquatic species in rivers. The present study developed a novel framework by combining a soil and water assessment tool (SWAT) and evolutionary algorithms to optimize the cultivation pattern at the catchment scale in the Tajan River basin, with the aim of mitigating the environmental impacts of surface runoff from farms. This river basin is located in northern Iran, where quick agricultural development is one of the environmental challenges. We utilized a SWAT to simulate the nitrate concentrations for different crops at the river basin scale by applying the Nash–Sutcliffe model efficiency coefficient (NSE) as a measurement index. Then, a novel model was developed to optimize the cultivation pattern by applying different metaheuristic algorithms. Fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) was applied as a decision-making system to select the best optimization algorithm. The results demonstrated that using a SWAT in the optimization model structure is a robust method for the design of an optimal cultivation pattern. The Nash–Sutcliffe model efficiency coefficient (NSE) was 0.74, demonstrating the robust predictive skills of the water quality model. The decision-making system indicated that particle swarm optimization and shuffled complex evolution were the best evolutionary algorithms to optimize the cultivation pattern using the proposed method. The proposed method opens a new window regarding the optimization of cultivation patterns in agriculture and provides an environmental-based optimization to design cultivation patterns on the catchment scale. [ABSTRACT FROM AUTHOR]

Due to the complex nature of processing units and the variability in raw oil properties, traditional first-principle methods are not always effective in predicting the output of these units without adapting them. Data-driven models have become popular alternatives because they are more adaptable to the variability in processing units, especially when large amounts of data are available. Feature selection is a crucial aspect of data-driven modeling, especially for processing units. It involves identifying and selecting the most important variables influencing the unit's output. The selected variables are then used as inputs to the model, which helps to reduce noise and improve model accuracy. Accordingly, this article presents a hybrid method that combines feature selection with data-driven modeling to create an accurate model for predicting the output of a Residue Fluid Catalytic Cracking (RFCC) process unit. The Metaheuristic Ant Colony Optimizer (MACO) algorithm was used to extract the best subset of features from the collected data, which were then used with the Artificial Immune Genetic Algorithm with Local Vaccination (AIGA-LV) method to select the optimal features. Finally, the Random Forest (RF) method was used to predict the products of the RFCC process unit. The data were collected from a refinery plant in central Iran for nine months. The proposed model was efficient in feature selection and flexible in handling different scenarios and products. It improved the accuracy to 99.17% at its best and improved the primary model by 3–20%. Therefore, this method could be useful for predicting the output of various industrial processes, especially the RFCC and FCC process units. [ABSTRACT FROM AUTHOR]

Reservoirs are crucial for water storage, flood control, and electricity generation. At the same time, evaporation in dam reservoirs causes water losses. This study evaluates the effect of weather conditions on the prediction of evaporation from the dam reservoir, and there are several developed models to predict evaporation under different scenarios. Using the deep neural network model long short term memory (LSTM) with a series of modern meta-heuristic algorithms, the present research aims to achieve an acceptable accuracy on weather parameters. The present study investigates the ability of the LSTM deep neural network and meta-heuristic algorithms of Artificial Bee Colony (ABC), Horse Herd Optimization (HOA), and Marine Predators (MPA) to develop an evaporation rate forecasting model for a tropical area in the Dez Dam reservoir, Khuzestan province, Iran. To investigate the effect of different input variable patterns on the prediction accuracy of the case model, twenty-eight scenarios were investigated for the input architecture along with the LSTM model and its combination with meta-heuristic algorithms. The models include ABC-LSTM, LSTM-HOA, and LSTM-MPA, respectively. The results showed that the combined LSTM-MPA model with RMSE, MAE, KGE, and WI evaluation criteria equal to 81.926 mm/month, 53.684 mm/month, 0.850, and 0.816, respectively, performs better than other meta-heuristic models. The examined deep meta-heuristic models are suggested for predicting evaporation from the Dez dam reservoir with limited information because the results show that they have more appropriate prediction capability than the LSTM Model. [ABSTRACT FROM AUTHOR]

Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

Developing a susceptibility map is a crucial primary step for dealing with undesirable natural phenomena, gully erosion included. On the other hand, recent computational progress call for employing new methodologies to keep the solutions updated. In this work, the performance of a conventional artificial neural network (ANN) is improved by applying a metaheuristic algorithm (symbiotic organisms search—SOS) for generating the gully erosion susceptibility map of an area in Golestan Province, Northern Iran. A geo-database is created from the gully erosion inventory and twenty conditioning factors. After analyzing the interrelated relationships between the geo-database components, training and testing data sets are formed. The models are executed with proper configurations and according to the results, the SOS algorithm could enhance the training accuracy of the ANN from 92.8% to 98.4%, and testing accuracy from 89.8% to 91.4%. In addition, comparing the performance of the SOS with shuffled complex evolution (SCE-NN) and electromagnetic field optimization (EFO-NN) algorithms revealed the greater accuracy of the SOS. However, the SCE-NN and EFO-NN performed more accurately than conventional ANN. Therefore, it can be concluded that the use of metaheuristic techniques may improve the prediction ability of the ANN in gully erosion susceptibility mapping. Finally, a monolithic equation is extracted from the SOS–ANN model to be used as a predictive formula for similar purposes. [ABSTRACT FROM AUTHOR]

Access to sufficient and safe water particularly in vulnerable countries is one of the most important goals targeted by the United Nations for 2030. The agricultural water supply is crucial in arid areas such as Iran. In water supply projects, employers usually seek less duration and cost, and higher quality, however, contractors seek a reduction of fluctuations in project resources, especially human resources, and these goals may be in conflict with each other. Therefore, finding an optimal project schedule considering different project objectives is vital for managers to achieve stakeholders' satisfaction. In this study, the four main project objectives including time, cost, quality, and resource leveling as well as the activity dependencies and resource constraints are addressed through fuzzy optimization. The proposed model was first implemented on a small-sized project instance for validation; then, the model was solved by the Nondominated Sorting Genetic (NSGA-II) and Multi-objective Particle Swarm Optimization algorithms using the information of a real-world agricultural water supply project. The results of the first Pareto set for different alpha-cuts in relation to the fuzzy quality factor show a set of solutions and different alternatives for project implementation which leads to a number of execution modes each of which may result in achieving the project objectives. Finally, the most ideal solution with a duration of 178 days was selected using the fuzzy TOPSIS method. The findings demonstrate the validity of the proposed model concerning the needs of agricultural water supply organizations. [ABSTRACT FROM AUTHOR]

