Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for Gene Expression Programming Based Power Transformer Fault Diagnosis

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.

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

Temperature and solar radiation-based modeling procedures are reported in this study for estimating daily reference evapotranspiration (ET0) by using gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS). A comparison is also made among these techniques and the corresponding traditional temperature/radiation-based ET0 estimation equations. Two data management scenarios were evaluated for estimating ET0: (1) the models were trained and tested using the local data of each studied weather station; and (2) the models were trained using the pooled data from all the stations and tested in each individual station. The GEP and ANFIS models were found to be better than the Hargreaves–Samani, Makkink and Turc ET0 equations in the first scenario. Comparison of GEP and ANFIS models trained with pooled data and tested for each station showed that the ANFIS models generally performed better than the GEP models. However, the comparison of GEP and ANFIS models trained and tested with pooled data revealed that the GEP models performed better than the ANFIS models in the second scenario.

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.

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

Prediction of flow duration curves for ungauged basins

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.

The hydraulic jump can be used for some purpose such as dissipating the flow energy in order to prevent bed erosion; aerating water and facilitating the mixing procedure of chemical that used for water purification. In this paper, various artificial intelligence (AI) models including gene expression programming (GEP), adaptive-neuro-fuzzy inference system with grid partition (ANFIS-GP), and neural networks (ANNs) were used to estimate developed and non-developed hydraulic jump length. Four various GEP, ANFIS-GP and ANN models including different combinations of Froude number, bed roughness height, upstream and downstream flow depth based on measured experimental data-set were developed to estimate hydraulic jump length variations. The root mean squared error (RMSE) and determination coefficient (R2) indices were applied for testing models’ accuracy. Regarding the comparison results, it was seen that the ANFIS-GP, ANN, and GEP models could be employed successfully in estimating hydraulic jump length. The comparison between three AI approaches emphasized the superiority of ANNs and ANFIS-GP over the other intelligent models for modeling developed and non-developed hydraulic jump length, respectively. For non-developed hydraulic jump, the R2 and RMSE values obtained as 0.87 and 2.84 for ANFIS-GP model.

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.

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.

Scouring downstream of grade control structure is a common problem. W-weir structures are recommended for controlling, reducing soil erosion, and development aquatic organisms habitat. W-weir structures can provide energy dissipation, increased aquatic habitat, grade-control, and bank and bed stabilization through the diversion of conveyance with the river-channel center. The current study aimed at analyzing the scour geometry downstream of W-weirs in the sinuous channels using heuristic gene expression programming (GEP) and support vector machine (SVM) models. A comparison was also made between the GEP and SVM with traditional multi variable linear regression (MLR) technique. The cross-validation technique (i.e. k-fold testing) was used to assess the developed models. Observations revealed that the scour typology affects the maximum scour depth value. The obtained results showed that both the GEP and SVM models give promising results in simulating the scour depth and scour length magnitudes. Assessing the models confirmed that using the k-fold testing cross validation is very necessary as all the available patterns can participate in training and testing stages, which reduces the risk of over fitting.

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.

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

This paper addresses processing of spectrophotometric array signals based on genetic algorithms (GA) least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. The key to complete LS-SVM regression is to choose its optimal parameters. Due to their outstanding ability in solving global optimization problems in complex multidimensional search space, GA are used in this study to obtain the optimal parameter combination of the LS-SVM model. Experimental results showed the GA-LS-SVM method to be successful for simultaneous multicomponent determination even where severe overlap of spectra was present. KeywordsLeast squares support vector machines; Genetic algorithms; Spectrophotometric array signals; Overlapping spectra; Artificial intelligence Nowadays, with the application of photometric diode array detector and computers, rapid scanning commercial spectrophotometers are capable of quickly generating huge data consisting of hundreds and even thousands of absorbance values per spectrum. The array data named fullspectrum contain sufficient information to be able to determine the contents of various compounds. The main drawback of ultraviolet-visible (UV-VIS) is its poor selectivity because in many cases UV-VIS spectra display strong overlaps, especially some less specific and selective chromagenic reagents often give rise to strongly overlapped spectra in many cases. The combination of artificial intelligence methods with the computer-controlled spectrophotometers was proven to be effective in overcoming this difficulty [1-3]. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system [4, 5]. However, ANN often has slow convergence, is prone to the existence of many local minima during training, and has a tendency of overfitting. Recently, a promising technology called support vector machines (SVM) has been used for classification and regression problems. SVM pioneered by Vapnik is a kind of machine learning method based on modern statistical learning theory and has notable properties including absence of local minima and high generalization ability [6, 7]. Suykens and his coworkers [8] introduced a modified version of SVM called least square SVM (LS-SVM) , which requires solving a set of linear equations instead of a quadratic programming problem and is much easier and computationally simpler than SVM. SVM and LS-SVM represent relatively recent artificial intelligence method and have found some applications in image analysis, classification and disease diagnosis etc. [9, 10]. It is worth mentioning that the success of LS-SVM model is highly dependent on the optimum choice of two parameters, the relative weight of regression error γ and the kernel width σ of radial basis function (RBF). Genetic algorithms (GA) [11, 12] introduced by John Holland are probabilistic optimization techniques based on natural evolution and genetics and Darwin’s theory of survival of the best. With their efficient and robust global search ability, GA are used to search two optimal parameters for the LS-SVM model simultaneously and automatically. The LS-SVM model then performs the regression task using these optimal parameters.

The diagnosis of incipient fault is very important for power transformer condition monitoring. The incipient faults are monitored by conventional and artificial intelligence (AI) based models. In this paper, the Proximal Support Vector Machine (PSVM) has been utilized to identify the incipient type of faults in an oil-immersed power transformer. Its performance is compared with traditional IEC/IEEE and AI methods (i.e. ANN and SVM). The juxtaposition of fault classification of ANN and SVM method notify that proposed approach is much swiftly. Simultaneous identification of oil immersed power transformer incipient faults has never been identified formerly by using Multi-PSVM. The desired test analysis of experimental data from working transformers in the Northern Power Grid of India has been executed to present the robustness of evaluated incipient faults for large variation in loading and operational conditions perturbations.