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Wind power as a renewable source of energy has numerous economic, environmental, and social benefits. To enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) was used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of the subsequent hybrid model. The capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP optimized by the Genetic Algorithm (MLP-GA) and standalone MLP without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the MLP-GA and standalone MLP model. In other words, the hybrid MLP-WOA models, with acceptable performances, reduced the RMSE values from 0.570∼2.995 to 0523∼2.751. Also, the obtained results indicated that the examined MLP-GA did not have a significant effect in increasing the estimation accuracy of standalone MLP models. It was concluded that the WOA optimization algorithm could improve the prediction accuracy of the MLP model and may be recommended for accurate wind speed prediction.

The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model’s superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity.

Climate change impacts and adaptations are ongoing issues that are attracting the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken into consideration. An adaptive neuro-fuzzy inference system (ANFIS)-based post-processing technique was used to match the power outputs of the regional climate model (RCM) with those obtained from reference data. The near-surface wind data obtained from an RCM were used to investigate climate change impacts on the wind power resources in the Caspian Sea. After converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results were investigated to reveal mean annual power, seasonal and monthly variability for 20 year historical (1981–2000) and future (2081–2100) periods. The results revealed that climate change does not notably affect the wind climate over the study area. However, a small decrease was projected in the future simulation, revealing a slight decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution, with winter and summer having the highest values. The results indicate that the middle and northern parts of the Caspian Sea have the highest values of wind power. However, the results of the post-processing technique using the ANFIS model showed that the real potential of wind power in the area is lower than that projected in the RCM.

The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers.

A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.

This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.

Estimation of wave height is essential for several coastal engineering applications. This study advances a nested grid numerical model and compare its efficiency with three machine learning (ML) methods of artificial neural networks (ANN), extreme learning machines (ELM) and support vector regression (SVR) for wave height modeling. The models are trained by surface wind data. The results demonstrate that all the models generally provide sound predictions. Due to the high level of variability in the bathymetry of the study area, implementation of the nested grid with different Whitecapping coefficient is a suitable approach to improve the efficiency of the numerical models. Performance on the ML models do not differ remarkably even though the ELM model slightly outperforms the other models.

In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (Kx). The proposed framework (ANFIS-FFA), combines the specific structures and strengths of both ANFIS and FFA approaches. The FFA is used to derive the optimum ANFIS parameters. The Kx data set includes 503 cross-sectional data point from small to large rivers. For pre-processing of the data set, the SSMD method is used, which is superior to the classical trial and error method. The database covers a wide range of river width (0.2867 m), and depths (0.03419.9 m). Fifteen different combinations of river width (B), depth (H), flow velocity (U) and shear velocity (U*) are implemented as inputs to create fifteen estimative models. The output of the ANFIS-FFA model is compared with the ANFIS and previously published equations to check the performance of the proposed model. The results show that the highest accuracy is attained by the M1 model, with all geometric and hydrodynamic parameters as input variables in comparison with ANFIS and previous equations. The R2 value, RMSE, MAE and NSE for ANFIS-FFA model are 0.67, 113.14 m2/s, 48 m2/s, and 0.63 for proposed dimensional model, and 0.35, 874.5, 520.8, and 0.1 in non-dimensional ANFIS-FFA model, respectively. These values were 0.37, 463.34 m2/s, 85.69 m2/s, and -5.19 for dimensional ANFIS model, and 0.11, 3269.88, 1932.09 and -11.54 for non-dimensional ANFIS model, respectively. Overall, hybridization caused 81%, 75%, 76% improvement in R2, RMSE and MAE. In another contribution of the paper, by using the matrix form of developed ANFIS-FFA optimized parameters, a novel explicit calculation procedure for estimation of Kx is derived. Based on the results, the proposed ANFIS-FFA model exhibits significant improvements than the classical ANFIS and highlights that optimizing by nature-inspired optimization algorithms plays a critical role in strengthening the ANFIS estimations generality.

