Oil price forecasting using gene expression programming and artificial neural networks

Laboratory experiments were used to estimate the hydraulic performance of emitters, i.e., the emitter flow variation (qvar) and manufacturer’s coefficient of variation (CVm), by measuring the discharge of different labyrinth-channel emitters at different operating pressures (P) and water temperatures (T). Considering the importance of the structural parameters of the labyrinth-channel emitters in drip irrigation design, which has been experimentally confirmed, artificial neural network (ANN) and gene expression programming (GEP) models were developed to predict qvar and CVm. The ANN and GEP models were trained and tested using structural parameters (including the number, height (H), and spacing of trapezoidal units and the flow path width and length) of different labyrinth-channel emitters, P and T as the input variables, and qvar and CVm as the outputs. Statistical criteria, including the coefficients of correlation (r), relative root-mean-square error (RRMSE), and mean absolute error (MAE), were used to examine the accuracy of the developed models. The ANN models exhibited good correlation with experimental values, with high r values 0.995 and 0.969 for qvar and 0.997 and 0.947 for CVm in the training and testing processes, respectively. The ANN models had lower RRMSE and MAE values than the GEP models. Furthermore, H was the dominant variable for obtaining the most accurate prediction model. The results confirm that the ANN models are superior to the GEP models for the prediction of the hydraulic performance of emitters.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Hydraulic jumps (HJs) play a vital role in energy dissipation in hydraulic systems and are critical for the effective design of water management structures. This study employed Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models to predict the roller length ratio (L*) of HJs over rough beds. The analysis utilized a dataset of 367 experimental observations with a 70–30 training and testing split. Comprehensive data descriptions were conducted, ensuring a detailed understanding of the inputs, including the upstream Froude number (F*), the ratio of initial to sequent HJ depth (H* = h2/h1), and the ratio of channel bed roughness to initial HJ depth (K* = ks/h1). Descriptive statistics revealed moderate variability and mostly symmetric distributions, making the dataset suitable for predictive modeling. A sensitivity analysis was conducted and confirmed that the depth ratio (H*) had the highest influence on L*, followed by F* and K*. The ANN model achieved a training R2 of 0.937 and a testing R2 of 0.935, with RMSEs of 1.737 and 1.719, respectively. The GEP model demonstrated a training R2 of 0.941 and a testing R2 of 0.930, with RMSEs of 1.682 and 1.780. Both models displayed reliable predictive capabilities, with minimal bias and consistent performance in unseen data, supported by comprehensive error distribution analysis and uncertainty evaluations. Moreover, the models demonstrated a high level of agreement with prior research results, highlighting the importance of thorough data characterization and model validation. Thus, ANN and GEP models have been recognized as effective techniques for predicting hydraulic jump length.Graphical

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.

This paper is aimed to introduce and validate a gene expression programming (GEP) model to estimate the rate of air flow in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure and air outlet pressure. To achieve the aim of this study, a series of laboratory experiments were designed and carried out and then a database comprising 47 datasets was prepared to develop new predictive models. A gene expression programming (GEP) model for prediction of air flow was proposed using the prepared datasets. In this regard, a series of sensitivity analyses were performed to choose the best GEP model. For comparison purposes, multiple regression (MR) analysis was also employed for air flow estimation. Several performance indices, i.e., coefficient of determination (CoD), mean absolute error (MAE), root mean square error (RMSE) and variance account for (VAF) were considered and calculated to evaluate the performance prediction of the developed models. Considering both training and testing datasets, the developed GEP model can provide higher performance prediction of rate of air flow in comparison to the MR model.

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized.

Prediction of flow duration curves for ungauged basins

Prediction of velocity is an important parameter in curved channels. Recently, artificial intelligent methods are developing than the common costly and time-consuming experimental and numerical methods in estimation of flow variables. In the present study, Gene expression programming (GEP) model as developed genetic programming model is used to predict the flow velocity in the channel with a sharp 90° bend. Different input parameter combinations are examined in order to finding the optimum model then the model efficiency are evaluated in estimation of the flow velocity in training and testing phases. By comparing the results of the GEP model with experimental data, there is an acceptable accordance between results with the root mean square error (RMSE) values of 0.828 and 0.8197 in the training and testing phase. The results show that the input parameters of (r,q) (polar points coordinate and the flow discharge) are the most effective parameters in bend flow pattern surveying. With increasing the flow discharge, the model error at the end section of the bend in the outer channel wall is decreased. Also a practical and reliable relationship is proposed by the GEP model and the sensitivity of suggested equation to each of input parameter is evaluated. Furthermore, the uncertainty of the proposed GEP model in the velocity prediction is assessed and different confidence bounds are introduced.

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.

Investigating the corrosion performance of Ti-6Al-4V biomaterial alloy with hydroxyapatite coating by artificial neural network

Computer-aided Prediction of the ZrO2 Nanoparticles' Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens

Water is a vital element that plays a central role in human life. This study assesses the status of indicators based on water resources availability relying on hydro-social analysis. The assessment involves countries exhibiting decreasing trends in per capita renewable water during 2005–2017. Africa, America, Asia, Europe, and Oceania encompass respectively 48, 35, 43, 20, and 5 countries with distinct climatic conditions. Four hydro-social indicators associated with rural society, urban society, technology and communication, and knowledge were estimated with soft-computing methods [i.e., artificial neural networks, adaptive neuro-fuzzy inference system, and gene expression programming (GEP)] for the world’s continents. The GEP model’s performance was the best among the computing methods in estimating hydro-social indicators for all the world’s continents based on statistical criteria [correlation coefficient (R), root mean square error (RMSE), and mean absolute error]. The values of RMSE for GEP models for the ratio of rural to urban population (PRUP), population density, number of internet users and education index parameters equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe and Oceania, respectively. Scalable equations for hydro-social indicators are developed with applicability at variable spatial and temporal scales worldwide. This paper’s results show the patterns of association between social parameters and water resources vary across continents. This study’s findings contribute to improving water-resources planning and management considering hydro-social indicators.

Prediction of crude oil prices in COVID-19 outbreak using real data