Hydrometric stations are important in most countries because of the application and importance of the data obtained from these stations. It is necessary to choose the best place for their establishment according to the cost of constructing hydrometric stations. The aim and innovation of this research are to optimize the location of hydrometric stations using Bat's meta-heuristic algorithm and interpolation methods, which information transfer entropy theory and Bat's algorithm were used to maximize the average amount of information transfer entropy. For this purpose, the data of 43 hydrometric stations of Karkheh basin in western Iran in period of 1991–2015 were used. In this research, two scenarios were investigated in order to improve the entropy of information transmission between stations. In the first scenario, using the kriging method to prepare the flow distribution map in the region and choosing normal kriging with spherical variogram as the best model to fit the average annual flow data and using the Bat algorithm to increase the correlation coefficient between the data and assuming no none of the available stations, 43 points were used to redeploy stations with higher average entropy in the region. The results of this scenario showed the concentration of new stations in the central and eastern areas of the basin. In the second scenario, the amount of entropy of information transfer at the regional level was calculated and 18 potential points were recommended for the establishment of new stations. The obtained variogram for the discharge of the basin showed that the range of influence is low and it is necessary to establish the stations at a close distance. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

Unsupervised clustering methods are used as data-driven models for mapping of mineral prospectivity (MPM). One of the challenging problems for modeling high potential mineral prospects using these algorithms is to select proper cluster centroids in order to locate the same geospatial objects into reasonable clusters. To overcome this kind of problems arise from traditional clustering algorithms (e.g. K-means), the combination of harmony search (HS) and artificial bee colony (ABC) metaheuristic optimization algorithms with K-means have been firstly developed in this study which can be used for selection of optimum cluster centroids for highlighting favorable targets in prospecting stage of mineral explorations. This stage is critically important for exploration of undiscovered mineral deposits, because the time and cost are two substantial factors in regional-scale exploration programs. This study stated that the applying hybrid data-driven clustering models (e.g., HS-KM and ABC-KM) are operational tools for identifying high-favorable areas linked to porphyry Cu systems in prospecting stage. Moreover, the results demonstrated that the properly selection of geospatial exploration features has strong impact on the modeling of mineral prospectivity in Kerman porphyry belt, Iran and cause significant decrease in systemic uncertainty linked to MPM. [ABSTRACT FROM AUTHOR]

There is increasing attention to the sustainable development of supply chain (SC) and reverse logistics (RL) in the contemporary competitive economy, notably in the food sector, by scholars and stakeholders. This study investigates a sustainable closed-loop supply chain (CLSC) for fish due to its high value in the family food basket, its perishability, and the importance of waste product recycling. A multi-objective mathematical model is developed under uncertainty and sustainability criteria to optimize production rates with the aim of better distribution among different demand markets, total costs, social issues, and negative environmental effects (e.g., CO2 emissions and unused/waste products). A combination of exact, meta-heuristic, and hybrid meta-heuristic algorithms are used to solve the suggested model. Then, the optimal solutions are found using the Taguchi method by evaluating the best initial replies. The solutions are evaluated based on various performance metrics. The analysis of variance (ANOVA) and the "filtering/displaced ideal solution" methods determine the best solution approach. Moreover, a case study with a trout CLSC in Northern Iran is examined. In addition, the Lingo software utilizes the ε-constraint method to evaluate and check the performance of the algorithms under different levels of uncertainty. Finally, sensitivity analyses are carried out to confirm the efficacy of the proposed algorithms. The findings demonstrate the proposed network's outstanding consistency with the algorithms used and its application and efficiency. [ABSTRACT FROM AUTHOR]

Flyrock is known as one of the main problems in open pit mining operations. This phenomenon can threaten the safety of mine personnel, equipment and buildings around the mine area. One way to reduce the risk of accidents due to flyrock is to accurately predict that the safe area can be identified and also with proper design of the explosion pattern, the amount of flyrock can be greatly reduced. For this purpose, 14 effective parameters on flyrock have been selected in this paper i.e. burden, blasthole diameter, sub-drilling, number of blastholes, spacing, total length, amount of explosives and a number of other effective parameters, predicting the amount of flyrock in a case study, Songun mine, using linear multivariate regression (LMR) and artificial intelligence algorithms such as Gray Wolf Optimization algorithm (GWO), Moth-Flame Optimization algorithm (MFO), Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO) and Multi-Verse Optimizer (MVO). Results showed that intelligent algorithms have better capabilities than linear regression method and finally method MVO showed the best performance for predicting flyrock. Moreover, the results of the sensitivity analysis show that the burden, ANFO, total rock blasted, total length and blast hole diameter are the most significant factors to determine flyrock, respectively, while dynamite has the lowest impact on flyrock generation. [ABSTRACT FROM AUTHOR]

Controlling and maintaining public health in the face of diseases necessitates the effective implementation of response strategies, including the distribution of vaccines. By distributing vaccines, vulnerable populations can be targeted, individuals can be protected, and the spread of diseases can be minimized. However, managing vaccine distribution poses challenges that require careful consideration of various factors, including the location of distribution facilities. This paper proposes a novel model that combines locationallocation problems with queueing systems methodologies to optimize the efficiency of vaccine distribution. The proposed model considers factors such as uncertain demand, varying service rates, depending on the system state. Its primary objective is to minimize total costs, which encompass the establishment and adjustment of the service mechanism, travel times, and customer waiting time. To forecast customer demand rates, the model utilizes time-series techniques, specifically the seasonal Autoregressive Integrated Moving Average model. In order to tackle large-scale problems, a total of 16 newly developed metaheuristic algorithms are employed, and their performance is thoroughly evaluated. This approach facilitates the generation of solutions that are nearly optimal within a reasonable timeframe. The effectiveness of the model is evaluated through a real-life case study focused on vaccination distribution in Iran. Furthermore, a comprehensive sensitivity analysis is conducted to demonstrate the practical applicability of the proposed model. The study contributes to the advancement of robust decision-making frameworks and provides valuable insights for addressing location-related challenges in health systems [ABSTRACT FROM AUTHOR]