Cloud Computing facilitates business by storing an enormous amount of data in the cloud transmitted over the Internet with seamless access to the data and no hardware compatibility limitations. However, data during transmission is vulnerable to man in middle, known plain text, chosen cipher text, related key and pollution attack.Therefore,uploading data on a single cloud may increase the risk of damage to the confidential data. Existing literature study uncovered multiple cryptography techniques such as SA-EDS, Reliable Framework for Data Administration (RFDA), Encryption and Splitting Technique (EST) to secure data storage over multi-cloud.However, existing methods are vulnerable to numerous attacks. This article emphasis on data security issues over multi cloud and proposes a Proficient Security over Distributed Storage (PSDS) method.PSDS divides the data is into two categories; normal and sensitive, further more the sensitive data is further divided into two parts. Each part is encrypted and distributed over multi-cloud whereas the normal data is uploaded on a single cloud in encrypted form. At the decryption stage, sensitive data is merged from multi-cloud. The PSDS is tested against multiple attacks and it has been concluded that it is resistant to related key attack, pollution attack, chosen ciphertext attack, and known plain text attack. Furthermore, PSDS has less computational time as compared to the STTN and RFD encryption method.

Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R2 = 0.81 and RMSE = 0.45 and SHDI6 with R2 = 0.82 and RMSE = 0.40.

The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.

Diabetic Retinopathy (DR) is an ophthalmic disease that damages retinal blood vessels. DR causes impaired vision and may even lead to blindness if it is not diagnosed in early stages. DR has five stages or classes, namely normal, mild, moderate, severe and PDR (Proliferative Diabetic Retinopathy). Normally, highly trained experts examine the colored fundus images to diagnose this fatal disease. This manual diagnosis of this condition (by clinicians) is tedious and error-prone. Therefore, various computer vision-based techniques have been proposed to automatically detect DR and its different stages from retina images. However, these methods are unable to encode the underlying complicated features and can only classify DR's different stages with very low accuracy particularly, for the early stages. In this research, we used the publicly available Kaggle dataset of retina images to train an ensemble of five deep Convolution Neural Network (CNN) models (Resnet50, Inceptionv3, Xception, Dense121, Dense169) to encode the rich features and improve the classification for different stages of DR. The experimental results show that the proposed model detects all the stages of DR unlike the current methods and performs better compared to state-of-the-art methods on the same Kaggle dataset.

Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, software-defined cloud computing (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

Accurate prediction of the remaining service life (RSL) of pavement is essential for the design and construction of roads, mobility planning, transportation modeling as well as road management systems. However, the expensive measurement equipment and interference with the traffic flow during the tests are reported as the challenges of the assessment of RSL of pavement. This paper presents a novel prediction model for RSL of road pavement using support vector regression (SVR) optimized by particle filter to overcome the challenges. In the proposed model, temperature of the asphalt surface and the pavement thickness (including asphalt, base and sub-base layers) are considered as inputs. For validation of the model, results of heavy falling weight deflectometer (HWD) and ground-penetrating radar (GPR) tests in a 42-km section of the Semnan–Firuzkuh road including 147 data points were used. The results are compared with support vector machine (SVM), artificial neural network (ANN) and multi-layered perceptron (MLP) models. The results show the superiority of the proposed model with a correlation coefficient index equal to 95%.

The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

In this paper, optimization of the first blade of a new test rig is pursued using a hybrid model comprising the genetic algorithm, artificial neural networks and design of experiments. Blade tuning is conducted using three-dimensional geometric parameters. Taper and sweep angle play important roles in this optimization process. Compressor characteristics involving mass flow and efficiency, and stress and eigenfrequencies of the blades are the main objectives of the evaluation. Owing to the design of blade attachments and their dynamic isolation from the disk, the vibrational behavior of the one blade is tuned based on the self-excited and forced vibration phenomenon. Using a semi-analytical MATLAB code instability, the conditions are satisfied. The code uses Whitehead’s theory and force response theory to predict classical and stall flutter speeds. Forced vibrational instability is controlled using Campbell’s theory. The aerodynamics of the new blade geometry is determined using multistage computational fluid dynamics simulation. The numerical results show increasing performance near the surge line and improvement in the working interval along with a 4% increase in mass flow. From the vibrational point of view, the reduced frequency increases by at least 5% in both stall and classical regions, and force response constraints are satisfied.

