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It is crucial to optimize energy consumption in buildings while considering thermal comfort. The first step here involved an EnergyPlus simulation on a trade center building located in Tehran, Bandar Abbas, and Tabriz, Iran. A multi-objective optimization was then performed based on non-dominated sorting genetic algorithm II (NSGA-II) in jEPlus + EA to establish the building in the selected city where would benefit the most from implementing the radiant ceiling cooling system. Efforts were undertaken to choose environmentally-friendly materials. The final solution by Pareto charts resulted in a 52% reduction in energy consumption, a 37.3% decrease in cooling load, and a 17.4% improvement in comfort hours compared to the original design. Annual emission of greenhouse gas reduced as 167.67 tone of CO2 equivalent emission, 25.77 ton of CH4, and 0.2 ton of NO2. The mentioned algorithm was conducted for the first time on a trade center, including a DOAS system and radiant ceiling cooling system. Simultaneously, the environmental-friendly materials were dealt with. The procedure holds significant relevance for the design and optimization of buildings in Iran, especially wherever the climate is hot and humid. This approach offers advantages to the environment by reducing the impact on energy resources and utilizing environmentally-friendly materials. [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]

Increasing the concentration of nitrates in aquifer systems reduces water quality and causes serious diseases and complications for human health. Therefore, it is important to monitor nitrate levels in groundwater resources and identify contaminated aquifers. In this research, multiple artificial neural network (ANN) structures and a genetic algorithm (GA) were combined to predict groundwater nitrate levels using its affecting factors in Mazandaran plain (north of Iran). Five ANN algorithms were trained, and their performances were evaluated during the training, cross-validating, and testing stages. Then, GA was combined with the ANNs, and the process of training, cross-validation, and testing was repeated with the same data. The results showed the factors of distance from industrial centers (R = − 0.57), population density (R = 0.56), and groundwater depth (R = − 0.15) are the most important factors in groundwater nitrate contamination. Further, a modular neural network (MNN) model showed the highest performance among the networks used in nitrate concentration modeling. Additionally, combining ANNs with GA enhanced the performance of the models in predicting nitrate concentration. The MSE and R-srqr of the MNN model in the test stage were estimated to be 0.2 and 0.7, respectively. After combining with GA, these values were improved to 0.1 and 0.8, respectively. Finally, the zoning map of nitrate pollution in groundwater was prepared using the ANN-GA hybrid model in the GIS environment. The cost-effective methodology presented in this study can be used to predict the spatial and temporal changes of nitrates in the study area and other areas with similar settings. [ABSTRACT FROM AUTHOR]

This research tries to find the best operation strategies for a reservoir system with the Flow Direction Algorithm (FDA), which was recently introduced. This study evaluates the implementation of the FDA, for the first time, for optimizing the hydropower operation of the Karun-4 reservoir in Iran for 106 months (from October 2010 to July 2019) and for the multi-reservoir systems for 12 months. Multi-Reservoir systems which are hypothetical 4 and 10-reservoir systems are studied to demonstrate the effectiveness and robustness of the algorithms. The results are compared to those of the three most commonly used evolutionary algorithms, namely the Particle Swarm Optimization Algorithm (PSO), the Weed Algorithm (WA), and the Genetic Algorithm (GA). The multi-reservoir results indicated that the absolute optimal solution was 308.292 in the four-reservoir benchmark system (FRBS) and 1194.441 in the ten-reservoir benchmark system (TRBS), and according to these results, FDA outperformed three other algorithms. In the Karun-4 reservoir, the best approach was chosen with the analytical hierarchy process (AHP) method, and according to the results, the FDA outperformed PSO, WA, and GA. The reliability percentage for FDA, PSO, WA, and GA was 95%, 86%, 78%, and 64%, respectively. The average optimal objective function value generated by FDA was 0.138, compared with PSO, WA, and GA, with the values of 0.322, 0.631, and 1.112, respectively, being better. The hydropower produced by FDA was more than three other algorithms in less time, with the lowest coefficient of variation value, which demonstrates the power of the FDA. [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]

Linseed (Linum usitatissimum L.), a globally cultivated oilseed crop in high demand, is the focal point of our efforts aimed at improving yield production. The achievement of robust yield outcomes relies on the intricate interplay of various agronomic traits. This study, conducted over two years at a research farm in Iran, presents a comprehensive analysis evaluating diverse agronomic characteristics inherent to different linseed cultivars and hybrids. Essential parameters, including days to emergence, days to flowering, plant height, number of branches, number of capsules per plant, number of seeds per capsule, 1000-seed weight, and seed yield per plant, were examined. For predictive insights into seed yield, machine learning techniques, specifically multilayer perceptron (MLP) and multiple linear regression (MLR), were employed. The analysis of contribution percentages for each agronomic variable to linseed seed yield revealed that the number of capsules per plant emerged as the most influential factor, contributing 30.7% among the considered variables. The results indicated the superiority of MLP over MLR, with RMSE and MAPE values equaling 0.062 g/plant and 3.585%, respectively. Additionally, R2 values for training, validation, and test phases exceeded 0.97. Consequently, MLP served as a merit function in the genetic algorithm (GA), targeting the identification of optimal trait levels to maximize linseed yield. The optimization outcomes demonstrated the potential achievement of a yield of 4.40 g/plant. To attain this performance, a set of agronomic characteristic values was proposed by GA, initiating a discussion on genetic modification possibilities. The findings of this study highlight the remarkable efficacy of machine learning tools, particularly neural networks, when paired with evolutionary optimization techniques such as genetic algorithms. These methodologies prove to be invaluable assets in aiding biotechnologists as they strive to enhance the genetic makeup of products for various applications, providing unwavering reliability and invaluable guidance in the pursuit of genetic modification endeavors. [ABSTRACT FROM AUTHOR]

