Multilayer Perceptron Artificial Neural Network Model Research Articles

Predicting reservoir sedimentation using multilayer perceptron – Artificial neural network model with measured and forecasted hydrometeorological data in Gibe-III reservoir, Omo-Gibe River basin, Ethiopia

The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sustainable reservoir sedimentation planning and management and to minimize reservoir storage capacity loss. The main objective of this study was to estimate and predict reservoir sedimentation using multilayer perceptron–artificial neural network (MLP-ANN) and random forest regressor (RFR) models in the Gibe-III reservoir, Omo-Gibe River basin. The hydrological and meteorological parameters considered for the estimation and prediction of reservoir sedimentation include annual rainfall, annual water inflow, minimum reservoir level, and reservoir storage capacity. The MLP-ANN and RFR models were employed to estimate and predict the amount of sediment accumulated in the Gibe-III reservoir using time series data from 2014 to 2022. ANN-architecture N4-100-100-1 with a coefficient of determination (R2) of 0.97 for the (80, 20) train-test approach was chosen because it showed better performance both in training and testing (validation) the model. The MLP-ANN and RFR models' performance evaluation was conducted using MAE, MSE, RMSE, and R2. The models’ evaluation result revealed that the MLP-ANN model outperformed the RFR model. Regarding the train data simulation of MLP-ANN and RFR shown R2 (0.99) and RMSE (0.77); and R2 (0.97) and RMSE (1.80), respectively. On the other hand, the test data simulation of MLP-ANN and RFR demonstrated R2 (0.98) and RMSE (1.32); and R2 (0.96) and RMSE (2.64), respectively. The MLP-ANN model simulation output indicates that the amount of sediment accumulation in the Gibe-III reservoir will increase in the future, reaching 110 MT in 2030–2031, 130 MT in 2050–2051, and above 137 MTin 2071–2072.

Coastal groundwater quality prediction using objective-weighted WQI and machine learning approach.

The water quality index (WQI) is a globally accepted guideline to indicate the water quality standard of any groundwater resource. Water levels in existing groundwater sources are declining in several coastal zones. Therefore, for monitoring water quality and improving water management, the prediction and identification of groundwater status by an effective technique with higher accuracy is urgently needed. Therefore, this research aims to find an effective model for WQI prediction by comparing entropy and critic weight-based WQI (ENW-WQI and CRITIC-WQI) with multi-layer perceptron artificial neural network (MLP-ANN) technique and also to identify contaminated zones using GIS. Initially, 1000 water sampling datasets with concentrations of several water quality parameters of different coastal blocks of eastern India during 2018 to 2022 are considered for the estimation of ENW-WQI and CRITIC-WQI. It shows 65% and 67% of the samples are excellent to good for drinking. ENW-WQI and CRITIC-WQI-based MLP-ANN models have been established considering different data portioning and hidden neuron numbers. Input variables and appropriate dataset partitioning with hidden neurons for models obtained from correlation and trial-error analysis. Spatial distribution maps are also produced for calculated WQIs using inverse distance weighted interpolation approaches. Three fitting models are obtained: ENW-WQI-MLP-ANN, CRITIC-WQI-MLP-ANN-I and CRITIC-WQI-MLP-ANN-II. CRITIC-WQI-MLP-ANN-II model (data ratio 85:15, network structure 6-12-1, R2 = 0.986, NSE = 0.98, and error rate 0.49%) provides the best accuracy in WQI prediction. The GIS-based WQI maps record several areas related to drinking water quality. The results of this research can help in planning the provision of safe drinking water in the future.

Modelling and forecasting India's electricity consumption using artificial neural networks

AbstractPrecise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand.

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.

Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

In this study, the prediction of pine mistletoe distribution in Scots pine ecosystems was explored using remote sensing variables to compare the multilayer perceptron (MLP) artificial neural network (ANN) and logistic regression (LR) model performances. For this purpose, 109 sample plots were distinguished in pure Scots pine forests (natural) in the Eastern Black Sea Region of Turkey. Distinguishing mistletoe-infected stands (69) and uninfected stands (40) was performed with field observations. The variables acquired from Landsat 8 (Level 1) images were used as independent variables for independent-sample t-test, MLP ANN and LR models. Remote sensing variables indicated that mistletoe-infected stands were in drier areas with a lower vegetation-leaf area index. Based on the performance results of both models, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy of the MLP ANN model were superior to those of the LR model. The prediction percentages (SEN, SPE, PPV and NPV) of mistletoe-infected stands were better than the prediction percentages of uninfected stands. The prediction accuracies of LR and MLP ANN models were 74.3% and 89.6%, respectively. However, all remote sensing variables were included in the prediction equation of the MLP ANN model, while the thermal infrared 1 (TIRS1) variable was included in the LR model. In the MLP ANN model, the TIRS1 variable also had the highest normalized importance (100%). The area under the curve (AUC) value for identifying the mistletoe-infected stands of Scots pine forests used by the MLP ANN model (0.892 ± 0.034) was higher than in the LR model (0.838 ± 0.039), explaining the more accurate predictions obtained from the MLP ANN model. The MLP ANN model showed much better performance than the LR model. The results of this study are expected to make important contributions to the identification of potential mistletoe-infected areas.

Machine learning coupled with group contribution for predicting the density of deep eutectic solvents

Deep Eutectic Solvents (DESs) are a recently introduced class of green solvents with unique and favorable characteristics. Despite their recent debut, the scientific community has begun to place greater emphasis on them as alternatives to ionic liquids (ILs). Knowledge of the various physical properties of DESs is essential for various applications in the chemical industries and related fields. In this study, a comprehensive database including 1410 density data points, from 166 different DESs at various temperatures and atmospheric pressure, were retrieved from open literature to develop models to increase the accuracy of density predictions. The densities of DESs were used to develop two commonly used machine learning models, namely Multilayer Perceptron Artificial Neural Network (MLPANN) and Least Square Support Vector Machine (LSSVM), in conjunction with the group contribution (GC) method. Based on the GC method, each fragment of a compound contributes a specific amount to the physical property's value. By considering this, the prediction ability was improved by applying the GC method in the model development procedure. Both models predict the DES densities by taking into account the effect of 35 functional groups, the temperature, and the HBA/HBD molar ratios. The optimum MLPANN model structure consists of a single hidden layer with five neurons and a logarithmic sigmoid transfer function. By employing this MLPANN-GC model, the values of the squared correlation coefficient, R2, and absolute average relative deviation percent, AARD%, were 0.99 and 0.61%, respectively, while for the LSSVM-GC model (with the radial basis function (RBF) kernel), they were 0.99 and 0.56%, respectively. Also, K-fold cross-validation was used to assess the performance of the LSSVM-GC model. The presented machine learning models in this study were found to perform more accurately than those obtained using the best current correlations and GC models for DES densities in the open literature. The more accurate results, in addition to the enhanced predictability behavior of the developed models, give these models a preference for use in industrial and academic applications.

An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India

• The hourly wind speed prediction is an important problem. • A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for prediction. • The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. • In the experiments, 38 years of hourly wind data belonging to 5 cities were used. • Grey Wolf Optimizer produced best results in terms of mean square error (MSE). The growing population has tremendously increased the daily energy demand all around the world. India is the second-most crowded nation in the world with approximately 1.4 billion people. New and renewable energy is on the agenda of India and in 2021 India possesses the fourth-largest installed capacity of wind power. Accurate prediction of wind speed is vital in wind farm design and operation. In this work, an hourly wind speed prediction with an artificial neural network optimized by a metaheuristics approach is conducted. A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for the prediction of the hourly wind speed. In this study, 38 years of hourly wind data belonging to 5 cities (Ambur, Hosur, Kumbakonam, Nagapattinam, and Pudukottai) were used. These cities have different specific properties such as latitude, longitude, and altitude. The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. In this work, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization (BBO), Evolutionary Strategy (ES), Genetic Algorithm (GA), Grey-Wolf-Optimizer (GWO), Population-Based Incremental Learning (PBIL), Particle Swarm Optimization (PSO), Tree-Seed Algorithm (TSA) have been used to optimize the weights of the ANN. GWO outperforms other metaheuristic algorithms in the prediction of wind speed with a FF MLP ANN model, with a success percentage rate of approximately 3% to 10,000%.

Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing.