Evaporation rate is one of the key parameters in determining the ecological conditions and it has an irrefutable role in the proper management of water resources. In this paper, the efficiency of some data-driven techniques including support vector regression (SVR) and artificial neural networks (ANN) and combination of them with wavelet transforms (WSVR and WANN) were investigated for predicting evaporation rates at Tabriz (Iran) and Antalya (Turkey) stations. For evaluating the performances of studied techniques, four different statistical indicators were utilized namely the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Additionally, Taylor diagrams were implemented to test the similarity among the observed and predicted data. Outcomes showed that at Tabriz station, the ANN3 (third input combination that are air temperatures and solar radiation used by ANN) with RMSE of 0.701, MAE of 0.525, R of 0.990 and NSE of 0.977 had better performances in comparison with WANN, SVR and WSVR. So, the wavelet transforms did not have positive effects in increasing the precision of ANN and SVR predictions at Tabriz station. Also, approximately the same trend was seen at Antalya station. In other words, ANN5 (fifth input combination that are air temperatures, relative humidity and solar radiation used by ANN) with RMSE of 0.923, MAE of 0.697, R of 0.962 and NSE of 0.898 had a more accurate predictions among others. Conversely, wavelet transform reduced the prediction errors of SVR at Antalya station. So, the WSVR5 with RMSE of 1.027, MAE of 0.728, R of 0.950 and NSE of 0.870 predicted evaporation rates of Antalya station more precisely than other SVR models. As a conclusion, results from the current study proved that ANN provided reasonable trends for evaporation modeling at both Tabriz and Antalya stations.

The first aim of the present study is to investigate the feasibility of the biogas production potential from Spent Mushroom Compost. Hence, its secondary aim is to study the replacement of this energy with the fossil energy source and its impact on the reduction of greenhouse gases emission. The production potential of biogas was evaluated in a laboratory-scale Open Outlet Flow Batch Type Anaerobic digester. Setups of biogas production were set at temperatures of 35°C and 55°C. Results indicate that the cumulative production of biogas at 55°C was more than that at 35°C (364.8 and 314.11 L/kg.VS, respectively). The energy required for 1 ton of spent mushroom compost in one period of production was 204.24–5.35T kW.h/ton. The amount of energy produced in a period of biogas production was calculated to be 277.95 kW.h/ton at 35°C and 322.84 kW.h/ton at 55°C. Results show that the total produced spent mushroom compost of the country is equal to 414,491.7 tons per year. Accordingly, the potential of methane production was calculated to be 8690.8 × 103 m3 per year. It can also reduce 0.02% of the total annual production of CO2 in Iran.

In this study, ensemble models using the Bates–Granger approach and least square method are developed to combine forecasts of multi-wavelet artificial neural network (ANN) models. Originally, this study is aimed to investigate the proposed models for forecasting of chlorophyll a concentration. However, the modeling procedure was repeated for water salinity forecasting to evaluate the generality of the approach. The ensemble models are employed for forecasting purposes in Hilo Bay, Hawaii. Moreover, the efficacy of the forecasting models for up to three days in advance is investigated. To predict chlorophyll a and salinity with different lead, the previous daily time series up to three lags are decomposed via different wavelet functions to be applied as input parameters of the models. Further, outputs of the different wavelet-ANN models are combined using the least square boosting ensemble and Bates–Granger techniques to achieve more accurate and more reliable forecasts. To examine the efficiency and reliability of the proposed models for different lead times, uncertainty analysis is conducted for the best single wavelet-ANN and ensemble models as well. The results indicate that accurate forecasts of water temperature and salinity up to three days ahead can be achieved using the ensemble models. Increasing the time horizon, the reliability and accuracy of the models decrease. Ensemble models are found to be superior to the best single models for both forecasting variables and for all the three lead times. The results of this study are promising with respect to multi-step forecasting of water quality parameters such as chlorophyll a and salinity, important indicators of ecosystem status in coastal and ocean regions.

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil–water systems (Krw and Kro) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on Krw and Kro is still a major challenge for precise studying of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable and simple to use paradigms to predict the temperature dependency of Krw and Kro. To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for Krw and Kro, respectively. Lastly, the performance comparison against the preexisting correlations indicated the large superiority of the newly introduced correlations. The findings of this study can help for better understanding the temperature dependency of Krw and Kro in TEOR.

The combination of machine learning and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. Such numerical combination can develop a smart multiphase bubble column reactor with the ability of low-cost computational time when considering the big data. However, the accuracy of such models should be improved by optimizing the data parameters. This paper uses an adaptive-network-based fuzzy inference system (ANFIS) to train four big data inputs with a novel integration of computational fluid dynamics (CFD) model of gas. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to $R = 0.99 $, and the number of rules during the learning process has a significant effect on the accuracy of this type of modeling. Furthermore, the proper selection of model’s parameters results in higher accuracy in the prediction of the flow characteristics in the column structure.