Water pollution escalates with rising waste discharge in river systems, as the rivers' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA's Pareto front is superior to NSGA-II, which highlights the MOCOA's effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size. [ABSTRACT FROM AUTHOR]

Urban drainage systems (UDSs) may experience failure encountering uncertain future conditions. These uncertainties arise from internal and external threats such as sedimentation, blockage, and climate change. In this paper, a new resilience-based framework is proposed to assess the robustness of urban flood management strategies under some distinct future scenarios. The robustness values of flood management strategies are evaluated by considering reliability, resiliency, and socio-ecological resilience criteria. The socio-ecologic resilience criteria are proposed considering the seven principles of building resilience proposed by Biggs et al. (2012). The evidential reasoning (ER) approach and the regret theory are utilized to calculate the total robustness of the flood management strategies. In this framework, the non-dominated sorting genetic algorithms III (NSGA-III) optimization model and the storm water management model (SWMM) simulation model are linked and run to quantify the criteria. The novelty of this paper lies in presenting a new framework to increase the sustainability and resilience of cities against floods considering the deep uncertainties in the main economic, social, and hydrological factors. This methodology provides policies for redesigning and sustainable operation of urban infrastructures to deal with floods. To evaluate the applicability and efficiency of the framework, it is applied to the East drainage catchment of the Tehran metropolitan area in Iran. The results show that real-time operation of existing flood detention reservoirs, along with implementing five new relief tunnels with a construction cost of 37.1 million dollars, is the most robust non-dominated strategy for flood management in the study area. Comparing the results of the proposed framework with those of a traditional framework shows that it can increase the robustness value by about 40% with the same implementation cost. [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]

The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young's Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson's ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young's modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson's ratio. The root mean square error (RMSE) and determination coefficient (R2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs. [ABSTRACT FROM AUTHOR]

Formation damage poses a widespread challenge in the oil and gas industry, leading to diminished permeability, flow rates, and overall well productivity. Acidizing is a commonly employed technique aimed at mitigating damage and enhancing permeability. In this study, to predict the permeability after acidizing in oil and gas reservoirs, three machine learning models, namely artificial neural networks, random forest, and XGBoost, along with genetic programming were used to estimate permeability changes after acidizing. These models are utilized to estimate permeability changes following acidizing operations. Training of the models involved a dataset comprising 218 acidizing operations conducted in diverse reservoirs across Iran. The input parameters, namely permeability, porosity, skin factor, calcite mineral fraction, acid injection rate, and injected acid volume, were optimized through the use of a genetic algorithm. Statistical and graphical analysis of the results demonstrates that genetic programming outperformed the other machine learning techniques, yielding superior performance with R square and RMSE values of 0.82 and 17.65, respectively. Nevertheless, the other models also exhibited commendable performance, surpassing an R square value of 0.73. The post-acidizing permeability data obtained from core flooding experiments conducted on carbonate and sandstone cores was utilized to validate the models. The genetic programming model demonstrates an average error of 21.1%. The evaluation of post-acidizing permeability using genetic programming, in comparison with the results obtained from the core-flood test, revealed errors of 22.95% and 32.4% for carbonate and sandstone cores, respectively. Furthermore, a comparison between the calculated post-acidizing permeability derived from the GP model and previous studies indicated errors within the range of 8.6–26.59%. The findings highlight the potential of genetic programming and machine learning algorithms in accurately predicting post-acidizing permeability, thereby aiding in acidizing design, effectiveness assessment, and ultimately enhancing oil and gas production rates. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is to formulate the problem of optimal design of renewable wind/solar/biomass hybrid system for grid-independent applications in a region of Iran and to compare the genetic algorithm (GA) and performance of particle swarm optimization (PSO) on this optimization problem. There are many types of research on solar and wind hybrid energy systems, but research on solar/wind/biomass hybrid energy systems is rare. The biomass energy system can be used as a support and complementary system along with wind and solar energy systems. This paper studies the optimum design of a biomass/PV/wind energy system for independent applications. The objective of the optimum design problem is to minimize the total net present cost (TNPC) of the PV/wind/biomass system during its lifetime subject to some constraints by adjusting three decision variables, namely the swept area of wind turbines, the area of PV panels and the capacity of biogas generators. For this aim, two efficient metaheuristic techniques of GA and PSO are used to solve the optimization problem. Simulation results show that PV/biomass system is the most cost-effective one for supplying the demanded load. Moreover, PSO leads to better results than GA. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for the optimal redesign of water quality monitoring networks in coastal aquifers. The GALDIT index is used to evaluate the extent and magnitude of seawater intrusion (SWI) in coastal aquifers. The weights of the GALDIT parameters are optimized using the genetic algorithm (GA). A SEAWAT-based simulation model, a spatiotemporal Kriging interpolation technique, and an artificial neural network surrogate model are then implemented to simulate total dissolved solids (TDS) concentration in coastal aquifers. To obtain more precise estimations, an ensemble meta-model is developed using the Dempster-Shafer's belief function theory (D-ST) to combine the results obtained from the three individual simulation models. The combined meta-model is then used for calculating more precise TDS concentration. Some plausible scenarios are defined for variation of water elevation and water salinity at the coastline to incorporate uncertainty through the concept of value of information (VOI). Finally, the potential wells with the highest values of information are taken into consideration to redesign coastal groundwater quality monitoring network under uncertainty. The performance of the proposed methodology is evaluated by applying it to the Qom-Kahak aquifer, north-central Iran, which is threatened by SWI. At first, the individual and ensemble simulation models are developed and validated. Then, several scenarios are defined regarding the plausible changes in TDS concentration and water level at the coastline. In the next step, the scenarios, the GALDIT-GA vulnerability map, and the VOI concept are used for redesigning the existing monitoring network. The results illustrate that the revised groundwater quality monitoring network containing 10 new sampling locations outperforms the existing one based on the VOI criterion. [ABSTRACT FROM AUTHOR]

