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Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables

AGRICULTURAL WATER MANAGEMENT 2019年 215卷 63-73页

作者： Maroufpoor, Saman Shiri, Jalal Maroufpoor, Eisa Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Univ Kurdistan Fac Agr Dept Water Engn Sanandaj Iran

The coefficient of uniformity (CU), an important parameter in design of irrigation systems, affects the quality and return of investment in irrigation projects significantly, and is a good indicator of water losses. In this paper, a single model was proposed to obtain the CU values in four sprinkler types of ZK30, ZM22, AMBO, and LUXOR. Average wind speed, coarseness index (large and small nozzle diameters), and sprinkler/lateral spacing were used as input parameters to obtain the CU values through employing the artificial neural networks (ANN), neuro-fuzzy grid partitioning (NF-GP), neuro-fuzzy sub-clustering (NF-SC), least square support vector machine (LS-SVM) and gene expression programming (GEP) techniques. The available data set consisted of 294 samples that were used to evaluate the proposed methodology. The applied techniques were assessed through the robust k-fold testing data assignment mode. Based on the results, all the applied models presented good capability in estimating CU. The obtained results revealed that the coarseness index (large nozzle diameter) had the lowest impact on modeling CU is sprinkler irrigation systems.

关键词： Coefficient of uniformity gene expression programming k-Fold testing Support vector machines Neural networks Neuro-fuzzy Sprinkler irrigation

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GEP-basedclassifiers with drift-detection

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EXPERT SYSTEMS 2021年第7期38卷 e12571-e12571页

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Fac Math Phys & Informat PL-80308 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst Gdynia Poland

In the paper, we propose two gene expression programming (GEP)-based ensemble classifiers with different drift detection mechanisms. In the related work section, we briefly review GEP as a classification tool, incremental classifiers, and concept drift detectors. Next, the structure of our two-level GEP ensemble with metagenes is described. Further on, two integrated classifiers with drift detection algorithm and Wilcoxon rank sum test drift detector are proposed. The approach is validated in the computational experiment in which several real-life and artificial datasets with concept drift have been used. Experiment confirmed that the proposed approach can be competitive to existing solutions. In the conclusion section, we briefly outline directions for future research.

关键词： classification algorithms concept drift detection ensemble classifiers gene expression programming

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Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques

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ENGINEERING WITH COMPUTERS 2019年第2期35卷 659-675页

作者： Faradonbeh, Roohollah Shirani Taheri, Abbas Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia

Rockburst phenomenon is the extreme release of strain energy stored in surrounding rock mass which could lead to casualties, damage to underground structures and equipment and finally endanger the economic viability of the project. Considering the complex mechanism of rockburst and a large number of factors affecting it, the conventional criteria cannot be used generally and with high reliability. Hence, there is a need to develop new models with high accuracy and ease to use in practice. This study focuses on the applicability of three novel data mining techniques including emotional neural network (ENN), gene expression programming (GEP), and decision tree-based C4.5 algorithm along with five conventional criteria to predict the occurrence of rockburst in a binary condition. To do so, a total of 134 rockburst events were compiled from various case studies and the models were established based on training datasets and input parameters of maximum tangential stress, uniaxial tensile strength, uniaxial compressive strength, and elastic energy index. The prediction strength of the constructed models was evaluated by feeding the testing datasets to the models and measuring the indices of root mean squared error (RMSE) and percentage of the successful prediction (PSP). The results showed the high accuracy and applicability of all three new models;however, the GA-ENN and the GEP methods outperformed the C4.5 method. Besides, it was found that the criterion of elastic energy index (EEI) is more accurate among other conventional criteria and with the results similar to the C4.5 model, can be used easily in practical applications. Finally, a sensitivity analysis was carried out and the maximum tangential stress was identified as the most influential parameter, which could be a guide for rockburst prediction.