The main innovation of the study is the use of a novel energo-environmental approach for investigation of biogas production, and analysis of the amount of methane and biogas produced in terms of energy production and global warming potential (GWP). Two types of reactors (laboratory-scale and semi-industrial reactors) were prepared for biogas production to perform a detailed study and for exact consideration of treatments in terms of production. Based on the results, the maximum methane production in the laboratory-scale set-up was related to a carbon/nitrogen (C/N) ratio of 30 at mesophilic temperature (35,967 ml/kg volatile solids). Accordingly, the C/N ratio in the semi-industrial reactor was considered to be 30; methane production was equal to 14/489m3 at loading rates of 237.5, 2.580 and 234.92 kg for cow manure, wheat straw and water content, respectively. The maximum biogas production occurred on day 65, from the viewpoint of energetic analysis. The highest daily net electricity production occurred on day 12, with a positive energy balance. However, considering GWP effects in the production and use of biogas, it would be better to stop production on day 48, in which case methane production would be equal to 77% of the final limit of biogas production.

In this paper the flutter behavior of a typical wing is investigated. The study is performed by presented Deferential Quadrature Method (DQM). The aerodynamic part adopted Wagner functions to model subsonic regime. Quasi steady and unsteady aerodynamics are considered to estimate the instability speed of the structure. Based on the presented model, a code is developed, for an arbitrary typical section beam. The obtained results validated the existing methods in the literature. The proposed method provides the advantage of finding the modes of oscillation and other dynamic parameters with less than 0.2% difference.

Natural companies employ turbine flow meters to measure natural gas which delivered to Compressed Natural Gas stations. The stations utilize compressors to increase pressure. The compressor produces a flow pulsation, which affects the accuracy of the measurement. The main aim of this article is to decrease the compressor effects on measurement accuracy by utilizing a snubber between the turbine flow meter and the reciprocating compressors. For this aim, numerical modeling has been built to simulate natural gas flow through a snubber. The effects of various snubber parameters on pressure pulsation have been investigated. The parameters included snubber volume to the minimum volume ratio, the ratio of height to diameter, outlet pipe length, and the existence and non-existence a buffer. The Ansys Fluent has been used for numerical modeling with transient analysis. Results show that in H/D value of 3, the maximum reduction in the percentage of pressure pulsation drop is about 47% and increasing the outlet pipe length to the 10 times of initial length causes a decrease of about 83% in pressure pulsations. Besides, for the ratio of snubber volume to the minimum volume from 1 to 16.7, the amplitude of pressure pulsations decreases from 4.1% to 0.25%.

Hydraulic designing of rockfill detention dam is based on the treatment of nondarcian and non-linear turbulence flow in coarse porous media. Previous literature focused only on the regular outlet ponds, and there is a shortcoming in the design methodology for detention rockfill dams. So, this paper develops a simple novel design framework by combining the governing equation of non-linear and non-darcian flow through rockfill dams with the hydrologic flood routing in reservoirs and hydraulic rules of storage dams. An innovative mathematical framework developed for preliminary designing of detention rockfill dams. The effects of type and size of rock fill materials, the shape of reservoir, shape, and intensity of inflow hydrograph on the peak of outflow, the volume of a stored flood, coefficients of flood peak and storage are investigated by parametric study in 36000 simulations. A simple design equation for preliminary designing of detention rockfill dams is provided and its applications in two design examples are presented. Its results indicate that the developed design equation (with R2 = 0.996, MAE = 0.008, and RMSE = 0.0041) is superior to the previous equations and can be used in preliminary designing of detention rockfill dams for flood peak reduction.

