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Modeling soil enzyme activity using easily measured variables: Heuristic alternatives

APPLIED SOIL ECOLOGY 2021年 157卷 103753-103753页

作者： Ebrahimi, Mitra Sarikhani, Mohammad Reza Shiri, Jalal Shahbazi, Farzin Univ Tabriz Fac Agr Dept Soil Sci Tabriz Iran Univ Tabriz Water Engn Fac Agr Tabriz Iran Univ Tabriz Fac Civil Engn Ctr Excellence Hydroinformat Tabriz Iran

In the present research, gene expression programming (GEP) and artificial neural network (ANN) techniques were used to estimate soil enzyme activity (SEA), including urease, alkaline phosphatase and dehydrogenase. Data from 65 soil samples located in Mirabad region, Suldoz plain (West Azerbaijan, Iran) were used to test the adopted methodology. The soil samples were selected from areas with different land usages (apple orchard, crop production, and rich pasture). Various combinations of the input parameters including soil texture, pH, organic carbon (OC), electrical conductivity (EC), microbial biomass carbon (MBC), and microbial soil respiration (SIR) were utilized to feed the applied models. The root mean square error (RMSE) and the coefficient of determination (R-2) were employed for assessing the models' performance accuracy. The highest R-2 and lowest RMSE were obtained for the models that used all available input parameters. The results showed that among targets (urease activity (UA), alkaline phosphatase (ALP) and/or dehydrogenase activity (DHA)), the highest performance accuracy was obtained for urease activity models. The obtained results revealed that the most effective parameters in estimating urease activity were soil texture, pH, EC and OC;where about 69% and 68% of its variability was predictable by the ANN and GEP, respectively.

关键词： Artificial neural network Dehydrogenase gene expression programming Phosphatase Urease

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Estimating n-tetradecane/bitumen mixture viscosity in solvent-assisted oil recovery process using GEP and GMDH modeling approaches

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PETROLEUM SCIENCE AND TECHNOLOGY 2019年第14期37卷 1640-1647页

作者： Rostami, Alireza Hemmati-Sarapardeh, Abdolhossein Mohammadi, Amir H. PUT Dept Petr Engn Ahvaz Iran Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran IRGCP Paris France Univ KwaZulu Natal Sch Engn Discipline Chem Engn Durban South Africa

In this study, the methods of group method of data handling (GMDH) and gene expression programming (GEP) were used to develop symbolic correlations for truthful viscosity estimation of n-tetradecane/bitumen mixture. A number of graphical and statistical tools were utilized to make evident the omnipotence of the proposed models as compared to the published literature correlations. It is found that the GMDH model is the best acting approach with the lowest total error of 2.51% and the highest R-2 of 0.9994. Sensitivity analysis verifies that concentration of the solvent is the most influencing input parameter on the viscosity estimation of n-tetradecane/bitumen mixture.

关键词： bitumen viscosity gene expression programming group method of data handling solvent-assisted recovery process sensitivity analysis

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A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran

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WATER SUPPLY 2019年第1期19卷 165-178页

作者： Emamgholizadeh, Samad Demneh, Razieh Karimi Shahrood Univ Technol Dept Soil & Water Shahrood Iran Gorgan Univ Water Engn Gorgan Golestan Iran

The estimation of the suspended sediment load in rivers is one of the main issues in hydraulic engineering. Different traditional methods such as the sediment rating curve (SRC) can be used to estimate the suspended sediment load of rivers. The main problem with this method is its low accuracy and uncertainty. In this study, the ability of three intelligence models namely: gene expression programming (GEP), artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) were compared with the SRC method. The daily flow discharge and sediment discharge at two hydrometric stations of the Kasilian and Telar rivers in the period of 1964-2014 were used to develop intelligence models. The performance of these methods indicated that all intelligence models give reliable results in the estimation of the suspended sediment load and their performance was better than the SRC method. Moreover, results showed that the GEP model with a high coefficient of determination (R-2) and a low mean absolute error (MAE) was better than both the ANN and ANFIS models for the estimation of daily suspended sediment load of the two sub-basins of the Kasilian and Telar rivers.