Urban water distribution networks are crucial infrastructures for providing essential services to society, but their exorbitant costs and limited water resources make their optimization a critical research area. Optimal management and design of these networks can help to reduce costs and enhance their efficiency while meeting technical, economic, and quality standards. In particular, the management of network pressure is critical for reducing leakage in water distribution systems. Thus, this study aimed to investigate two objective functions for optimizing the water distribution network: (i) minimizing costs while considering the number of pressure-relief valves, and (ii) minimizing network pressure by observing the optimal pressure range. To achieve this, the Nondominated Sorting Differential Evolution (NSDE) multi-objective metaheuristic algorithm was employed as the optimization tool, and a computer program was written in MATLAB software for solving the optimization models. EPANET software was also used for hydraulic simulation of the water distribution network. The efficiency and capabilities of these models were tested on the case study of the third district of Mashhad in Iran. The results indicated that the installation and adjustment of pressure-relief valves in accordance with the positions and optimal settings of the output of the proposed models significantly improved the desired goals, particularly the average pressure of the network. As an example of optimization, the study achieved a 56.12% reduction in pressure compared to the case without a plan, considering five pressure-relief valves. [ABSTRACT FROM AUTHOR]

One of the basic steps in water resources management and planning according to population increase and lack of water resources in Iran is to optimize the use of dam reservoirs. In this research, the effect of meteorological droughts on the optimization of the Aydoghmoush dam reservoir in the northwest of Iran was evaluated by applying metaheuristic algorithms under the impact of future climate change. Three models and two scenarios of SSP2-4.5 and SSP2-8.5 of the sixth IPCC report, and the LARS-WG downscaling model were used for Aydoghmoush dam weather station for the base period (1978-2014) and future periods of 2022-2040 and 2070-2100. The inflow and outflow of the dam, as well as the optimal utilization of the dam reservoir, were evaluated using standalone, and hybrid mode of genetic, slime mold, and ant colony algorithms. Results of the best release scenario (SSP2-8.5) showed that the annual rainfall in the future periods will decrease by 8.9 mm, and 14.5 mm, respectively, compared to the base period. The objective function of optimizing the use of the dam reservoir was defined as minimizing the sum of squared relative deficiencies in each month and maximizing the reliability in the statistical period of 2011-2021. The results showed that in terms of time reliability, vulnerability, and stability, the hybrid slime mold-genetic algorithm was better than other algorithms with values of 0.73, 0.32, and 28.78. Prediction of the dam's inflow and outflow using the hybrid slime mold-genetic algorithm indicated high accuracy compared to other models by 13% and 19% errors, respectively. [ABSTRACT FROM AUTHOR]

Purpose: A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures. Design/methodology/approach: As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods. Findings: The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software. Originality/value: The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time. [ABSTRACT FROM AUTHOR]

Relief operations and planning to implement them are essential due to the unpredictability of natural disaster occurrences and their concomitant damages. The Crisis and Disaster Management Organization's responsibility is to execute plans, coordinate, control relief, and rescue operations to reduce the impacts and consequences of these disasters. Thus, this organization should attend to the struck regions' needs with their maximum power as soon as possible. This study has initially reviewed the latest available literature regarding the humanitarian supply chain to determine the research gap. Accordingly, in this research, a mathematical model with a leader-follower approach for relief delivery in the crisis response phase was designed, which took into account the policies of the country of Iran for the distribution of relief items. The amount of inventory of suppliers was also considered as uncertain. Then, to validate it, a numerical example was solved by the metaheuristic algorithm MOEA/D. The results indicated that the designed model is valid and the selected algorithm to solve it has an acceptable performance. Finally, some effective parameters were selected in the model and the sensitivity of the model was evaluated based on their changes. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to study the Multi-Skill Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where (1) durations of activities depend on the familiarity levels of assigned workers, (2) more efficient workers demand higher per-day salaries, (3) projects have different due dates and (4) the budget of each period varies over time. The proposed model is bi-objective, and its objectives are minimization of completion times and costs of all projects, simultaneously. Design/methodology/approach: This paper proposes a two-phase approach based on the Statistical Process Control (SPC) to solve this problem. This approach aims to develop a control chart so as to monitor the performance of an optimizer during the optimization process. In the first phase, a multi-objective statistical model has been used to obtain control limits of this chart. To solve this model, a Multi-Objective Greedy Randomized Adaptive Search Procedure (MOGRASP) has been hired. In the second phase, the MSRCMPSP is solved via a New Version of the Multi-Objective Variable Neighborhood Search Algorithm (NV-MOVNS). In each iteration, the developed control chart monitors the performance of the NV-MOVNS to obtain proper solutions. When the control chart warns about an out-of control state, a new procedure based on the Conway's Game of Life, which is a cellular automaton, is used to bring the algorithm back to the in-control state. Findings: The proposed two-phase approach has been used in solving several standard test problems available in the literature. The results are compared with the outputs of some other methods to assess the efficiency of this approach. Comparisons imply the high efficiency of the proposed approach in solving test problems with different sizes. Practical implications: The proposed model and approach have been used to schedule multiple projects of a construction company in Iran. The outputs show that both the model and the NV-MOVNS can be used in real-world multi-project scheduling problems. Originality/value: Due to the numerous numbers of studies reviewed in this research, the authors discovered that there are few researches on the multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with the aforementioned characteristics. Moreover, none of the previous researches proposed an SPC-based solution approach for meta-heuristics in order to solve the MSRCMPSP. [ABSTRACT FROM AUTHOR]