This study aimed to predict some soil water contents and soil erodibility indices with a multilayer perceptron (MLP) artificial neural network (ANN) using remote sensing data (Landsat 8 OLI TIRS) and topographic variables from a digital elevation model (DEM) in a semi-arid ecosystem. In models, the input variables were derived from remote sensing imaging and DEM. The output variables were field capacity, wilting point, aggregate stability index, structural stability index, dispersion ratio, and clay flocculation index. This study was realized in the watersheds of the Koruluk dam, the Kızlarkalesi, and the Telme ponds built for agricultural irrigation in Gümüşhane-Şiran. The soil samples were obtained from two depths (0-10cm and 10-20cm) from 59 soilprofiles. Besides field capacity, wilting point, and aggregate stability analysis, undispersed/dispersed sand, silt, clay contents, and organic matter analysis were performed due to their strong effect on soil moisture, soil water content, and erodibility indices. The correlation analysis results showed significant relationships between soil characteristics and soil water contents/soil erodibility indices. The remote sensing variables were derived from three Landsat images of 2015 (June, July, and September). The performance results of MLP ANN models predicted for soil water contents and erodibility indices ranged from 0.75 to 0.90 for R2, 0.046-4.115 for root mean square error (RMSE), 4.46-6.54 for normalized root mean square error (NRMSE), and 0.042-0.186 for mean absolute error (MAE). Topography was a more significant group of variables that affected soil water contents and soil erodibility indices and the feature importance of topography in the prediction was over 55%. The results showed that the use of topographic variables together with remote sensing variables in MLP ANN modeling increased the performance of the models.

Financial distress prediction using integrated Z-score and multilayer perceptron neural networks

The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore, financial risk forecasting continues to be a central issue in financial planning, dealing with new types of uncertainty. This paper presents a stock market forecasting model combining a multi-layer perceptron artificial neural network (MLP-ANN) with the traditional Altman Z-Score model. The contribution of the paper is presentation of a new hybrid enterprise crisis warning model combining Z-score and MLP-ANN models. The new hybrid default prediction model is demonstrated using Chinese data. The results of empirical analysis show that the average correct classification rate of thew hybrid neural network model (99.40%) is higher than that of the Altman Z-score model (86.54%) and of the pure neural network method (98.26%). Our model can provide early warning signals of a company's deteriorating financial situation to managers and other related personnel, investors and creditors, government regulators, financial institutions and analysts and others so that they can take timely measures to avoid losses.

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation

Time-resolved knowledge of physico-chemical properties of Municipal Solid Waste (MSW) materials and their thermal energy content is one of the important subjects needed to build waste incineration power plants around the world. For this purpose, machine learning models were used to predict the Higher Heating Value (HHV) of MSW based on the initial materials. Four types of machine learning methods: Radial Bias Function Artificial Neural Network (RBF-ANN), Multilayer Perceptron Artificial Neural Network (MLP-ANN), Support Vector Machine (SVM) and Adaptive Nero-Fuzzy Inference System (ANFIS) were used for modeling the HHV with six different inputs (carbon, water, hydrogen, oxygen, nitrogen, sulfur, and ash). The results showed that RBF-ANN can predict the HHV of MSW with higher accuracy than other models. The overall Mean Absolute Percentage Error (MAPE) for MLP-ANN, SVM and ANFIS models were 7.3, 11.77 and 23.76%, respectively. The MAPE of the best topology for RBF model (6-17-1) with spread factor of 0.8 reach to 0.45%. Finally, the results of this study proved that ANN's can be used as a practical tool with high accuracy and reliability for design and management of waste incineration plants.

Soft-computing modeling and multiresponse optimization for nutrient removal process from municipal wastewater using microalgae

This study develops empirical models for the prediction of the removal efficiencies of inorganic Nitrogen (N) and Phosphorus (P) from municipal wastewater using microalgae (Chlorella kessleri). Our work identified the effects of operational parameters of temperature (T), light-dark cycle (LD), and nitrate-to-phosphate (N:P) ratio on simultaneous N and P removal. Three competitive soft-computing techniques known as response surface methodology (RSM), multilayer perceptron artificial neural network (MLP-ANN), and support vector regression (SVR) were applied to construct the predictive models using real-life experimental data obtained via the Box-Behnken Design (BBD) matrix. A Bayesian optimization algorithm was applied to automatically tune the hyperparameters to develop optimized MLP-ANN and SVR models. The overall results exhibited that the SVR model is better than MLP-ANN and RSM models to assess simultaneous N and P removal efficiencies. The extra simulated data further confirmed the prediction capability of the developed SVR models under different conditions. Finally, the models developed by SVR were hybridized with a genetic algorithm (GA) to maximize the nutrient (N and P) removal efficiency (>93%) at optimum conditions as 29.3°C, 24/0 h/h of LD, and 6:1 of N:P.