关键词： Rockburst occurrence Data mining techniques Emotional neural network gene expression programming C4 5 algorithm Conventional criteria

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Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods

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JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA 2019年第7期68卷 573-584页

作者： Jafari, Robabeh Torabian, Ali Ghorbani, Mohammad Ali Mirbagheri, Seyed Ahmad Hassani, Amir Hessam Islamic Azad Univ Dept Environm Engn Sci & Res Branch Tehran Iran Univ Tehran Dept Civil Environm Engn Tehran Iran Tabriz Univ Dept Water Engn Tabriz Iran Near East Univ Engn Fac Mersin 10 TR-99138 Nicosia North Cyprus Turkey Khajeh Nasir Toosi Univ Technol Dept Civil Environm Engn Tehran Iran

Aquifers are one of the largest available freshwater resources. In this paper, total dissolved solids (TDS) of the groundwater aquifer in Tabriz plain is estimated by groundwater physicochemical parameters including Na, HCO3, Ca, Mg, and SO4 in the eastern region of Urmia Lake. For this purpose, four soft computing approaches, namely, multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and gene expression programming (GEP) were used to predict TDS for a period of 10 years (2002-2012). Data were collected from the East Azerbaijan Regional Water Organization, which totaled 1,742 samples. In the application, of the whole data set, 70% (1,220 samples) was used for training and 30% (522 samples) for testing. In the following, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) statistics were used for evaluating the accuracy of the models. According to the results, MLP, ANFIS, SVM, and GEP models could be employed successfully in estimating TDS alterations. A comparison was made between these soft computing approaches that corroborated the superiority of the GEP model over MLP, SVM, and ANFIS models with RMSE = 58.93, R = 0.998, and MAE = 5.21.

关键词： adaptive neuro-fuzzy inference system gene expression programming multilayer perceptron support vector machine total dissolved solids

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Data splitting strategies for improving data driven models for reference evapotranspiration estimation among similar stations

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 162卷 70-81页

作者： Shiri, Jalal Marti, Pau Karimi, Sepideh Landeras, Gorka Univ Tabriz Fac Agr Water Engn Dept Tabriz Iran Univ Illes Balears Dept Biol Area Engn Agroforestal Carretera Valldemossa Km 7-5 Palma De Mallorca 07022 Spain AB Basque Country Res Inst Agr Dev NEIKER Alava Basque Country Spain

In the last years, different heuristic data driven models have been proposed to estimate reference evapotranspiration (ETo) with high performance accuracy as an alternative to empirical and physically-based approaches. However, these models, despite their complexity and soundness, rely on finite data series, like the empirical approaches, and their actual practical validity highly depend on the data management adopted in their development and assessment, in particular on the data splitting adopted. A major issue for ensuring a sound assessment of the heuristic model performance is the definition of a suitable criterion for splitting the data series in training and testing data. The present study evaluates new different data set splitting strategies based on the adoption of ancillary external inputs for enhancing the performance of the gene expression programming- based models for estimating ETo. All models are assessed using k-fold validation considering annual test sizes. The results show that it is preferable to incorporate the external target variable as input to feed the new model, rather than to incorporate the original external input variables of the model. Regarding the external performance of the models, it is crucial to select a suitable training station for each testing station for providing accurate enough estimations. This way, the applicability of such approaches is not limited to local emergency models, but it allows estimating ETo elsewhere without the need of training previously a local model using local targets. Finally, it is important to select properly which station/s will provide external ancillary ETo inputs to the training process, because otherwise they introduce noise to the model and decrease their generalizability.