The goal of this study is mapping flood risks over Golestan province in Iran using one of the artificial intelligence methods called multivariate adaptive regression splines (MARS). In this sense, 14 flood conditioning factors were considered and the maps were made in ArcGIS. Additionally, two novel metaheuristic algorithms namely cat swarm optimization (CSO) and water cycle algorithm (WCA) were applied to optimized the MARS parameters. According to the results, the area under the curve provided by receiver operating characteristic curve illustrated the accuracy of 94.5% for the integrated MARS-WCA. As regards the MARS-WCA model, a total area of 44.74% was identified as highly susceptible for flooding. In addition, to determine the maximum influence of input variables on mapping flood risks, the sensitivity analysis was performed. By performing sensitivity analysis, altitude and slope with NDVI were the three important variables, respectively, for spatially flash flood prediction. [ABSTRACT FROM AUTHOR]

Snow avalanches impose a considerable threat to infrastructure and human safety in snow bound mountain areas. Nevertheless, the spatial prediction of snow avalanches has received little research attention in many vulnerable parts of the world, particularly in developing countries. The present study investigates the applicability of a stand alone convolutional neural network (CNN) model, as a deep learning approach, along with two metaheuristic algorithms including grey wolf optimization (CNN-GWO) and imperialist competitive algorithm (CNN-ICA) in snow avalanche modelling in the Darvan watershed, Iran. The analysis was based on thirteen potential drivers of avalanche occurrence and an inventory map of previously documented avalanche occurrences. The efficiency of models' performance was evaluated by Area Under the Receiver Operating Characteristic curve (AUC) and the Root Mean Square Error (RMSE). The CNN-ICA model yielded the highest accuracy in both training (AUC = 0.982, RMSE = 0.067) and validation (AUC = 0.972, RMSE = 0.125) steps, followed by the CNN-GWO model (AUC of 0.975 for training, RMSE of 0.18 for training, AUC of 0.968 for validation, RMSE of 0.157 for validation). However, the standalone CNN model showed lower goodness-of- fit (AUC = 0.864, RMSE = 0.22) and predictive performance (AUC = 0.811, RMSE = 0.330). The approach utilized in this study is broadly applicable for identifying areas where avalanche hazard is likely to be high and where mitigation measures or corresponding land use planning should be prioritized. [ABSTRACT FROM AUTHOR]

Schools as social bases and children's centers are among the most vulnerable areas to flooding. Flood susceptibility mapping is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive accuracy. This study aims to develop the innovative hybrid models by coupling the support vector regression (SVR), statistical approaches, and two meta-heuristic algorithms, whale optimization algorithms (WOA) as well as grey wolf optimizer (GWO). According to the proposed methodology, a hybrid feature of SVR and frequency ratio (FR-SVR) is optimized by applying the GWO and WOA optimization algorithms to generate the maps related to flood susceptibility. The method was utilized for the Ardabil Province located in southwestern Caspian Sea precincts of which faced devastating floods. The GIS database including 147 ground control locations of flooded zones and nine factors which influence flood were utilized to learn and ascertain the validity of the models. Three statistical metrics namely, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristic curve (AUC) were computed for the developed models in order to estimate prophetically. The results indicated that the meta-optimized FR-SVR-GWO as well as FR-SVR-WOA models exceeded the FR-SVR and FR models in training (RMSEFR-SVR-WOA = 0.2016, RMSEFR-SVR-GWO = 0.1885, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.88) and validation (RMSEFR-SVR-WOA = 0.2025, RMSEFR-SVR-GWO = 0.1986, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.87) phases. The FR-SVR-WOA and FR-SVR-GWO models were very competitive regarding AUC and RMSE values, but the FR-SVR-WOA model reproduced greater flood susceptibility rates and was considered for identifying vulnerability of schools to flood events. To this end, number of schools, number of students in each school, and the area of the school building were taken into account to generate the vulnerability map. The results demonstrated that schools with the highest and lowest vulnerability to flooding were mostly located in southeastern and central parts of the Ardabil Province, respectively. [ABSTRACT FROM AUTHOR]

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided. [ABSTRACT FROM AUTHOR]

Extracting Total Precipitable Water Vapor (TPW) and its variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) data on a local scale is challenging. Meta-heuristic algorithms have a high ability to solve complex engineering problems of hydrology and climatic studies. Hence, the present study extracts TPW values using meta-heuristic algorithms for MODIS surface reflectance data. In this regard, radiosonde observations of six stations in the western part of Iran during 2019 and 2020 were used as the ground truth data for training algorithms and evaluating the results. The results were also validated with Global Positioning System (GPS) TPW data of Tehran-Mehrabad station. Moreover, MODIS surface reflectance (MOD021) and cloud mask (MOD35) products were prepared to distinguish cloudy days from non-cloudy, along with MODIS TPW (MOD05) for corresponding dates. Meta-heuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search (HS), Invasive Weed Optimization (IWO), Gray Wolf Optimization (GWO), and Grasshopper Optimization Algorithm (GOA), were applied to improve the performance of MOD05 algorithm. The results showed the proper performance of all the selected algorithms. Nevertheless, the GOA algorithm acquired the best result. The Root Mean Square Error (RMSE) and R values obtained from comparing GOA results with radiosonde data were 2.96 mm and 0.82. Those values for comparing GOA results with GPS data were 2.94 mm and 0.83, respectively. However, MODIS TPW, RMSE, and R showed 6.53 mm and 0.69, respectively, compared to radiosonde data and 6.55 mm and 0.68 compared to GPS data. Overall, the results proved the proper performance of the used meta-heuristic algorithms in extracting TPW from MODIS measurements on a local scale. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved. [ABSTRACT FROM AUTHOR]

• Development of a hybrid soft computing model to predict the Runoff. • Preprocessing the signal of the time series of input variables using the Wavelet technique. • Design an MLPNN model and train it using the PSO technique. A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science. Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e. , hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986-Aug 2017 in Dez River basin (MRDR m), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e. , without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (α) for the original MRDR m time series is achieved at 100. Then, the PACF (partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGEʹ) of 0.83, volumetric efficiency (VE) of 0.91, Nash–Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGEʹ of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s. [ABSTRACT FROM AUTHOR]