Process intensification of hydrogen production by catalytic steam methane reforming: Performance analysis of multilayer perceptron-artificial neural networks and nonlinear response surface techniques

Uncertainty about how process factors affect output might lead to waste of resources in laboratory experiments. To address this constraint, a data-driven method might be used to describe the non-linear connection between process parameters and desired output. A Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN) and non-linear response surface method are used to predict hydrogen generation by catalytic steam methane reforming. The impact of training methods (scaled conjugate and gradient descent), hidden layer variation, artificial neuron variation, and activation functions were studied in 80 MLP-ANN combinations (hyperbolic tangent function and sigmoid function). The performance of MLP-ANN models was affected by the training techniques, activation functions, layer count, and number of artificial neurons. The model with the sigmoid function and 3 input layers, 17 artificial neurons in the first layer, 15 artificial neurons in the second layer, and 2 output nodes had the greatest performance among the 40 configurations of scaled conjugate trained ANNs. It projected an 89.55% maximal hydrogen yield with a coefficient of determination (R2) of 0.997 and reduced errors with Mean absolute percentage error (MAPE) and mean squared error (MSE) of 0.199 and 0.121, respectively. Similarly, the gradient descent ANN model with hyperbolic tangent activation function had the greatest performance among the 40 gradient descent trained-ANN configurations. The 3–15–7–2 gradient descent trained ANN model projected a maximum hydrogen output of 89.73% compared to the experimental results of 89.51%. The MLP-ANN models outperformed nonlinear response surface methods, with R2, MAPE, and MSE of 0.231, 0.191, and 0.988, respectively. The updated Garson algorithm indicated that the input parameters impacted the hydrogen production in the sequence reaction temperature>methane partial pressure>steam partial pressure. The sensitivity analysis might assist identify how resources should be spent.

CT-Based Prediction of Liver Function and Post-PVE Hypertrophy Using an Artificial Neural Network.

Background: This study aimed to evaluate whether hypertrophy after portal vein embolization (PVE) and maximum liver function capacity (LiMAx) are predictable by an artificial neural network (ANN) model based on computed tomography (CT) texture features. Methods: We report a retrospective analysis on 118 patients undergoing preoperative assessment by CT before and after PVE for subsequent extended liver resection due to a malignant tumor at RWTH Aachen University Hospital. The LiMAx test was carried out in a subgroup of 55 patients prior to PVE. Associations between CT texture features and hypertrophy as well as liver function were assessed by a multilayer perceptron ANN model. Results: Liver volumetry showed a median hypertrophy degree of 33.9% (16.5–60.4%) after PVE. Non-response, defined as a hypertrophy grade lower than 25%, was found in 36.5% (43/118) of the cases. The ANN prediction of the hypertrophy response showed a sensitivity of 95.8%, specificity of 44.4% and overall prediction accuracy of 74.6% (p < 0.001). The observed median LiMAx was 327 (248–433) μg/kg/h and was strongly correlated with the predicted LiMAx (R2 = 0.89). Conclusion: Our study shows that an ANN model based on CT texture features is able to predict the maximum liver function capacity and may be useful to assess potential hypertrophy after performing PVE.

Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation.

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

Predictive Maintenance of Bus Fleet by Intelligent Smart Electronic Board Implementing Artificial Intelligence

This paper is focused on the design and development of a smart and compact electronic control unit (ECU) for the monitoring of a bus fleet. The ECU system is able to extract all vehicle data by the on-board diagnostics-(ODB)-II and SAE J1939 standards. The integrated system Internet of Things (IoT) system, is interconnected in the cloud by an artificial intelligence engine implementing multilayer perceptron artificial neural network (MLP-ANN) and is able to predict maintenance of each vehicle by classifying the driver behavior. The key performance indicator (KPI) of the driver behavior has been estimated by data mining k-means algorithm. The MLP-ANN model has been tested by means of a dataset found in literature by allowing the correct choice of the calculus parameters. A low means square error (MSE) of the order of 10−3 is checked thus proving the correct use of MLP-ANN. Based on the analysis of the results, are defined methodologies of key performance indicators (KPIs), correlating driver behavior with the engine stress defining the bus maintenance plan criteria. All the results are joined into a cloud platform showing fleet efficiency dashboards. The proposed topic has been developed within the framework of an industry research project collaborating with a company managing bus fleet.