关键词： Data driven models Evapotranspiration Ancillary inputs gene expression programming

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Dataset used to develop soft computing models that predict the stiffness modulus of bituminous mixtures

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DATA IN BRIEF 2024年 54卷 110382页

作者： Leon, Lee P. Martin, Hector Rathnayake, Upaka Felix, Portia Univ West Indies Fac Engn Dept Civil & Environm Engn St. Augustine 32080 Trinidad Tobago Univ Belfast Queens Univ Belfast Dept Civil Engn Belfast North Ireland Atlantic Technol Univ Fac Engn & Design Dept Civil Engn & Construct Ash Lane Sligo F91 YW50 Ireland

This data article presents information on the measurement of Indirect Tensile Stiffness Modulus of laboratory and field asphalt mixtures. The asphalt mixes are composed of three distinct binders that were categorised by their penetration grade (40/55-TLA, 60/75-TLA, and 60/70-MB) and aggregates (limestone, sharp sand, and filler). The asphalt mixtures are called dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA). The variables in the dataset were selected in accordance with the specifications of the dynamic modulus models that are currently in use as well as the needs for the quality control and assurance (QC & QA) assessment of asphalt concrete mixes. The data parameters included are temperature, asphalt content, and binder viscosity, air void content, cumulative percent retained on 19, 12.5, and 4.75 mm sieves, maximum theoretical specific gravity, aggregate passing #200 sieve, effective asphalt content, density, flow, marshal stability, coarse-to-fine particle ratio and the Indirect Tensile Stiffness Modulus (ITSM). Utilising soft computing techniques, models were developed utilising the data thus eliminating the requirement for complex and timeconsuming laboratory testing.

关键词： Asphalt concrete gene expression programming Pavement design Indirect tensile stiffness modulus Multi expression programming Pavement materials

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Optimization of cesium adsorption by Prussian blue using experiments and gene expression modeling

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JOURNAL OF WATER PROCESS ENGINEERING 2021年 41卷

作者： Nguyen, Mai Ngoc Yaqub, Muhammad Kim, Seongbeom Lee, Wontae Kumoh Natl Inst Technol Dept Environm Engn 61 Daehak Ro Gumi 39177 South Korea

Water-soluble and insoluble Prussian blue (PB) named PB-sol and PB-insol are synthesized to investigate the removal of cesium (Cs+) from an aqueous solution. During synthesis, different molar ratios of potassium hexacyanoferrate and ferric chloride are tested for adsorption of Cs+. The effect of synthesized PBs on the pH of deionized water (DI) and surface water is also studied. The effect of aqueous solution pH, competitive ions, and initial concentration of Cs+ on adsorption by PB-sol and PB-insol are explored separately as well as in combination with zeolite. Moreover, the adsorption kinetics are discussed by applying pseudo 1st and pseudo 2nd order kinetic models, and gene expression programming (GEP) model is developed for the first time to optimize the process. The PB-sol and PB-insol led to a lower pH of the aqueous solution. PB-insol/zeolite showed maximum adsorption of 91.8 mg g (-1) at an initial Cs+ concentration of 100 ppb in the surface water, whereas PB-sol, PBinsol, and PB-sol/zeolite demonstrated comparatively lower results. An increasing trend with higher initial concentrations is observed, while high pH and the absence of competing ions also favored the Cs+ adsorption. A significant decrease is noticed in presence of both monovalent and divalent ions (K+, Na+, Ca2+, and Mg2+). The kinetics are better described by the pseudo 2nd order kinetics model because of the physiochemical adsorption. Moreover, the developed GEP model presented reasonably good results for PB-sol and PB-insol by tracing the input-output relationship with R-2 = 0.799 and 0.886, respectively, for the testing dataset.

关键词： Adsorption Cesium gene expression programming Kinetics Prussian blue

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Prediction of water quality parameters using evolutionary computing-based formulations

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2019年第10期16卷 6377-6396页

作者： Najafzadeh, M. Ghaemi, A. Emamgholizadeh, S. Grad Univ Adv Technol Fac Civil & Surveying Engn Dept Water Engn POB 76315-116 Kerman Iran Shahrood Univ Technol Dept Water & Soil Engn Shahrood Iran