Dust pollution poses significant risks to human health, air quality, and food safety, necessitating the identification of dust occurrence and the development of dust susceptibility maps (DSMs) to mitigate its effects. This research aims to detect dust occurrence using satellite images and prepare a DSM for Bushehr province, Iran, by enhancing the attentive interpretable tabular learning (TabNet) model through three swarm-based metaheuristic algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and hunger games search (HGS). A spatial database incorporating dust occurrence areas was created using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2022, including 15 influential criteria related to climate, soil, topography, and land cover. Four models were employed for modeling and DSM generation: TabNet, TabNet-PSO, TabNet-GWO, and TabNet-HGS. Evaluation of the modeling results using performance metrics indicated that the TabNet-HGS model outperformed the other models in both training (mean absolute error (MAE) = 0.055, root-mean-square error (RMSE) = 0.1, coefficient of determination (R2) = 0.959), and testing (MAE = 0.063, RMSE = 0.114, R2 = 0.947) data. Following TabNet-HGS, the TabNet-PSO, TabNet-GWO, and TabNet models demonstrated progressively lower accuracy. The validation of the DSM was performed by assessing receiver operating characteristic (ROC) curves, revealing that the TabNet-HGS, TabNet-PSO, TabNet-GWO, and TabNet models exhibited the highest modeling accuracy, with corresponding area under the curve (AUC) values of 0.994, 0.986, 0.98, and 0.832, respectively. These results highlight the enhanced accuracy of dust susceptibility modeling achieved by integrating swarm-based metaheuristic algorithms with the TabNet model. The dust susceptibility map provides valuable insights into the sources, pathways, and impacts of dust particles on the environment and human health in the study area. [Display omitted] • Satellite imagery and comprehensive data yield precise dust susceptibility maps. • Swarm-based metaheuristics enhance TabNet accuracy for accurate dust modeling. • TabNet-HGS model outperforms others, improving dust susceptibility mapping. [ABSTRACT FROM AUTHOR]

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data. [ABSTRACT FROM AUTHOR]

Mechanical properties of carbonate rocks (elasticity modulus (E) and uniaxial compressive strength (UCS)) are important properties in tunneling, rock excavation and rock drilling blasting. Determination of these parameters using testing rock cores is almost difficult due to the discontinuities presence and it requires well-prepared cylindrical core samples. In addition, the testing procedure is expensive and time consuming. Thus, indirect tests are often utilized to evaluate the mechanical properties. In this research, a new technique for data processing called support vector regression (SVR) improved by metaheuristic algorithms (harmony search (HS), grey wolf optimizer (GWO), cuckoo search (CS), dolphin echolocation (DE) and genetic algorithm (GA)) to estimate of mechanical properties of carbonate rocks from physical properties and petrographic characteristics is applied. The techniques were employed in an open access literature. (case study: Koohrang's third tunnel path, Iran). In these techniques, petrographic characteristics (allochem percent, carbonate percent, dolomite percent and grain size) and physical properties (saturated unit weight (γsat), S-wave velocity (Vs), dry unit weight (γd), P wave velocity (Vp)) were used as the inputs, while the mechanical properties of carbonate rocks were the outputs. Different performance concepts were used to compare the prediction models performance. The outcomes obtained show that the SVR-HS technique has robust potential for the prediction of mechanical properties of carbonate rocks based on physical properties and petrographic characteristics with high accuracy. [ABSTRACT FROM AUTHOR]

Monitoring and assessment of groundwater quality (GWQ) as an important freshwater source for drinking purposes in urban and rural regions of developing countries due to rapidly increasing contamination is one of the concerns of water managers. Therefore, developing an efficient intelligent model for analyzing GWQ could help hydro-environmental engineers for sustainable water supply. The current research investigated the applicability of a novel nature-inspired optimization algorithm hybridized with multi-layer perceptron artificial neural network based on gray wolf optimization (GWO) for estimating dissolved oxygen (DO) total dissolved solid (TDS) and turbidity parameters at Asadabad Plain, Iran, and results are compared with the stand-alone multi-layer perceptron artificial neural network (MLPANN), generalized regression neural network (GRNN), and multiple linear regression (MLR) approaches. Evaluation of performance of models is carried out using various statistical indices like relative root mean square error, Nash-Sutcliffe efficiency, and correlation coefficient. Based on the results obtained, it is found that the hybrid GWO-MLPANN is a beneficial GWQ forecasting tool in accordance to high performance accuracy. Also, the study found that the superiority of the applied meta-heuristic algorithm (GWO) in improving the performance accuracy of the stand-alone artificial intelligence techniques in modeling the GWQ parameters. [ABSTRACT FROM AUTHOR]

As a frequent natural disaster, landslides incur significant economic and human losses worldwide. The main idea of this paper is to propose novel integrative models for landslide susceptibility evaluation in a prone area of Chaharmahal va Bakhtiari Province in Iran. To do this, four metaheuristic techniques, namely chimp optimization algorithm (ChOA), crow search algorithm (CSA), satin bowerbird optimization (SBO), and water cycle algorithm (WCA) are used to supervise the training of an artificial neural network (ANN). A spatial database is created by 170 historical landslides and 14 most common conditioning factors. The models are optimized in terms of hyper-parameters and elite ones are then used to produce the susceptibility maps. During the training phase, all models could acquire a reliable understanding of the landslide pattern. The obtained accuracy index of area under the receiving operating characteristic curve (AUROC) showed that the maps produced by the WCA-ANN (AUROC = 0.925) and SBO-ANN (AUROC = 0.900) are more accurate than those of the ChOA-ANN (AUROC = 0.851) and CSA-ANN (AUROC = 0.855). Considering this assessment, the suggested hybrid models are reliable enough for landslide susceptibility modeling. Accordingly, the generated susceptibility map can be used for urban planning and generating alert systems over the studied area. [ABSTRACT FROM AUTHOR]

Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particle swarm optimizer (PSO) and whale optimization algorithm (WOA), are proposed for daily river flow prediction of the Taleghan River, which is the major source of potable water for Tehran, the capital of Iran. Gamma test (GT) was used for the determination of input variables for the models. The ANFIS-WOA model was found to exhibit the best performance in prediction of river flow according to root mean square error (RMSE ≈ 3.75 m3.s−1) and Nash-Sutcliffe efficiency (NSE ≈ 0.93). It improved the prediction performance of the classical ANFIS model by 6.5%. The convergence speed of ANFIS-WOA was also found to be higher compared to other hybrid models. The success of the ANFIS-WOA model indicates its potential for use in the simulation of highly nonlinear daily rainfall–runoff relationships. [ABSTRACT FROM AUTHOR]

Monitoring the water contaminants is of utmost importance in water resource management. Prediction of the total dissolved solid (TDS) is particularly essential for water quality management and planning in the areas exposed to a mixture of pollutants. TDS primarily includes inorganic minerals and organic matters, and various salts and increasing the concentration of TDS causes the esthetic problems. The reflection of the pollutant burden of the aquatic system can remarkably determined by TDS magnitudes. This study focuses on the prediction of TDS and several biochemical parameters such as Na, Ca, HCO3, and Mg in a river system. To overcome nonstationarity, randomness, and nonlinearity of the TDS data, a multi-step supervised machine learning evolutionary algorithm (MSMLEA) is proposed to improve the model's performance at two gaging stations, namely Rig-Cheshmeh and Soleyman-Tangeh, in the Tajan River, Iran. In addition, a hybrid model that recruits intrinsic time-scale decomposition (ITD) for frequency resolution of the input data as well as a multivariate adaptive regression spline (MARS) were adopted. A novel metaheuristic optimization algorithm, crow search algorithm (CSA), was also implemented to compute the optimal parameter values for the MARS model. To validate the proposed hybrid model, standalone MARS, empirical mode decomposition (EMD)-based models, and hybrid ITD-MARS as well as a MARS-CSA were considered as the benchmark models. Results suggest the ITD-MARS-CSA outperforms other models. [ABSTRACT FROM AUTHOR]

Estimating total precipitable water vapour (TPWV) from advanced microwave scanning radiometer 2 (AMSR2) measurements with reasonable accuracy is challenging. Therefore, this study aims to improve the AMSR2 TPWV algorithm using metaheuristic algorithms for the first time. Radiosonde observations of six stations in the western part of Iran during 2020 were used as reference data. Moderate Resolution Imaging Spectroradiometer (MODIS) products, including land surface temperature and cloud mask, were used in the TPWV extracting algorithm and in selecting clear-sky days, respectively. Five metaheuristic methods – genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and bat algorithm (BA) – were used, due to the ability of metaheuristic methods in modelling complicated issues such as AMSR2 TPWV estimation. The WOA, GWO, and PSO showed the best performance. Therefore, metaheuristic algorithms can be used to improve the AMSR2 TPWV algorithm with reasonable performance on a local scale. [ABSTRACT FROM AUTHOR]

This article proposes a deterministic model for a post-disaster scenario in an urban emergency medical services system to allocate the emergency vehicles to the patients and transfer them to the hospital. To solve the model, an exact approach called the -constraint method is applied to the problem. Since this problem belongs to the class of NP-hard problems, two metaheuristic algorithms, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective imperialist competitive algorithm (MOICA), are applied to large-scale problems. The performance of the algorithms is evaluated using computational experiments. Finally, the model is applied in a real-life case study for an expected earthquake in Iran and several managerial insights are extracted. [ABSTRACT FROM AUTHOR]

Stand-alone hybrid energy systems based on solar and energy storage are an effective option for rural areas to meet the load demand. The objective of the current work is to the optimal configuration of a stand-alone hybrid photovoltaic/battery energy storage system with the help of an efficient metaheuristic algorithm, improved harmony search, to supply electrical of a residential load in Iran. The objective function is a minimization of total life cycle cost (TLCC) subject to the reliability index (loss of load probability). The optimal configurations of hybrid systems are compared in respect of different losses of load probability (0 to 20%). Sensitivity analysis and effects of economic parameters based on photovoltaic and battery prices are carried out to study the possibility of the suggested scheme. The results show that, by increasing the reliability index from 0 to 20%, the optimal number of panels and batteries decreases by 52 and 1202. Also, it is found that the TLCC of the system and cost of system components are increased by decreasing of the reliability index value. [ABSTRACT FROM AUTHOR]

Summary: The main purpose of this study is to develop a sustainable smart energy management system for the desert climate, which aims to reduce energy expenses, energy consumption, and greenhouse gas (GHG) emissions via finding optimum power output and smart scheduling while considering users' uncertain behaviors. Moreover, the effectiveness of five metaheuristic optimization algorithms is analyzed and reviewed for presented system, which is modeled as a multiobjective function and contains over 1000 variables. The case study is city of Kashan located at the desert area of Iran with hot and dry climate. Presented system is established based on smart residential energy hub and home energy management system. Residential loads for a modern household are appropriately categorized and modelled. Ten different uncertain scenarios for users' energy consumption are simulated within the algorithm with considering users' comfort level simultaneously. Both energy cost and users' comfort deviation for studied multi‐energy system are formulated as a multiobjective function with two weighting factors. Our results present a comparison between different cases studied and the effects of uncertain power consumption on energy cost, comfort level, and computing time. Our findings indicate that the presented system with specified weighting factors is able to reduce energy expenses around 50% in different cases. Accordingly, due to a noticeable decrease in energy consumption, GHG emissions from fossil fuels are reduced remarkably considering the fact that only 1% of Iran's power supply is provided by clean energies. Results also illustrate that considering uncertainty has more effect on users' comfort level than energy cost. [ABSTRACT FROM AUTHOR]