Solar radiation, moisture and temperature are the most vital meteorological variables which affect plant growth. Due to the fact that the global solar radiation (GSR) is scarcely gauged at meteorological stations in developing countries, it is commonly estimated by data-driven techniques or by empirical equations. In this study, support vector regression (SVR), model trees (MT), gene expression programming (GEP) and adaptive neuro–fuzzy inference system (ANFIS) and several empirical equations were applied to assess the relations between GSR and several meteorological variables including minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (RH), sunshine hours (n), maximum sunshine hours (N), corrected clear-sky solar irradiation (ICSKY), day of year (DOY) and extra-terrestrial radiation (Ra). For this purpose, the daily GSR measured from the beginning of 2011 to the end of 2013 at Tabriz synoptic station, which is located in semi-arid regions of Iran, were used. A direct strong relationship was observed to exist between the GSR and n. For evaluating the performances of studied techniques, three different statistical indicators were used namely root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). Additionally, a Taylor diagram was utilized to test the similarity between the observed and predicted GSR values. Results indicated that the SVR-6 with input parameters of Ra, RH, Tmin, Tmax, n/N had better accuracy in predicting GSR with RMSE of 1.656, MAE of 0.990, CC of 0.980 and WI of 0.990 than the other models. Moreover, MT-6 ranked as the second best model in the prediction of GSR values. As an interesting point, studied empirical equations had lower accuracies comparing with the SVR, GEP, MT and ANFIS methods. For instance, GSR values were computed by Angstrom and Prescott equation, as the best empirical equation, with RMSE of 1.786, MAE of 1.156, CC of 0.977 and WI of 0.988. Conclusively, results from the current study proved that the SVR provided reasonable trends for GSR modeling at Tabriz synoptic station. Furthermore, MT models with linear equations can be implemented with a high degree of simplicity and acceptable precision in GSR estimation.

Web content credibility implies finding credible and correct information on the web. Recent studies have shown there is an increasing trend of users turning towards the web for searching information related to a variety of topics including health, stocks, education, politics to name few. Information credibility is a critical factor in these domains for the decision makers. There is no limitation on the authorship of those articles and content. One criterion for evaluating credibility is to check the authority or source of information. However, there are situations when wrong information flows from credible sources. There are various approaches towards credibility assessment, broadly categorized into human-based and computational approaches. Computational approaches utilizing machine learning based techniques are computationally expensive. Reputation based approaches overcome this, however the latest work fails to take into account issue of negative referrals and utilizes simple summation as the calculation structure making it more resilient to attacks. This paper put forth verified hypothesis of direct relationship of credibility to the expertise of entity. Authors proposed a Bayesian based approach using feedback in the form of interaction among the entities to compute their expertise level, thereby showing improved results in terms of Precision, Correlation and Mean Average Error. The experiments are performed on two different datasets, one of the dataset is developed from a survey as the part of the research study. The results from the two experiments show that the reputation ranks are independent of the pattern of ratings and density of data, unlike previous techniques whose results were limited by these factors. The proposed technique gives 27% and 18% more precise results for the two experiments respectively compared to the baseline. The correlation results are also significant in both experiments for the proposed technique with significant values of 0.39 and 0.87 showing a linear relationship between predicted and original data. The paper also discusses the reputation attacks and proposes counter measures to tackle these attacks through simulation results.

Human immunodeficiency virus (HIV) prevalence owing to sexual contact has been on the rise in Iran since 2006, with those afflicted liable to social stigmatization. Focusing on the role of the media, this quantitative study investigates factors associated with HIV-related stigma in Iran by using a structural equation model (PLS-SEM). A questionnaire was administered to 315 respondents drawn through 2-stage cluster sampling carried out in Iran's capital city, Tehran. The results indicate that media exposure augments HIV-related knowledge and thus, diminishes the HIV-related stigma; this effect is, however, diminished by the underdeveloped role of the Iranian media in HIV-oriented discussions. Moreover, adherence to Islamic teachings on sexual relationships boosts HIV-related knowledge. Furthermore, obtaining information through interpersonal communications influences knowledge and stigma regarding HIV; this effect is more powerful than that of the media in Tehran. In a backdrop of a pervasive stigma surrounding HIV in Iran, interpersonal communications appear to play a more prominent role in obtaining HIV-related information than do the media, which calls for the further development of the media's capacity and communication power. Keywords: HIV-Related stigma, Media exposure, PLS-SEM

Accurate simulation of evaporation plays an important role in the efficient management of water Resources. Generally, evaporation is measured using the direct method where Class A pan-evaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012–2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott's Index and the Nash–Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran.