Rivers, as one of the crucial components of water resources, play a substantial role in human life. Due to a wide range of water consumptions, water quality is emphatically recommended assigning at a permissible level. Enormous field investigations have been carried out to quantify indicators of water quality parameters which have a great impact on the water quality. In this way, there are not explicit equations to predict WQPs with an allowable degree of accuracy. In the present work, gene expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) have been employed to estimate three indices including biochemical oxygen demand (BOD), dissolved oxygen, and chemical oxygen demand (COD). To develop the proposed models, nine input parameters Ca2+, Na+, Mg2+, NO2-, NO3-, PO43-, EC, PH, and turbidity were selected as effective variables. Results of training and testing stages for these approaches have been investigated. Performance of the models indicated that the relative superiority of the EPR approach compared to the GEP and MT models. Gamma test was applied to determine important parameters for predicting BOD, COD, and COD indices. It was found that PO43-, Ca2+, and pH have the most significant effect on BOD, COD, and COD, respectively.

关键词： Water quality parameters River Evolutionary polynomial regression gene expression programming Model tree Gamma test

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Mathematical modeling and multi-attribute rule mining for energy efficient job-shop scheduling

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JOURNAL OF CLEANER PRODUCTION 2019年 241卷 118289-000页

作者： Zhang, Liping Li, Zhixiong Krolczyk, Grzegorz Wu, Dazhong Tang, Qiuhua Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Hubei Peoples R China Ocean Univ China Sch Engn Tsingdao 266100 Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China Opole Univ Technol Dept Mfg Engn & Automat Prod PL-45758 Opole Poland Univ Cent Florida Dept Ind Engn & Management Syst Dept Mech & Aerosp Engn Orlando FL 32816 USA

Manufacturing industry accounts for about one-third of the world's total energy consumption (TEC). This study aims to develop a novel mixed-integer mathematical model to represent the direct energy consumption of machines and indirect energy consumption on a shop floor. In comparison with traditional modeling methods, this paper proposes an effective gene expression programming-based rule mining (GEP-RM) algorithm to generate dispatching rules automatically. This method consists of three attributes that have significant impacts on the TEC of a manufacturing process. In addition, diversified rule mining operators with self-learning are designed to ensure population diversity and convergence. Moreover, a perturbation trigger mechanism for reconstructing rules is introduced to avoid being trapped into a local optimum. An unsupervised learning algorithm is achieved by setting the evolution direction with global best and current worst in order to mine the value of the substantial historical data. Experimental results have shown that the proposed multi-attribute rule mining approach outperforms other dispatching rules in terms of energy saving and production efficiency. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Dispatching rule Energy consumption gene expression programming Job-shop scheduling Unsupervised learning

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Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches

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NEURAL COMPUTING & APPLICATIONS 2019年第12期31卷 8661-8680页

作者： Ahmadi, Mohsen Jafarzadeh-Ghoushchi, Saeid Taghizadeh, Rahim Sharifi, Abbas UUT Dept Ind Engn POB 57166-419 Orumiyeh Iran

This study presents a new hybrid algorithm for forecasting economic growth using indicators of knowledge-based economy (KBE). The algorithm consists of three steps, namely preprocessing, processing, and postprocessing. Preprocessing consists of principal component analysis and reproduction algorithm, which are used to decrease the number of variables and increase the volume of data. Economic growth is predicted during processing using multilayer perceptron (MLP), adaptive neuro-fuzzy inferences system, and gene expression programming (GEP). The variables are added separately to the process. The best model is selected during the postprocessing step to forecast economic growth. GEP model is used to forecast unique indicators. The last step involves substitution of forecasted indicators in the best model. In this study, the KBE indicators of Iran from 1993 to 2013 are predicted in the processing step. The MLP model is used, which includes four indicators, namely technological foundation, structure of trained manpower, export and trademark, and employee. Indicators are also forecasted using the GEP model between 2013 and 2020. The results are used to estimate economic growth between forecasting periods. A self-organizing map is used to recognize relationships between variables. The results show the efficiency of the algorithm in multivariate forecasting.

关键词： Hybrid forecasting Artificial neural network gene expression programming Economy growth

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