Prediction of nanofluid viscosity using multilayer perceptron and Gaussian process regression

More than a decade, a numerous experimental and theoretical studies of thermophysical properties of nanofluids are conducted to reveal its heat transfer characteristics. Due to nanofluid unique thermal properties, it is broadly used in various applications from automobile applications to biomedical applications. Despite that various experimental and theoretical studies of nanofluids are developed, the accordance between them is very little and also it is tiresome and expensive. To predict the thermal properties in an easy way, soft computing tools are utilized. In this research work, dynamic viscosity ratio of Al2O3/H2O is predicted using machine learning techniques like multilayer perceptron and Gaussian process regression. In the proposed multilayer perceptron—artificial neural network model, varying a range of neurons in the hidden layer and using Levenberg–Marquardt as training function, it is found that 6 neurons in the hidden layer give less root mean square error value of 0.01118. Different kernel functions are opted to train the proposed Gaussian process regression model, and it is found that Matern kernel function shows the best performance with less root mean square error value of 0.018, and regression coefficient value of both the models is 0.99. This research work will reduce the experimental test run cost, and the models are accurate in prediction.

Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes.

Reliable prediction of municipal solid waste (MSW) generation rates is a significant element of planning and implementation of sustainable solid waste management strategies. In this study, the multi-layer perceptron artificial neural network (MLP-ANN) is applied to verify the prediction of annual generation rates of domestic, commercial and construction and demolition (C&D) wastes from the year 1997 to 2016 in Askar Landfill site in the Kingdom of Bahrain. The proposed robust predictive models incorporated selected explanatory variables to reflect the influence of social, demographical, economic, geographical and touristic factors upon waste generation rates (WGRs). The Mean Squared Error (MSE) and coefficient of determination (R2) are used as performance indicators to evaluate effectiveness of the developed models. MLP-ANN models exhibited strong accuracy in predictions with high R2 and low MSE values. The R2 values for domestic, commercial and C&D wastes are 0.95, 0.99 and 0.91, respectively. Our results show that the developed MLP-ANN models are effective for the prediction of WGRs from different sources and could be considered as a cost-effective approach for planning integrated MSW management systems.

A neural computing strategy to estimate dew-point pressure of gas condensate reservoirs

A novel multilayer perceptron artificial neural network (MLP-ANN) model is proposed to estimate the dew-point pressure (DPP) of gas condensate reservoirs as a function of gas composition, reservoir temperature and, molecular weight and specific gravity of C7+. For this purpose, a comprehensive database was prepared by reviewing literature and the results of MLP-ANN are graphically and statistically compared with these actual values. The R-squared (R2) and mean relative error are determined to be 0.9868 and 1.5%, respectively, which reveals that DPP values are well predicted by this model. Furthermore, the MLP-ANN model is compared with previous developed models.

Estimating biofuel density via a soft computing approach based on intermolecular interactions

In this work, the density of biofuel is estimated using four intelligent models: a Least Square Support Vector Machine (LSSVM), a Radial Basis Function Artificial Neural Network (RBF-ANN), a Multi-layer Perceptron Artificial Neural Network (MLP-ANN), and an Adaptive Network-based Fuzzy Inference System (ANFIS). These models are used to estimate the density of biofuel based on intermolecular interactions and the van der Waals radii of the atoms. Various statistical analyses are performed on the original (experimental) and estimated data. It is found that the LSSVM model can provide more accurate predictions than the other three models. The R-squared value (R2) and the mean absolute relative error (MARE) for the LSSVM, RBF-ANN, MLP-ANN and ANFIS models are 0.847 & 0.056, 52.067 & 0.379, 57.385 & 0.371 and 65.096 & 0.678, respectively. This study shows that the LSSVM model is a promising tool for estimating the density of biofuel, offering an alternative to classic thermodynamic models.