In this paper, investigations were performed on a dual-fueled constant-speed engine. Initially, the emissions and performance of a diesel engine were investigated, and after moving to the dual-fuel engine, experimental tests were carried out under different loads (10, 25, 50, 75, and 100% of the full load) and pilot to gaseous fuel (PGF) ratios (30, 40, and 50%). The results showed that under different loads and PGF ratios, the emissions of nitrous oxides and particle materials in the dual mode were lower than those for the diesel engine, and that the difference was significant. The emission of carbon dioxide in the dual-fueled mode varied little compared to that of the diesel mode, although it was lower than that for the diesel engine. In loads lower than 75% of the full load, the emission of carbon monoxide in the diesel engine was lower than that for the dual-fueled engine. However, in full load, this result was reversed and had significant difference. The dual mode had lower hydrocarbon emission compared to that of the diesel mode in all PGF ratios and loads. A computational fluid dynamics-based numerical simulation was performed by KIVA3V, and its results showed good agreements with the experimental results under cylinder pressure.

Biodiesel can easily be used as an alternative fuel in diesel engines. It is environmentally friendly and can be produced from low-cost feedstocks such as waste cooking oil (WCO). WCO contains a significant amount of free fatty acid, which is extracted by a two-step process of converting the free fatty acid by acid catalysis (H2S04) and converting the triglycerides using an NaOH catalyst. Currently, the major challenge for the industrial production of biodiesel is optimizing the yield while meeting American Society for Testing and Materials (ASTM) standards. In this study, experiments were performed to optimize the reaction conditions. The studied experimental parameters were the alcohol types (methanol, ethanol), the alcohol-to-oil molar ratio (AOMR; 3:1, 6:1, 9:1), the amount of catalyst (0.5, 1.0, 1.5 wt% of the oil), the temperature of the reaction (50, 60, 70, 80°C), the mixing intensity (300, 600, 900 rpm), and the reaction time (30, 60, 90 min). The biodiesel production yield (BPY) was optimized based on the experimental data. The optimum value of the BPY based on methanol is 95.92%, which is obtained at 73.80°C, with a reaction time of 74.02 min, an AOMR of 6.58:1, a catalyst concentration of 1.13 and a mixing intensity of 824.45 rpm. In the case of ethanol, the optimum BPY is 95.53%. which is obtained at 64.96°C, with a reaction time of 88.02 min, an AOMR of 7.005:1, a catalyst concentration of 1.25 and a mixing intensity of 592.18 rpm. These results of biodiesel production were confirmed by the experimental data.

Hydrogen is a clean energy source with a relatively low pollution footprint. However, hydrogen does not exist in nature as a separate element but only in compound forms. Hydrogen is produced through a process that dissociates it from its compounds. Several methods are used for hydrogen production, which first of all differ in the energy used in this process. Investigating the viability and exact applicability of a method in a specific context requires accurate knowledge of the parameters involved in the method and the interaction between these parameters. This can be done using top-down models relying on complex mathematically driven equations. However, with the raise of computational intelligence (CI) and machine learning techniques, researchers in hydrology have increasingly been using these methods for this complex task and report promising results. The contribution of this study is to investigate the state of the art CI methods employed in hydrogen production, and to identify the CI method(s) that perform better in the prediction, assessment and optimization tasks related to different types of Hydrogen production methods. The resulting analysis provides in-depth insight into the different hydrogen production methods, modeling technique and the obtained results from various scenarios, integrating them within the framework of a common discussion and evaluation paper. The identified methods were benchmarked by a qualitative analysis of the accuracy of CI in modeling hydrogen production, providing extensive overview of its usage to empower renewable energy utilization.

A genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to train the GNNE parameters. CLMS algorithm is used to achieve the optimum weights of components. Recorded data for two different stations located in Iran are used for the development of the GNNE models. Furthermore, the GNNE predictions are compared with the existing machine-learning models. The results demonstrate that GNNE outperforms other methods for the prediction of DSTs.