The aim of this paper was to develop a binary bi-level optimization model for the emergency warehouse location-allocation problem in terms of national and regional levels. This type of modeling is suitable for countries where the design of the disaster emergency network is decentralized. The upper-level decision-maker makes a decision regarding the location and allocation of national warehouses through considering the location of regional warehouses and allocating them to the demand cities. Each regional warehouse can provide a service for the demand cities within a specified distance threshold, ultimately affecting the efficiency of the solution algorithms. The optimization model parameters were calculated in terms of the real data in Iran. To solve the small size problem, an exact method was proposed from the explicit complete enumeration. Due to the complexity of the model with the large size, two innovative hybrid genetic algorithms, namely HG-ES-1 and HG-ES-2, were suggested. The results obtained from solving the problems showed that the HG-ES-1 algorithm outperformed HG-ES-2. The findings further indicated the proper functioning of the solution approaches. [ABSTRACT FROM AUTHOR]

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information. [ABSTRACT FROM AUTHOR]

In this paper, the numerical modeling of an indirect-expansion solar-assisted heat pump (IDX-SAHP) water heater system is proposed and the exergy-energy analyses and multiple-objective optimization (MOO) of the developed system are investigated. After the verification of the IDX-SAHP with the experimental results, the system performance is examined under the temperate climate of Iran throughout the year. Next, the sensitivity analysis (SA) of diverse variables comprising ambient temperature (T a) , solar intensity (I T) , water temperature at the condenser outlet (T W , o ) , compressor speed ( ω) , collector area (A cl) , thermal conductivity of the collector plate (k p) , number of the glass cover ( N gl) , tube diameter in the solar collector (D cl) , tube length in the condenser (L cond) , and absorber thickness of the collector plate (δ) is implemented via the one-parameter-at-a-time (OPAT) technique. Then, the influence of various factors such as T a , I T , A cl and ω on the exergy efficiency and exergy destruction of the IDX-SAHP system are studied. Besides, the single-objective optimizations (SOOs) and MOO processes are implemented through Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) to maximize the coefficient of performance (COP) and the collector efficiency (CE), individually and simultaneously. The results achieved by the SOOs depict that the design of IDX-SAHP as per the COP-based SOO offers a better system compared to the CE-based SOO. Furthermore, the optimal solutions obtained by the MOO are presented in the form of the Prato curve to depict the COP and CE interactions. Afterward, to achieve the final optimum layout of the system, the Analytic Hierarchy Process (AHP) technique as a robust multi-criteria decision analysis method (MCDM) is integrated with the MOO process through MATLAB programming language. The optimization results demonstrate that the MOO process gives better performance compared to the SOO process in such a way that although the CE of the maximized IDX-SAHP reduces a little from 52 to 47 % , its COP is enhanced greatly from 3 to 7 , resulting in reduction of the total working hour of the system up to 282 hours compared to the initial system, and consequently, the overall power use of the maximized system largely reduces. This investigation clarifies the importance of exergy-energy analyses, SA, MOO, and MCDM during the IDX-SAHP design to increase efficiency and decrease the power use of the system by opting the most appropriate design parameters of the system. [ABSTRACT FROM AUTHOR]

Excessive exploitation of groundwater has hitherto led to a significant land subsidence in a considerable number of plains in Iran. The compaction of aquifer layers ends up with changes in aquifer parameters, including hydraulic conductivity (Kx), specific yield (Sy), and compressibility (α). Accordingly, a precise estimation of aquifer parameters, Kx, Sy, and α seems essential for future water resources planning and management. In this study, an innovative inversion solution based on the combination of lattice Boltzmann method (LBM) and genetic algorithm (GA) was developed to determine the aquifer parameters, Kx, Sy, and α in Darab plain (in Fars province, Iran), which is highly subject to land subsidence. Herein, a newly developed LBM for unconfined groundwater flow was employed by incorporating the amount of subsidence measured by synthetic aperture radar interferometry (InSAR) spanning from 2010 to 2016. In order to optimize the aquifer parameters, the whole process of inverse modeling is replicated on the annual basis from 2010 to 2016 which leads to the temporal estimation of the aquifer parameters. Due to the compaction occurring in the aquifer system, a declining temporal trend is observed in the aquifer parameters in most parts of the plain. By fitting a function to time-dependent aquifer parameters, Kx, Sy, and α, their corresponding values and consequently the amount of subsidence in the near future, i.e., 2017, are predicted. The small average relative error (~ 3.5%) between the predicted land subsidence and the InSAR measurements demonstrates the high performance of the proposed inverse modeling approach. [ABSTRACT FROM AUTHOR]