关键词： adaptive neuro inference fuzzy system artificial neural network gene expression programming sediment rating curve suspended sediment load

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A Service-Oriented Remanufacturing Framework With Recovery Timing Prediction Based on Remote Condition Monitoring

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IEEE ACCESS 2019年 7卷 70046-70058页

作者： Liu, Qingtao Shang, Ziyu Ding, Kai Lv, Jingxiang Changan Univ Key Lab Rd Construct Technol & Equipment MoE Xian 710064 Shaanxi Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Cardiff Univ Sch Engn Inst Energy Cardiff CF24 3AA S Glam Wales

Remanufacturing is generally regarded as a key technology to implement cleaner production. However, in traditional remanufacturing, scrap products are recycled and remanufactured after their performance declines sharply. This passive approach easily arises many problems such as increases of remanufacturing cost, unstable product quality, and unsatisfactory customer demand, which brought great challenges to the remanufacturing industry. To address these challenges, a novel framework, namely service-oriented remanufacturing (SORM), is proposed to improve the overall efficiency of remanufacturing. Contrast to the traditional mode, SORM actively recovers in-service products at the optimal recovery time based on their real-time performance obtained by remote monitoring. The operational logic and implementation path of SORM is firstly discussed. Then the recovery timing prediction (RTP) model, as the core issue of the SORM, is presented to figure out the optimal recovery time of in-service products. Moreover, a comprehensive method combining a two-parameter Weibull distribution (TPWD) and gene expression programming (GEP) is developed to solve the model. The example of excavator remanufacturing illustrates the feasibility of the SORM. Finally, the key findings and managerial implications from application results and discussion are summarized, which provides the theoretical guidance and technical support for better sustainable development.

关键词： Service-oriented remanufacturing recovery timing prediction remote condition monitoring Weibull distribution gene expression programming

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Deep gene selection method to select genes from microarray datasets for cancer classification

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BMC BIOINFORMATICS 2019年第1期20卷 1-15页

作者： Alanni, Russul Hou, Jingyu Azzawi, Hasseeb Xiang, Yong Deakin Univ Sch Informat Technol Geelong Vic Australia

Background: Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis. Results: The gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost. Conclusions: We provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples' classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

关键词： gene selection Microarray Evolutionary algorithms gene expression programming

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GEP and MLR approaches for the prediction of reference evapotranspiration

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NEURAL COMPUTING & APPLICATIONS 2019年第10期31卷 5843-5855页

作者： Mattar, Mohamed A. Alazba, A. A. King Saud Univ Coll Food & Agr Sci Agr Engn Dept POB 2460 Riyadh 11451 Saudi Arabia King Saud Univ Alamoudi Water Res Chair Riyadh 11451 Saudi Arabia

In this study, reference evapotranspiration (ETo) is modeled as one of the major items of hydrological applications from different combinations of climatic variables using two different techniques: gene expression programming (GEP) and multiple linear regression (MLR). The data used in modeling were collected from weather stations in Egypt through the CLIMWAT database. The Penman-Monteith FAO-56 equation was considered as a reference target for ETo values depending on the entire climatic variables. The developed ETo models' performances were compared and evaluated with regard to their predictive abilities using statistical criteria to identify the superiority of one modeling approach over the others and determine climatic variables which have a significant effect on ETo. The results indicated that GEP and MLR models' contribution toward mean relative humidity and wind speed at 2 m height is greater compared to that of other variables. Meanwhile, when adding temperature data to models, solar radiation has a slight effect on increasing the accuracy of ETo estimate. Moreover, the lower statistical error criteria values of GEP models confirmed their better performance than MLR models and other empirical equations.

关键词： Evapotranspiration gene expression programming Linear regression Penman-Monteith

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Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods

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COMPOSITE STRUCTURES 2019年 215卷 69-84页

作者： Naderpour, H. Nagai, K. Fakharian, P. Haji, M. Semnan Univ Fac Civil Engn Semnan Iran Univ Tokyo Inst Ind Sci Tokyo Japan

There are several methods for predicting experimental results such as empirical methods, elasticity and plasticity theory. Among these methods, the use of soft computing has been expanded due to good capabilities and high accuracy in predicting the target. Soft computing contains computational techniques and algorithms to provide useful solutions to deal with complex computational problems. In this study, three methods including Artificial neural networks, Group method of data handling and gene expression programming are utilized to predict the compressive strength of columns confined with FRP. Total of 95 experimental data were selected to form the model. The height of the column, the compressive strength of unconfined concrete, the elastic modulus of FRP, the area of longitudinal steel, the yield strength of longitudinal steel and confinement pressure provided by FRP and transverse steel were considered as input parameters, while the compressive strength of FRP-confined columns was considered as the target. The proposed methods are compared with the existing models and provide great accuracy in predicting the results. Among the utilized methods, the ANN model showed the highest accuracy.