Evaporation accounts for varying shares of water balance under different climatic conditions, and its correct prediction poses a significant challenge before water resources management in watersheds. Given the complex and nonlinear behavior of the evaporation component, and according to the fact that this parameter is not measured at many meteorological stations, at least during some timeframes, and that the meteorological stations measuring this component are not properly distributed in many developing countries, including Iran, the main objective of this work was to predict the evaporation component at two meteorological stations (Rasht and Lahijan) located in Gilan province in northern Iran over the 2006–2016 time period. To that end, those meteorological parameters recorded at the two stations which had the highest impact on evaporation prediction were identified using Pearson correlation coefficient. Selected parameters were then used, under separate scenarios, as inputs to support vector regression (SVR) and SVR model coupled with firefly algorithm (SVR-FA) in order to simulate evaporation values on a daily scale. Evaporation amounts showed the highest correlation with net solar radiation and saturation vapor pressure deficit at Lahijan and Rasht stations, respectively. Root mean square error values of evaporation prediction at testing phase of SVR and SVR-FA ranged from 1.05 to 1.43 and 1.02 to 1.31 mm, respectively, at Lahijan station and from 1.02 to 1.28 and 0.88 to 1.17 mm, respectively, at Rasht station for various scenarios. For underpredicted evaporation data set, the magnitude of RMSE reduction from SVR1 to SVR7 was 27% at Lahijan and 18% at Rasht station; whereas RMSE decrement from SVR-FA1 to SVR-FA7 was 18 and 26 percent at Lahijan and Rasht stations, respectively. This means that for the underpredicted data set, the role of increasing the number of SVR and SVR-FA input parameters in decreasing evaporation prediction error has been more conspicuous at Lahijan and Rasht stations, respectively. Analysis of SVR and SVR-FA performance at various 2-mm intervals of measured evaporation showed that prediction error has generally been increasing with increment of evaporation values, with the highest errors observed at the 8-10 mm interval for both Lahijan and Rasht stations (error rates of 3.42 and 2.42 mm/day at Lahijan and 6.13 and 5.84 mm/day at Rasht station, with SVR1 and SVR-FA1 models, respectively).

Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people’s health and can even block the roads used to give emergency aid, worsening the situation. To cope with these issues, flood management systems (FMSs) are adopted for the decision-making process of critical situations. Nowadays, conventional artificial intelligence and computational intelligence (CI) methods are applied to early flood event detection, having a low false alarm rate. City authorities can then provide quick and efficient response in post-disaster scenarios. This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs. CI approaches are categorized as single and hybrid methods. The paper also identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management. Ensemble CI approaches are shown to be highly efficient for flood prediction.

Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.

Prediction of earthquakes has been long of interest of scientists to create a timely warning to save lives and reduce the damage. During the last few decades, scientists could record and classify the earthquakes’ effective parameters through careful studies. Precursor, as one of the most important parameters, presents the variation in the concentration of radon gas in the earth’s crust released by faults. Measuring and comparing this precursor requires the installation of appropriate hardware in the vicinity of the faults. The extraction of this gas and its lead ions will create additional precursors in the atmosphere layers. Through intelligent analyzing such historical meteorological data sets which are being measured and recorded in most parts of the world, the earthquakes can be predicted. In order to predict the magnitude and number of the earthquakes in this study, the particle filter-based and support vector regression is used. To evaluate the validity of the proposed method, the results are compared with multi layered perceptron neural network and support vector regression. The proposed method indicated the relationship between climatic data and the occurrence of earthquake leading to a precision of 96% for predicting the mean magnitude of earthquakes and a high accuracy of 78% for the expected earthquake count in a month. The accuracy of the method was measured by the correlation coefficient index.

Audio streams, such as news broadcasting, meeting rooms, and special video comprise sound from an extensive variety of sources. The detection of audio events including speech, coughing, gunshots, etc. leads to intelligent audio event detection (AED). With substantial attention geared to AED for various types of applications, such as security, speech recognition, speaker recognition, home care, and health monitoring, scientists are now more motivated to perform extensive research on AED. The deployment of AED is actually a more complicated task when going beyond exclusively highlighting audio events in terms of feature extraction and classification in order to select the best features with high detection accuracy. To date, a wide range of different detection systems based on intelligent techniques have been utilized to create machine learning-based audio event detection schemes. Nevertheless, the preview study does not encompass any state-of-the-art reviews of the proficiency and significances of such methods for resolving audio event detection matters. The major contribution of this work entails reviewing and categorizing existing AED schemes into preprocessing, feature extraction, and classification methods. The importance of the algorithms and methodologies and their proficiency and restriction are additionally analyzed in this study. This research is expanded by critically comparing audio detection methods and algorithms according to accuracy and false alarms using different types of datasets.

Abstract The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of ScienceTM Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references.