This paper evaluates a comparison between three machine learning algorithms (MLAs), namely support vector machine (SVM), multilayer perceptron artificial neural network (MLP-ANN) and random forest (RF), in landslide susceptibility mapping and addresses a optimization algorithm to optimize the performance of a MLA to yield more accurate and reliable results. A genetic algorithm (GA) approach as a part of evolutionary algorithms was utilized in order to optimize the performance of best model among three utilized MLAs. The study area (Tarom–Khalkhal sub-basin) is located in North-West of Iran with mountainous nature (western part of Alborz Mountains), wherein numerous landslide occurrences were recorded. In this case, fifteen predisposing factors, gathered from aerial images and field surveys, were considered to generate the final landslide susceptibility models. The validation procedure was conducted with taking advantage of confusion matrices for different algorithms. Finally, landslide susceptibility maps were generated and evaluated through receiver operator characteristic (ROC) curves. RF algorithm showed the best performance; therefore, hybridized genetic random forest (GRF) was employed in order to optimize the hyperparameters (number of trees, number splits and depth) of the model, which can affect the performance of model. As a result, GRF has best performance among all mentioned algorithms with AUC = 0.93. As a conclusion, genetic algorithm was found to be suitable in optimizing the performance of machine learning algorithms, which is crucial when it comes to landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Despite the high level of interpretability of the Classification and Regression Tree (CART) as one of the widely-used data mining models, its results usually are less accurate than other models. To address the low accuracy of the CART model, this paper developed two new and enhanced CART-based algorithms that can provide both high interpretability and high accuracy for flood susceptibility mapping. In the developed models, various hyperparameters of the CART were explicitly tuned using the genetic algorithm (GA) and grid search (GS) methods. Eleven variables and 222 flood locations were used in Sardabroud watershed in Iran for modelling. The results showed area under the receiver operating characteristic curve (AUROC) values of 0.884, 0.927 and 0.908, respectively, for the CART, CART-GS and CART-GA models in the validation run. The results showed that the developed models can produce the output that satisfies both accuracy and interpretability criteria. [ABSTRACT FROM AUTHOR]

Summary: Greenhouse gas emissions and increasing energy consumption motivate researchers toward using waste heat recovery. In the present study, a novel combined cooling and power (CCP) generation system driven by waste heat recovery of a steel factory in northeastern Iran is presented which uses an ammonia‐water mixture as the working fluid and consists of a vapor generator, a superheater, a turbine, an expansion valve, a mixer, a compressor, an evaporator, two recuperators, two condensers, and two pumps. In the presented system, a non‐isothermal phase change occurs in the vapor generator while an isothermal phase change happens in condensers. To evaluation of the system performance, comprehensive thermodynamic and thermoeconomic analyses are performed. The effects of generator pressure and ammonia concentration on the performance of the cycle are investigated. To satisfy different needs of users, the cycle is optimized by a genetic algorithm for different ratios of power to cooling capacity considering single and multi‐objectives and the best values of generator pressure and ammonia concentration are presented for each power to cooling ratio. The results show that based on the multi‐objective function, for heat source temperature of 573K the best conditions happen at power to cooling capacity ratio of 0.6, in which the optimum generator pressure and ammonia concentration are 43.87 bar and 79.49%, respectively. [ABSTRACT FROM AUTHOR]

Hazardous material (Hazmat) transportation is an inseparable section of the industry, despite its major financial and health risks. In order to optimize Hazmat transportation, a multi-objective Hazmat routing model with time windows is employed where the risk and distance are minimized. Due to the uncertainty of Hazmat transportation risk, a Z-number fuzzy approach is used to estimate the risk, in which the probability of occurrence and the severity is considered in the context of Z-information. The severity of the event includes the affected population and depends on the amount of transported Hazmat and the number of individuals affected by the explosion. To tackle the proposed model, the present paper utilizes a multi-objective hybrid genetic algorithm, the validity of which is tested by Solomon's problems. Furthermore, the optimization of a case study concerning the Hazmat distribution in Iran is analyzed using the suggested approach to assess the efficiency of the proposed fuzzy problem in real-world applications. [ABSTRACT FROM AUTHOR]

Background: This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques.Methods: The data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to increase the performance of the models, mutual information classification (MIC-FS), genetic algorithm (GA-FS), and random forest (RF-FS) were used to select the ideal feature sets for model development.Results: In this study, 28% of patients undergoing IUI treatment obtained a successful pregnancy. Also, the average age of women and men was 24.98 and 29.85 years, respectively. The calibration plot in this study for IUI success prediction by machine learning models showed that between feature selection methods, the RF-FS, and among the datasets used to fit the models, the balanced dataset with the Stomek method had well-calibrating predictions than other methods. Finally, the brier scores for the LR, SVC, RF, XGBoost, and Stack models that were fitted utilizing the Stomek dataset and the chosen feature set using the Random Forest technique obtained equal to 0.202, 0.183, 0.158, 0.129, and 0.134, respectively. It showed duration of infertility, male and female age, sperm concentration, and sperm motility grading score as the most predictable factors in IUI success.Conclusion: The results of this study with the XGBoost prediction model can be used to foretell the individual success of IUI for each couple before initiating therapy. [ABSTRACT FROM AUTHOR]

Medical data mining is considered as a new solution to the analysis of medical data and further expansion of the relative knowledge. Medical data mining has a high potential to discover the hidden patterns. Recently, some studies have been conducted on the relationship between the individuals' environmental surroundings and their exposure to diseases, thus clearly confirming the considerable impact of the quality of environmental indicators such as environmental pollutants on diseases. In this respect, the current study investigated the effect of the environmental conditions on the chance of diabetes based on medical data mining technique. Given that diabetes is a global threat to the human health, an Ensemble Classifier based on Genetic Algorithm (ECGA) method was designed to study the environmental conditions in diabetes. Decision tree, random forest, K-nearest neighbor, and naive were included in the designed ensemble classifier. It was found that ECGA was more accurate than the base classifier algorithms. For study purposes, three datasets were collected from different regions in Iran with different climatic conditions and it was found that environmental conditions had a significant impact on diabetes. [ABSTRACT FROM AUTHOR]