Multi-objective models must be built to optimise the use of water supplies. In this study, the performance of the farmland fertility algorithm (FFA) and that of the Harris hawks optimisation (HHO) algorithm were evaluated and compared with each other to solve the two-objective problem of optimal operation of the Golestan dam reservoir under baseline (April 2006 to October 2018) and climate change conditions (April 2021 to October 2033) in Golestan Province, Iran. The operation rules were extracted by minimising vulnerability and maximising reliability indices under both conditions. The results showed that when considering a constant value of the reliability index (i.e. 80%), the rate of vulnerability changes in the multi-objective FFA (Moffa) is lower than that of the multi-objective HHO (Mohho) algorithm. Therefore, Moffa has performed better than the Mohho algorithm in the optimal operation of the Golestan dam reservoir under both baseline and climate change conditions. On the other hand, the comparison of the optimum rules obtained under the baseline and climate change conditions showed that supplying the water demands would be more concordant with the optimum rules under climate change conditions in both optimisation algorithms. Therefore, the Golestan dam reservoir could perform better under climate change conditions in the case of using both the mentioned algorithms. [ABSTRACT FROM AUTHOR]

Hedging policy is an applicable strategy for water resources systems with storage reservoirs to reduce the damage resulting from extended droughts. Optimizing the operation of parallel and cascade multireservoir systems is a challenging task because of its many complexities. This study proposes a two-stage approach for reaching both operating rule curves and rationing coefficients for multireservoir systems. In the first stage, initial solutions of the main problem are achieved by coupling the mixed-integer nonlinear programming (MINLP) with the particle swarm optimization algorithm. The achieved solutions are then adjusted in the second stage by implementing a distributed simulation–optimization model that efficiently models the system features. The proposed approach has been applied to gain optimal rule curves and rationing coefficients for the reservoirs of the Great Karun multireservoir system in southwestern Iran. The proposed model outperformed the other two—i.e., the distributed simulation–optimization model and the lumped MINLP model—in multireservoir system operation and was able to obtain the optimal hedging operating policy within a reasonable time. With this model, supply can be prioritized for various system demands. Also, the hedging operating policy from the proposed model significantly reduced the magnitude of failures during drought periods. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to design and regulate the Adaptive Neural Fuzzy Inference Systems (ANFIS) of supplier development, determine the importance of different categories of supplier development (SD), and suggest appropriate activities based on the results for the development of suppliers in the automotive industry. Design/methodology/approach: To design ANFIS, Grid Partitioning, Subtractive Clustering, and FCM have been used. Then, they have been regulated using Back Propagation (BP), Hybrid, Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods. A five-step procedure has been used for sensitivity analysis of the supplier development categories. By comparing the results of the regulated systems, the most appropriate ones were selected. Also, based on the supplier development score in different categories separately, the supplier development score was predicted for 53 strategic suppliers in the automotive industry. By sensitivity analysis, SD-related categories were prioritized to guide automotive industry manufacturers to use SD-related activities. Findings: Findings indicated that the FCM compared to the other two methods; meta-heuristic regulation methods compared to BP and Hybrid, and ACO, DE, and GA compared to PSO led to better results. The ACO in all systems, the DE in four systems, and the GA in two categories were identified as the dominant methods, while the PSO was not dominant in any of the categories. This finding implies the priority of meta-heuristic algorithms as ACO> DE> GA> PSO, based on the data of this study. The results of the correlation between the scores of the ANFIS and the average scores of the experts show that the designed ANFIS has high accuracy. Practical implications: The results of this study will direct manufacturers' investments and direct involvement in SD. The findings encourage manufacturers and suppliers of the Iranian automotive industry first to activities related to the development of environmental capabilities and then activities related to the development of three other categories. The managers of Iran's automotive industry are suggested to apply the activities related to the development of suppliers' environmental capabilities. These activities include evaluating the supplier's environmental performance and feedback, sharing environmental information, ethics and social responsibility, obtaining environmental and social certifications, developing programs to improve the quality of life of target communities, green procurement, and environmental awareness, logistics activities Inversion, and joint efforts to improve performance are sustainable. Originality/value: This study was one of the first in-house studies to compare the results of metaheuristic algorithms compatible with ANFIS in the field of SD. In addition, in terms of implementation, it offered suitable SD activities to car manufacturers. [ABSTRACT FROM AUTHOR]

In the present study, for the first time, a new framework is used by combining metaheuristic algorithms, decomposition and machine learning for flood frequency analysis under climate-change conditions and application of HadCM3 (A2 and B2 scenarios), CGCM3 (A2 and A1B scenarios) and CanESM2 (RCP2.6, RCP4.5 and RCP8.5 scenarios) in global climate models (GCM). In the proposed framework, Multivariate Adaptive Regression Splines (MARS) and M5 Model tree are used for classification of precipitation (wet and dry days), whale optimization algorithm (WOA) is considered for training least square support vector machine (LSSVM), wavelet transform (WT) is used for decomposition of precipitation and temperature, LSSVM-WOA, LSSVM, K nearest neighbor (KNN) and artificial neural network (ANN) are performed for downscaling precipitation and temperature, and discharge is simulated under present period (1972–2000), near future (2020–2040) and far future (2070–2100). Log normal distribution is used for flood frequency analysis. Furthermore, analysis of variance (ANOVA) and fuzzy method are employed for uncertainty analysis. Karun3 Basin, in southwest of Iran, is considered as a case study. Results indicated that MARS performed better than M5 model tree. In downscaling, ANN and LSSVM_WOA slightly outperformed other machine learning algorithms. Results of simulating the discharge showed superiority of LSSVM_WOA_WT algorithm (Nash-Sutcliffe efficiency (NSE) = 0.911). Results of flood frequency analysis revealed that 200-year discharge decreases for all scenarios, except CanESM2 RCP2.6 scenario, in the near future. In the near and far future periods, it is obvious from ANOVA uncertainty analysis that hydrological models are one of the most important sources of uncertainty. Based on the fuzzy uncertainty analysis, HadCM3 model has lower uncertainty in higher return periods (up to 60% lower than other models in 1000-year return period). [ABSTRACT FROM AUTHOR]