Abstract The widespread use of digital images has led to a new challenge in digital image forensics. These images can be used in court as evidence of criminal cases. However, digital images are easily manipulated which brings up the need of a method to verify the authenticity of the image. One of the methods is by identifying the source camera. In spite of that, it takes a large amount of time to be completed by using traditional desktop computers. To tackle the problem, we aim to increase the performance of the process by implementing it in a distributed computing environment. We evaluate the camera identification process using conditional probability features and Apache Hadoop. The evaluation process used 6000 images from six different mobile phones of the different models and classified them using Apache Mahout, a scalable machine learning tool which runs on Hadoop. We ran the source camera identification process in a cluster of up to 19 computing nodes. The experimental results demonstrate exponential decrease in processing times and slight decrease in accuracies as the processes are distributed across the cluster. Our prediction accuracies are recorded between 85 to 95% across varying number of mappers.

Wireless Body Area Network (WBAN) is a new trend in the technology that provides remote mechanism to monitor and collect patient’s health record data using wearable sensors. It is widely recognized that a high level of system security and privacy play a key role in protecting these data when being used by the healthcare professionals and during storage to ensure that patient’s records are kept safe from intruder’s danger. It is therefore of great interest to discuss security and privacy issues in WBANs. In this paper, we reviewed WBAN communication architecture, security and privacy requirements and security threats and the primary challenges in WBANs to these systems based on the latest standards and publications. This paper also covers the state-of-art security measures and research in WBAN. Finally, open areas for future research and enhancements are explored.

Using medical data mining models has been considered as a significant way to predict diseases in recent years. In the field of healthcare, we face a large amount of data, and this is one of the challenges in predicting and analyzing the target disease. With the help of data mining models, one can convert this data into valuable information, and through analyzing them logically and scientifically, one can reach accurate decision-making and actual prediction. Another challenge in the field of disease prediction is selecting features that are more significant than other features. Feature subset selection is performed to improve the performance of models with the highest accuracy. The purpose of this study is to select significant features by comparing data mining models to predict liver disease based on an extraction, loading, transformation, analysis (ELTA) approach for correct diagnosis. Hence, the data mining models are compared based on the ELTA approach, such as random forest, Multi-Layer Perceptron (MLP) neural network, Bayesian networks, Support Vector Machine (SVM), and Particle Swarm Optimization (PSO)-SVM. Among these models, the PSO-SVM model has the best performance regarding the criteria of specificity, sensitivity, accuracy, Area under the Curve (AUC), F-measure, precision, and False Positive Rate (FPR). Furthermore, a 10-fold cross-validation method for evaluation of models is used so that the models were evaluated on a liver disease dataset. The average of estimated accuracy was calculated as 87.35%, 78.91%, 66.78%, 76.51% and 95.17% for Random forest, MLP Neural network, Bayesian network, SVM and PSO-SVM models, respectively. Regarding the mentioned evaluation criteria, we obtained the highest performance of accuracy with the least number of features through the hybrid PSO-SVM-based optimized model. Keywords: Data mining, Liver disease, Classification models, Feature selection, Disease prediction

The paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia during 1946-2012 are used as case studies. Even though a number of mathematical functions have been proposed for modeling the precipitation estimation, these models still suffer from the disadvantages such as their being very demanding in terms of calculation time. Artificial neural network (ANN) can be used as an alternative to the analytical approach since it offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. Due to its being a crucial problem, this paper presents a process constructed so as to simulate precipitation with an adaptive neuro-fuzzy inference (ANFIS) method. ANFIS is a specific type of the ANN family and shows very good learning and prediction capabilities, which makes it an efficient tool for dealing with encountered uncertainties in any system such as precipitation. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.

The amount of water allocated to irrigation systems is significantly greater than the amount allocated to other sectors. Thus, irrigation water demand management is at the center of the attention of the Ministry of Agriculture and Forestry in Turkey. To plan more effective irrigation systems in agriculture, it is necessary to accurately calculate plant water requirements. In this study, daily reference evapotranspiration (ETo) values were estimated using tree-based regression and deep learning-based gated recurrent unit (GRU) models. For this purpose, 15 input scenarios, consisting of meteorological variables including maximum and minimum temperature, wind speed, maximum and minimum relative humidity, dew point temperature, and sunshine duration, were considered. ETo values calculated according to the United Nations Food and Agriculture Organization (FAO) Penman-Monteith method were considered as model outputs. The results indicate that the random forest model, with a correlation coefficient of 0.9926, is better than the other tree-based models. In addition, the GRU model, with R = 0.9837, presents good performance relative to the other models. In this study, it was found that maximum temperature was more effective in estimating ETo than other variables.

Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.

Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210.

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.