Technical management of agricultural units plays an important role in increasing the yield, energy efficiency, and decreasing the production costs. Based on that, the present study aimed to evaluate and optimize the technical and economic efficiency in Saffron farms in the 2019–20 cropping season in Iran. Required data were collected from 70 Saffron farms through interviews and questionnaires and were analyzed and compared using two optimization methods including data envelopment analysis (DEA), and multi‐objective genetic algorithm (MOGA). Based on the results related to the energy section, the total energy input was obtained as 43,578 MJ ha−1 before any optimization, while it was determined as 36,033 and 36,910 MJ ha−1 after optimization using DEA, and MOGA, respectively. Also DEA and MOGA methods improved the energy ratio index (ER) (0.002) by 50, and 159%, respectively. Results related to the economic section showed that the total production costs were mitigated from 1260 $ ha−1 to 863.5 and 1069 $ ha−1 after optimization by DEA, and MOGA, respectively. After optimization of revenue (using MOGA method), and total costs (using MOGA, and DEA), the benefit cost ratio index was improved from 1.43 to 2.09 (using DEA), and 3.3 (using MOGA). Consequently, MOGA optimization method showed better results compared to DEA in both energy and economic sections. [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]

Flood mitigation measures are aimed at reducing adverse flooding impacts. Flood source control schemes mainly concentrate on biological soil and water conservation measures (SWCMs) including the revival and preservation of soil and land use. Such activities prevent splash erosion, reduce the velocity of surface runoff, increase surface roughness that in turn improve infiltration and control soil erosion. Nevertheless, based on technical and budget constraints, uniform implementation of biological SCWMs is neither practical nor optimal. Previous studies on spatial optimization of BMPs (best management practices) have mostly focused on urban structural and/or biological BMPs. Besides, the incorporation of land use/cover and slope maps in source control site selection has not been thoroughly studied. In this research, cover–slope zones are proposed as spatial units for spatial optimization of biological SCWMs. The objective of this study was to determine the optimal spatial distribution of SWCMs using coupled simulation–optimization models. A fully distributed event-based deterministic hydrologic rainfall–runoff model was developed that involved SCS-CN (soil conservation service curve number), ModClark and Muskingum as infiltration, rainfall–runoff and stream routing model, respectively. Genetic algorithm was adopted to search the optimized areas based on two objective functions: minimization of the outlet peak discharge and the cost. Nardin sub-catchment in northern Iran was selected as the case study. For different degrees of flood peak reduction under 100-year design rainfall, the optimal patterns identified by the optimization algorithm were a combination of less effective/costly options (exclusion) and the most effective option (transplant). Accordingly, biological SCWMs were assigned from the farthest zone to the outlet, stretching into downstream zones. [ABSTRACT FROM AUTHOR]

This study provides a framework for optimizing ecosystem service zones based on landscape patterns. Ecosystem service zoning is a tool for making more informed land-use decisions and integrating ecosystem services into land development strategies. Accordingly, a meta-heuristic algorithm (simulated annealing-genetic hybrid) was applied to optimize zones of three ecosystem services, including aesthetic value, recreation value, and noise pollution reduction service. The goal was to select ecosystem service zones in terms of area, percentage of landscape, shape, and number of patches in two land-use types, including the built-up and green cover. The results indicated that optimum zones based on green infrastructure comprised more extent and average suitability than human infrastructure. The western and southern parts of the target area showed the highest values of ecosystem services for the two land-use classes. The convergence curve of the objective functions indicated the good performance of the employed algorithm in optimizing ecosystem service zones. Overall, the results indicated that optimizing ecosystem service zones based on landscape patterns improved the land-use management framework in the study area. Moreover, the used algorithm allowed for integrating different parameters into ecosystem services zone configurations. [ABSTRACT FROM AUTHOR]

Surface runoff is one of the most significant components of the water cycle, which increases soil erosion and sediment transportation in rivers and decreases the water quality of rivers. Therefore, accurate prediction of hydrological response of watersheds is one of the important steps in regional planning and management plans. In this regard, the rainfall-runoff modeling helps hydrological researchers, especially in water engineering sciences. The present study was conducted to analyze the rainfall-runoff simulation in the Gorganrood watershed located in northeastern Iran using AWBM, Sacramento, SimHyd, SMAR, and Tank models. Daily rainfall, daily evapotranspiration, and daily runoff of seven hydrometric stations in the period of 1970-2010 and 2011-2015 were used for calibration and validation, respectively. The automated calibration process was performed using genetic evolutionary search algorithms and SCEUA methods, using Nash Sutcliffe Efficiency (NSE) and root mean of square error (RMSE) evaluation criteria. The results indicated that the SimHyd model with NSE of 0.66, TANK model using Genetic Algorithm and SCE-UA methods with NSE of 0.67 and 0.66, and Sacramento model using genetic algorithm and SCE-UA methods with NSE of 0.52 and 0.55 have the best performance in the validation period. [ABSTRACT FROM AUTHOR]

This study evaluates the techno‐economic feasibility of a new biomass‐driven cogeneration system designed to fulfill all energy requirements (i.e., electricity, hot water, cooling, and heating) of a grid‐connected building throughout the year. The proposed system is also expected to achieve the goal of zero energy building (ZEB). A 10‐story residential complex is considered as the present case study. The study is carried out for three cities of Iran with different climate conditions (mild, hot, and cold) and with the annual energy requirements of about 104,472 kWh, 128,784 kWh, and 142,518 kWh, respectively. After defining two objective functions ("annual actual benefit" and "loss of building requirements supply probability"), the system is optimized by using the Non‐dominated Sorting Genetic Algorithm (NSGA‐II). The results show that the designed system is economically viable with the levelized cost of energy (LCOE) of about 0.15–0.2 $/kWh. The results also indicate that if the cost of CO2 emission is taken into consideration, the annual benefit of the system is decreased by 16%–25%. Finally, a sensitivity analysis shows that heat generation cost and electricity selling price have the most and the least effect on the system's annual benefit, respectively. [ABSTRACT FROM AUTHOR]