Due to climate change and urban growth, the demand for new freshwater sources, especially groundwater, is increasing in water-deficient countries like Iran. Therefore, this study aimed at groundwater potential mapping (GPM) of the Nahavand Plain, Iran, using an optimized adaptive neuro fuzzy inference system (ANFIS) in a geographic information system, with three metaheuristic optimization algorithms: differential evolution (DE), particle swarm optimization (PSO) and ant colony optimization (ACO). A spatial database was constructed using 273 spring locations and 14 groundwater conditioning factors. The optimization algorithms were evaluated using the receiver operating characteristic (ROC) technique. The ANFIS-DE, ANFIS-PSO and ANFIS-ACO models resulted in accuracy of 0.816, 0.809 and 0.758, respectively; the high and very high potential for groundwater springs covered 26% of the Nahavand Plain. The Root Mean Square Error (RMSE) for the training and validation datasets was lowest for the ANFIS-DE model compared to the other two models; and the ANFIS-PSO model had a higher convergence speed. These results may play an important role in sustainable groundwater management in the Nahavand Plain. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this study was to evaluate the use of an integrated life cycle assessment (LCA), artificial neural network, and metaheuristic optimization model to improve the sustainability of tomato-based cropping systems in Iran. The model outputs the combination of input usage in a tomato cropping system, which leads to the highest economic output and the least environmental impact. Methods: The LCA inventory was created using data from 114 open-field tomato farms in the Alborz Province of Iran during one growing period in 2015. Among all management components, the main focus was on irrigation management systems. The optimization problem was designed by integrating three indicators: carbon footprint (CF), benefit-cost ratio (BCR), and energy use efficiency (EUE) as the objective of field tomato production. The functional unit was 1 kg of tomato aligned with the system boundary of the cradle to market life cycle. Three artificial neural networks (ANNs) were applied to model relationships between the inputs and three indices (CF, BCR, and EUE) as the objective functions. Multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) were used to minimize the CF and maximize the BCR and EUE indicators. The abovementioned aims have been pursued by developing codes in MATLAB software. Results and discussion: CF, BCR, and EUE were calculated to be 0.26 kg CO2−eq (kg tomato)−1, 1.8, and 0.5, respectively. MOGA results envisage the possibility of an increase of 86% and 50% in the EUE and BCR and a 43% reduction in the CF of tomato production systems. Moreover, EUE and BCR increased by 83% and 49%, and CF was reduced by 39% from the optimum results obtained from the MOPSO algorithm. It was revealed that in order to optimize field tomato production with the target objectives of this study, a large additional use for irrigation pipes, plastic, and machinery in comparison to current situation is required, while a large reduction of biocide, chemical fertilizer, and electricity consumption is indispensable. Conclusions: According to the results of our study, it was concluded that the optimal solutions require a modernization of irrigation systems and a decrease in the consumption of chemical fertilizers and pesticides. The implementation of management options for such solutions is discussed. [ABSTRACT FROM AUTHOR]

Root zone temperature is one of the most important soil characteristics, controlling many of the physical, chemical and biological processes in the soil. Temperature varies by soil depth, and exerts a profound impact on plant germination and growth. In this study, the accuracy of two artificial intelligence models including support vector regression (SVR) and elman neural network (ENN) and their hybrids with firefly algorithm (SVR-FA and ENN-FA) and krill herd algorithm (SVR-KHA and ENN-KHA) was assessed in estimating soil temperature (Ts) at 5, 10, 20, 30, 50 and 100 cm depths at Maragheh meteorological station in north-western Iran. The results of the models were evaluated under 5 scenarios with various inputs including the main meteorological parameters measured at the station (air temperature, sunshine hours, relative humidity, wind speed and saturation vapour pressure deficit). Daily Ts data recorded from January 1, 2006 to December 30, 2012 and from January 1, 2013 to December 30, 2015 were used for model training and testing, respectively. The results showed that error rates have decreased from 5 to 10 cm soil depth (root mean square error (RMSE) reduced by 2.97, 4.68 and 3.19% for the best scenarios of SVR, SVR-FA and SVR-KHA models, respectively), whereas error rates have been increasing from 10 to 100 cm soil depths (RMSE increased by 62.4, 80.9 and 73.6% for the best scenarios of SVR, SVR-FA and SVR-KHA models, respectively). For the best scenarios of ENN, ENN-FA and ENN-KHA models, RMSE values decreased by 2.1, 1.6 and 3.1% from 5 to 10 cm depth and increased by 61.1, 84.1 and 81.1% from 10 to 100 cm depth, so that all six models reached their best performance at 10 cm soil depth. Examination of the results in terms of under-estimation or over-estimation of Ts indicated that the lowest and highest differences in performance between under- and over-estimation sets were 0.01 °C (SVR-FA at 5 cm depth) and 1.64 °C (SVR at 100 cm depth) for SVR-based models and 0 °C (ENN at 10 cm depth) and 0.56 °C (ENN at 100 cm depth) for ELM-based models, respectively. According to the results from the best scenarios of SVR, SVR-FA and SVR-KHA models in the under-estimation set at 100 cm depth, all the three models have exhibited a poorer performance over the temperature range 15–25 °C (RMSE increased by 56.7, 47 and 61.3% for SVR, SVR-FA and SVR-KHA, respectively) compared to temperature values outside that range. Exactly the same trend was also observed for ELM-based models, where the mentioned increases in RMSE were about 37.7, 59.4 and 55.5% for ELM, ELM-FA and ELM-KHA, respectively. According to the results, bio-inspired metaheuristic optimisation algorithms based on SVR and ENN which use appropriate meteorological parameters as inputs can have a relatively satisfactory performance in estimating Ts under climatic conditions similar to our study area, especially in lower depths, and can be used as an alternative to direct measurement of this important parameter. • Hybrid models improved Ts estimation compared to base models. • Especial improvement for Ts values <5 °C (5, 10 and 20 cm) and 15–25 °C (100 cm) • Bio-inspired metaheuristic optimisation algorithms reliably estimated Ts. • Estimation accuracy was reduced by soil depth. [ABSTRACT FROM AUTHOR]