关键词： Composite Confinement Artificial neural networks gene expression programming Group method of data handling Concrete column

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Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar

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Scientific reports 2025年第1期15卷 19414页

作者： Naseer Muhammad Khan Liqiang Ma Waleed Bin Inqiad Muhammad Saud Khan Imtiaz Iqbal Muhammad Zaka Emad Saad S Alarifi Xinjiang Key Laboratory of Coal-bearing Resources Exploration and Exploitation Xinjiang Institute of Engineering Urumqi 830023 China. nmkhan@mce.nust.edu.pk. Key Laboratory of Xinjiang Coal Resources Green Mining (Xinjiang Institute of Engineering) Ministry of Education Urumqi 830023 China. nmkhan@mce.nust.edu.pk. Xinjiang Engineering Research Center of Green Intelligent Coal Mining Xinjiang Institute of Engineering Urumqi 830023 China. nmkhan@mce.nust.edu.pk. Sustainable Advanced Geomechanical Engineering Military College of Engineering National University of Sciences and Technology Risalpur 23200 Pakistan. nmkhan@mce.nust.edu.pk. School of Mines China University of Mining and Technology Xuzhou 221116 Jiangsu China. nmkhan@mce.nust.edu.pk. Xinjiang Key Laboratory of Coal-bearing Resources Exploration and Exploitation Xinjiang Institute of Engineering Urumqi 830023 China. ckma@***. Key Laboratory of Xinjiang Coal Resources Green Mining (Xinjiang Institute of Engineering) Ministry of Education Urumqi 830023 China. ckma@***. Xinjiang Engineering Research Center of Green Intelligent Coal Mining Xinjiang Institute of Engineering Urumqi 830023 China. ckma@***. School of Mines China University of Mining and Technology Xuzhou 221116 Jiangsu China. ckma@***. Department of Civil Engineering College of Engineering & Physical Sciences Aston University Aston St Birmingham B4 7ET UK. Department of Civil Engineering Price Faculty of Engineering University of Manitoba Winnipeg MB R3T 5V6 Canada. Department of Petroleum Engineering King Fahd University of Petroleum and Minerals Dhahran Eastern Province Saudi Arabia. Department of Geology and Geophysics College of Science King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia.

The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing [Formula: see text] of 0.998 followed by BR having [Formula: see text] values equal to 0.946 while MEP had the lowest testing [Formula: see text] of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn't produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry.

关键词： Cement mortar Compressive strength gene expression programming Interpretable machine learning Metakaolin

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Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms

Results in Engineering

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Results in Engineering 2025年 26卷

作者： Ouafa Belkacem Ahmed Rezrazi Kamel Aizi Lokmane Abdelouahed Maamar Laidi Abdelhafid Touil Leila Cherifi Salah Hanini Laboratory of Biomaterials and Transport Phenomena (LBMTP) Yahia Fares University Medea Algeria The Central Directorate of Research and Development (DCRD) SONATRACH Boumerdes Algeria Laboratoire des sciences et technologies de l’information de la communication et de la connaissance (Lab-STICC) University of Bretagne Sud France LSPC—Laboratoire de Securité des Procédes Chimiques INSA Rouen Normandie UNIROUEN Normandie Univiversity 76000 Rouen France