The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software's capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model's characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models' characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library. [ABSTRACT FROM AUTHOR]

The adverse effects of improper disposal of collected and treated wastewater have become inevitable. To achieve the desired environmental standards, in addition to the construction of wastewater treatment plants, there is also a need to evaluate the continuous performance of treatment systems. In Iran, treated wastewater is mostly used in agriculture. Therefore, the use of wastewater with poor quality characteristics can endanger health. In this study, the neural network model's efficiency was investigated to predict the performance of the Perkandabad wastewater treatment plant in Mashhad in Iran. To achieve this, first, the factors affecting the TBOD parameter were identified as one of the quality indicators of the effluent. In the next step, using a genetic algorithm and network input factors, the performance of the treatment plant was predicted and evaluated. The highest correlation coefficient for the TBOD parameter was 0.89%. The results show that among the input parameters in the model, the amount of organic matter pollution load has the greatest effect on this prediction. [ABSTRACT FROM AUTHOR]

H 2 S is a byproduct in anaerobic digesters (ADs), causing significant challenges due to its negative impact on biogas quality and equipment corrosion. As a solution, previous researchers developed complex mechanistic models that are computationally expensive due to solving equations with many variables. These models were only tested within normal H 2 S ranges. This research introduces a novel hybrid fuzzy-decision tree (F-DT) model to predict H 2 S production from primary/secondary sludge ratio (P/S), dry solids (DS), and volatile solid (VS) reduction parameters in the Qom wastewater treatment plant in Iran. Notably, the H 2 S concentrations in this facility consistently surpass the acceptable thresholds reaching levels as high as 6500 ppm, in contrast to the designed accepted limit of 3000 ppm. The process involved collecting and pre-processing data, creating a decision tree classifier for rule generation, developing a fuzzy model suited for complex systems like AD, and optimizing it with a genetic algorithm. The analysis of the final model revealed that the P/S ratio significantly influences elevated H 2 S levels, indicating that maintaining a P/S ratio > 1.2 holds promise as an effective strategy. The model is both simple and interpretable, with decent accuracy in predicting H 2 S compared to previous models. This study not only introduces a useful method for optimizing industrial-scale ADs, but also aims to uncover the reasons behind high H 2 S levels. [Display omitted] • F-DT is a hybrid fuzzy and AI-based model to address high uncertainty of AD. • DS is the least influential factor on H 2 S production. • Low primary to secondary sludge (P/S) is critical for high H 2 S production in AD. • Increasing P/S ratio >1.2 is crucial to control H 2 S and optimal biogas production. [ABSTRACT FROM AUTHOR]

One strategy to deal with unauthorized groundwater withdrawals by farmers is to impose penalties. Because farmers' over-exploitation is subject to social, economic, cultural, and agricultural characteristics of the region, feedback assessments of a penalty policy using socio-economic simulation is indispensable. This study presents an agent-based framework to study the interaction among agricultural, environmental, and regulator agents within an agricultural system. The behavior of the agricultural sector was simulated at two levels: agricultural sub-agents, and agricultural group-agents. For agricultural sub-agents, individual benefit was maximized under behavioral and physical constraints simulated by a fuzzy inference system (FIS) and mathematical programing. A coupled linear optimization and nondominated sorting genetic algorithm (NSGA-II) were adopted to identify the best solution, from the agricultural sector's viewpoint. The proposed framework was implemented in the Najaf Abad hydrological unit, situated in Isfahan province, central Iran, considering fixed and stepwise penalty approaches. Sensitivity analysis of the amount of base penalty in the fixed penalty scenario indicated that, under a $0.12 per cubic meter penalty, the annual over-exploitation declined from the current 79 million cubic meters (MCM) to zero. Furthermore, the results indicated that if the width of the step increased from 1 to 9 MCM/year , the over-exploitation increased by about 18 MCM/year. [ABSTRACT FROM AUTHOR]

Directional Overcurrent Relays (DOCRs) are essential protective devices in distribution networks which are usually set without considering any contingencies. However, the current challenge in power systems is the existence of uncertainty and its unfavorable consequences. It sometimes appears that some elements simultaneously fail which makes other parts overloaded to the extent that it leads to cascading outages. Therefore, DOCRs may malfunction, which ends in unwanted trips when there is no fault, or they may not operate in case the fault is located within their reach. In such cases, the coordination setting needs complex programming with many related non-linear inequality constraints. In this paper, a novel hybrid method is proposed based on multi-objective optimization including new objective functions using Genetic Algorithm (GA). Also, the cascading outages are considered in the presented method based on network data analysis. This approach is performed on distribution part of the IEEE 14-bus meshed system and a real industrial radial feeder named TOSEE, located in Iran. The simulations were implemented in MATLAB and PowerFactory-DIgSILENT software packages in different models and the results are evaluated. [ABSTRACT FROM AUTHOR]

The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions. [ABSTRACT FROM AUTHOR]

