Search

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.

The volumetric properties of hydrocarbons such as density have particular effects on designing industrial processes so in the current study, the densities of hydrocarbon mixtures have been ...

Floods are among the most frequently occurring natural disasters and the costliest in terms of human life and ecosystem disturbance. Identifying areas susceptible to flooding is important for devel...

High values of higher heating value (HHV) for municipal solid wastes (MSWs) turn them into promising sources of energy nowadays. Accurate prediction of HHV values is investigated using intelligent ...

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.

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.

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%.

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, radial basis function (RBF) and multilayer perceptron (MLP) neural networks were proposed for accurate prediction of under-saturated oil viscosity. To this end, more than 600...

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.

Mutual Authentication is the initial and the most significant line of defense in any open and trusted network system. Weaknesses in mutual authentication protocol may lead to eavesdropping, unautho...

Todays, using wireless sensor networks including a large number of sensor nodes located in the target environment to monitor physical and environmental activities has widely expanded due to their b...

Back propagation (BP) neural networks are one of the most commonly used artificial neural networks, widely applied in many theoretical and practical areas. The performance of BP neural networks is ...

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).

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 s...

This study uses the empirical models of extreme learning machine (ELM) method to predict daily horizontal diffuse solar radiation (HDSR). As a possibility for modification, the recent hybrid ELM methods such as complex ELM (C-ELM), self-adaptive evolutionary ELM (SaE-ELM), and online sequential ELM (OS-ELM) have been developed for the prediction of the daily HDSR. The empirical model of ELM predicts the HDSR using clearness index as the sole predictor. For this aim, two types of correlations are evaluated: (1) the diffuse fraction-clearness index and (2) the diffuse coefficient-clearness index. The measured diffuse and global solar radiation data sets of southern Iranian cities (Yazd, Shiraz, Bandar Abbas, Bushehr, and Zahedan) are utilized to evaluate the models. The precision of the C-ELM, SaE-ELM, OS-ELM, and ELM models is evaluated for different regions on the basis of five statistical performance evaluation parameters. The results confirm that the performance of hybrid ELM is pretty accurate ...

The confluence of two natural rivers or two artificial channels is of special importance due to the complexity of flow behavior. Such complexity has considerable effects on the accuracy of flow characteristics measured by instruments. The main objective of this study is to investigate the effect of width and discharge ratio change on open-channel junction velocity distribution and flowmeter mean velocity measurement accuracy. To accomplish this, a CFD model of an open-channel junction was simulated and validated with an experimental study. After validation, the open-channel junction was modeled with various width and discharge ratios. The results showed that increasing the width ratio from W* = 0.5 to W* = 2.0 decreases the effect of tributary flow on main flow, which in turn results in a decrease in flowmeter measurement error from 73% to 35%, respectively. Also, the increment of discharge ratio from 0.25 to 0.75 reduces flowmeter measurement error from 59.5% to 13.65%.