Barite scale formation is a persistent and detrimental challenge in the oil industry during water-flooding operations, primarily due to the incompatibility between injected water and formation water. This study introduces a novel and highly effective approach for predicting barite scale formation and inhibition by leveraging advanced artificial intelligence (AI) techniques. Innovative intelligent models, including Random Forest (RF), k-nearest Neighbors (KNN), Extreme Learning Machine (ELM), Support Vector Regression (SVR), Decision Trees (DT), and Multilayer Perceptron (MLP), were developed and optimized for this purpose. Notably, the MLP and SVR models were enhanced through training with the particle swarm optimization algorithm (PSO) and data augmentation techniques. The performance of the models was rigorously evaluated using statistical criteria and graphical tools, demonstrating exceptional accuracy with minimal errors. The ELM model emerged as the most precise (RMSE = 0.0005, R² = 0.999), closely followed by the SVR-PSO model (RMSE = 0.0008, R² = 0.999). Additionally, the ELM model successfully estimated over 95 % of the data points, with an applicability domain of 97.82 %. Moreover, we conducted an additional validation using a new independent dataset, which showed an excellent correlation coefficient (0.99) and a very low RMSE (0.01), further demonstrating the robustness and generalizability of the proposed models. This work shows the transformative potential of AI-driven modeling in addressing complex challenges such as barite scale formation. The findings underscore the practical implications of AI techniques in optimizing oilfield operations, enhancing efficiency, and reducing costs, thereby offering a significant advancement in the field.

关键词： Barite Inhibition Formation Intelligent models Water flooding Data augmentation AI Artificial Intelligence ANN Artificial Neural Network ANNs Artificial Neural Networks AARE Average Absolute Relative Error ARE Absolute Relative Error BFGS Broyden-Fletcher-Goldfarb-Shanno BR Bayesian Regularization C Ba ₂₊ Barium concentration C So4 ₂- Sulfate concentration C inb Concentration of barite inhibitor Cp Solubility of barite scale inhibitors C SVR specific parameter CFBPN Cascade-Forward Back-Propagation Network DETPMP Diethylenetriaminepenta (methylene phosphonic acid) DT Decision Tree EOR Enhanced Oil Recovery ELM Extreme Learning Machine GEP gene expression programming GBDT Decision Trees Optimized by Gradient Boosting GRNN generalized Regression Neural Network GWO Grey Wolf Optimizer Kᵢ Initial permeability K d The detected permeability K d exp Experimental permeability damage K d pred Predicted permeability damage Knn The k-nearest neighbors LSSVM Least Squares Support Vector Machine MLP Multi-Layer Perceptron MSE Mean Square Error MAE Mean Absolute Error Q Flow rate PPCA Poly-Phosphono Carboxylic Acid PSO Particle Swarm Optimization RELU Rectified Linear Unit RBF Radial Basis Function RMSE Root Mean Square Error R 2 Correlation Coefficient RF Random Forest SCG Scaled Conjugate Gradient SVR Support Vector Regression SVM Support Vector Machine SD Standard Deviation SHAP Shapley additive explanations T Temperature t Injection time V inj Volume of water injection ΔP Differential pressure ε Epsilon, SVR specific parameter

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A new conventional criterion for the performance evaluation of gang saw machines

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MEASUREMENT 2019年 146卷 159-170页

作者： Haghshenas, Sina Shaffiee Faradonbeh, Roohollah Shirani Mikaeil, Reza Haghshenas, Sami Shaffiee Taheri, Abbas Saghatforoush, Amir Dormishi, Alireza Urmia Univ Technol Fac Min & Met Engn Orumiyeh Iran Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia Tarbiat Modares Univ Fac Engn Tehran 14115143 Iran Shahrood Univ Technol Fac Min Petr & Geophys Shahrood Iran

The process of cutting dimension stones by gang saw machines plays a vital role in the productivity and efficiency of quarries and stone cutting factories. The maximum electrical current (MEC) is a key variable for assessing this process. This paper proposes two new models based on multiple linear regression (MLP) and a robust non-linear algorithm of gene expression programming (GEP) to predict MEC. To do so, the parameters of Mohs hardness (Mh), uniaxial compressive strength (UCS), Schimazek's F-abrasiveness factor (SF-a), Young's modulus (YM) and production rate (Pr) were measured as input parameters using laboratory tests. A statistical comparison was made between the developed models and a previous study. The GEP-based model was found to be a reliable and robust modelling approach for predicting MEC. Finally, according to the conducted parametric analysis, Mh was identified as the most influential parameter on MEC prediction. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Gang saw machine Carbonate rocks Cutting dimension stones Maximum electrical current gene expression programming Multiple linear regression

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