In recent years, the limited energy sources and their huge costs in many countries to save money have attracted the attention of many scholars. The purpose of the energy consumption assessment of a building is to reduce the waste of the energy, and this reduction in the energy has a direct relationship to the lower costs. In this paper, the researcher tends to investigate the sensitivity and the energy analysis of a warm and dry residential complex in Iran (Yazd). Initially, the simulation of the energy consumption of a building was calculated by using the eQUEST software, to determine the thermal load of the building for a range of important parameters (such as wall insulation, roof and floor insulation, air penetration, glass type and window size). Then by using the SPSS software, the relationships have been extracted to connect the energy of the building with these factors. Then, by using the SPSS software, it attempted to connect a meaningful relationship between the energy consumption of the building and the mentioned factors. Also, the energy efficiency of the building was optimized by converting the data to the neural network, by using the genetic algorithm. Finally, this study was also carried out in warm and dry areas, to describe the accuracy of the results, and in each step, appropriate validations have been done. [ABSTRACT FROM AUTHOR]

Summary: The purpose of this paper is to present a model for optimal placement of distributed generation units and sectionalizing switches (relocation of the existing switches and installation of new ones) to increase distribution network resilience against the threat of transmission system outage. The proposed model is formulated as a multi‐objective optimization problem with the objectives of minimizing investment costs and maximizing load restoration. In this regard, distribution system operation during normal conditions as well as transmission system outages is considered. In addition, the unique characteristics of the real power grid in Khuzestan province in southwestern of Iran are addressed in the proposed model which makes the model applicable to the real networks with similar characteristics in many parts of the Middle East. This grid has two completely different load patterns (in terms of load curve shape and peak load) corresponding to two hot and temperate periods, which causes completely different concerns for the network's operator. The proposed model is solved using multi‐objective genetic algorithm (NSGA‐II), and it is implemented on the 33‐node test system and the results are analyzed. Moreover, it is applied to the 327‐node real‐world test system in Khuzestan province to demonstrate its applicability to large networks. In both cases, the results confirm the effectiveness of the proposed optimization model. [ABSTRACT FROM AUTHOR]

Copper is an essential material for electrical conductivity and is a good conductor for heat. The porphyry copper deposits (PCD) are one of the most important resources of copper, where the determination of copper grade is one of the most important issues. The finding complex relationship between copper grade and kind of rocks is a major change for modelers. This study employed the adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) to estimate the copper grade in PCDs. The Henry gas solubility optimization (HGSO), weed algorithm (WA), and moth flame optimization (MFO) were applied to set the parameters of the MLP and ANFIS models. The Iju PCD, as one of the important copper deposits in the Kerman province of Iran, was chosen as a case study for predicting the copper grade. Three scenarios were used as input to the models. The first scenario used the latitude and altitude of boreholes as input and the second scenario used the longitude and altitude of boreholes as input. The third scenario used the latitude, longitude, and altitude of boreholes as input. Results of the first scenario indicated that the percent bias of the ANFIS model was 0.26, while it was 0.19, 0.22, and 0.24 for the ANFIS-HGSO, ANFIS-MFO, and ANFIS-WA models. The accuracy of models indicated that the integration of ANFIS and HGSO decreased the root mean square error (RMSE)of the ANFIS-MFO, ANFIS-WA, and ANFIS models about 14%, 21%, and 27%, respectively, in the training phase in the second scenario. The RMSE for the ANFIS-HGSO was 1.98 in the training phase, while it was 2.31, 2.45, and 2.67 for the ANFIS-MFO, ANFIS-WA, and ANFIS models, respectively, in the third scenario. The accuracy of three input scenarios was compared with that of ANFIS-HSGO. The Mean absolute error of ANFIS-HSGO for the third input scenario was 67% and 40% less than for the first and second input scenarios in the testing phase. The third scenario was the best input scenario. Uncertainty analysis for all the models showed that the least value of uncertainty belonged to the ANFIS-HGSO. This study also used an inclusive multiple model to estimate copper grade based on providing a synergy among multiple models. The utilization of an inclusive multiple model based on the outputs of the hybrid and standalone ANFIS and MLP models could increase the accuracy of individual models. The inclusive multiple model and the comprehensive uncertainty analysis are the innovations of the current study. [ABSTRACT FROM AUTHOR]

Hybrid and integrated techniques can be used to investigate intrinsic uncertainties of the overlay and index groundwater vulnerability assessment methods. The development of a robust groundwater vulnerability assessment framework for precise identification of susceptible zones may contribute to more efficient policies and plans for sustainable managements. To achieve an overall view of the groundwater pollution potential, the DRASTIC framework (Depth to the water table, net Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, and hydraulic Conductivity) can be used for intrinsic vulnerability assessment. However, the unreliability of this index is because of its inherent drawbacks, including the weight and rating assignment subjectivity. To modify the rating range, this study recommended a new DRASTIC modification using a recently introduced Multi-Criteria Decision-Making (MCDM) method, namely the Stepwise Weight Assessment Ratio Analysis (SWARA); in addition, the Entropy and Genetic Algorithm (GA) methods were employed to alter the relative weights of DRASTIC parameters. To improve the DRASTIC index, nitrate concentration data from 50 observation wells in the study site were used. To assess the models' overall performance, the datasets obtained from new observation wells, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were studied. The experiments were carried out in the aquifer of the Qazvin Plain in Iran. The results indicated the higher performance of the modified DRASTIC framework, manifested as an increase in the AUC value from 0.58 for generic DRASTIC to 0.68 for the SWARA-Ent framework and 0.74 for the SWARA-GA framework. The application of the SWARA technique, as an effective MCDM method, resulted in the DRASTIC rating system enhancement. The generic DRASTIC optimization by integrating SWARA and GA provided an effective framework to assess groundwater vulnerability to nitrate contamination in the Qazvin Plain. [ABSTRACT FROM AUTHOR]

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

Bed sills are a kind of grade control structure built at the same level as the river bed to protect it against erosion and to reduce the longitudinal slope of the channel. The aim of the present research was to determine the optimal dimensions of the structure by devising a new methodology to minimize cost and maximize stability, based on the optimization model of the multi-objective non-dominated sorting genetic algorithm (NSGA-II). This is achieved by calculating the width, height and distance between bed sills with these stability and cost objectives in mind. The NSGA-II algorithm is applied to solve the problem, and a set of Pareto points is obtained. The results are verified by considering the bed sills of the River Madarsoo in Iran and the River Plima in Italy, and comparing the results with those obtained using the Branch-and-Bound (B-and-B) algorithm. The results indicate that the cost and stability could respectively be improved by up to 18 and 20% in the River Madarsoo, and up to 18 and 26% in the River Plima, if the NSGA-II algorithm is used. [ABSTRACT FROM AUTHOR]

In this study, new hybrid artificial neural network (ANN) models were used for predicting the groundwater resource index. The salp swarm algorithm (SSA), particle swarm optimization (PSO), and genetic algorithm (GA) were used to find the weight and bias values of the ANN models. The ANN-PSO, ANN-SSA and ANN-GA models were used to predict the groundwater resource index (GRI)-based drought at different timescales (6, 12, and 24 months) in Yazd plain, Iran. Five input scenarios were used for modeling GRI. The best input scenario was a combination of one-month-lagged GRI, two-month-lagged GRI, three-month-lagged GRI, four-month-lagged GRI, and five-month-lagged GRI, which is known as the fifth input scenario. The outputs of models indicated that the ANN-SSA model with input scenario (5) decreased the mean absolute error (MAE) of ANN-PSO (5) and ANN-GA (5) by 43% and 51%, respectively. Among the hybrid ANN models, ANN-SSA (5), ANN-PSO (5) and ANN-GA (5) outperformed the other hybrid ANN models. [ABSTRACT FROM AUTHOR]

This paper presents the construction of prediction intervals (PIs) associated with the Emotional Artificial Neural Network (EANN) via the Lower Upper Bound Estimation method (LUBE) and the classic Bootstrap method for the Suspended Sediment Load (SSL) modeling. As point prediction conveys no information about modeling reliability, PIs were applied. The constructed PIs via the EANN were also compared to those of the classic feed-forward Neural Network (FFNN) model. It was attempted to construct the PIs of the SSL modeling in both daily and monthly scales for two watersheds, Upper Rio Grande River, in the United States and the Lighvanchai River in Iran. The PIs were quantified via coverage and width criteria. In the LUBE method, Genetic Algorithm (GA) constructed the PIs by minimizing the cost function of Combinational coverage Width-based Criterion (CWC). Comparison of the results indicated that the criterion of the CWC for PIs of EANN was up to 72% and 78% lower than that to the PIs of the FFNN, respectively, in the LUBE and Bootstrap methods, which showed the reliability of the EANN. In addition, obtained results via the LUBE and the Bootstrap techniques denoted the lower level of uncertainty of the LUBE method. Comparing the CWC criterion of both methods indicated that for the LUBE method, CWC was up to 39% lower than that for the Bootstrap method. Also, the PIs of the FFNN for the Lighvanchai river modeling showed reliable results with CWC of 56% lower than Upper Rio Grande River, but PIs of the EANN led to lower level of uncertainty in Upper Rio Grande River modeling. [ABSTRACT FROM AUTHOR]

In this research, reliability indicators of water distribution networks are evaluated under pipe failure conditions. The case studies include two benchmark and one real-life water distribution networks in Iran with more hydraulic constraints. Some important reliability indicators are presented such as resilience index, network resilience, modified resilience index and minimum surplus head index. GANetXL is used to do one-objective and two-objective optimization of the previously mentioned water distribution networks in order to not only minimize the cost, but also maximize the reliability indicators. Moreover, the results of a statistical analysis for each pipe is used to determine the sensitive pipes that are of the most failure probability. GANetXL is an optimization tool in Excel environment and works based on Genetic Algorithm. GANetXL has the capability of being linked to EPANET (Hydraulic simulation software). The results obtained clearly show that network resilience index is of poor performance when compared with the other indexes under pipe failure conditions, especially in real-life networks that include small pipe diameters. It was also showed that if a water distribution network was optimized only in terms of cost, there would be an unacceptable pressure drop at some nodes in case of pipe failure. [ABSTRACT FROM AUTHOR]

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R2) values were obtained from ANFIS-GA model. The values of R2 and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields. [ABSTRACT FROM AUTHOR]

Optimal operation of cascade hydropower reservoirs is a complex high-dimensional engineering problem. Developing an appropriate model to solve such problems requires an efficient search method proportional to the dimensions of the problem. Accordingly, this research employed the new fitness-distance-balance (FDB) selection method in the moth swarm algorithm (MSA) to achieve promoted FDB-MSA with a high performance in solving complex large-scale problems. To ensure the efficiency of the developed algorithm, five benchmark functions of Shekel, Six-Hump Camel, McCormick, Goldstein-Price and Rosenbrock were used. Then, the FDB-MSA was used for optimization of hydropower generation of a real five-reservoir system along Karun River at Iran. This is the largest cascade reservoir system in Iran, which supplies more than 90% of the country's hydropower demand. The results of the developed algorithm were compared with those of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. It was found that the FDB-MSA could successfully increase the hydropower generation by 59.5% (6724 GW) compared to the actual generation of energy over a 180-months operational period. The corresponding values for PSO and GA algorithms were 54.3% and 9.2% respectively. In addition, the results revealed the superiority of FDB-MSA to GA and PSO, so that, it demonstrated the smallest difference (3.41%) between nominal and optimal power generation compared to the PSO (6.58%) and GA (33.89%). [ABSTRACT FROM AUTHOR]