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Deep Heuristic Evolutionary Regression Model Based on the Fusion of BiGRU and BiLSTM

COGNITIVE COMPUTATION 2023年第5期15卷 1672-1686页

作者： Xu, Lixiang Xu, Wei Cui, Qingzhe Li, Mengying Luo, Bin Tang, Yuanyan Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China Univ Sci & Technol China Sch Publ Affairs Hefei 230026 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China FST Univ Macau Zhuhai UM Sci & Technol Res Inst Macau Peoples R China

The input for stock market prediction is usually a period of stock price data with time series characteristics, which will keep changing over time and have more complex background relationships. How to effectively mine and fuse multiple heterogeneous data of the stock market is difficult to be handled by traditional recurrent neural networks (RNN). To solve this problem, we divide the regression model into an encoder and decoder structure. In this paper, we first use RNN technique for missing value complementation, then use the fusion model of bidirectional gate recurrent unit (BiGRU) and bidirectional long short-term memory network (BiLSTM) as the encoder to extract. Finally, the group method of data handling (GMDH) model is used as a decoder to obtain stock market prediction results based on the time series data features. A deep heuristic evolutionary regression model (BBGMDH) based on the fusion of BiGRU and BiLSTM is proposed by the above process. We have conducted extensive experiments on four real stock data, and the results show that BBGMDH significantly outperforms existing methods, verifying the effectiveness of the encoding-decoding stepwise regression model in stock price prediction tasks. The reason is that the encoding layer utilizes the powerful time series data processing technology of RNN to effectively extract the hidden features of stock data, and the decoding layer utilizes the GMDH heuristic evolutionary computation method to simulate the "genetic mutation selection evolution" process of an organism for the regression task of stock market prediction, making full use of their complementary properties. We provide a new solution to the regression prediction problem.

关键词： Cognitive computing Gated recurrent neural network group method of data handling Long short-term memory Stock prediction

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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation

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JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING 2025年第5期17卷 2928-2942页

作者： Mkono, Christopher N. Shen, Chuanbo Mulashani, Alvin K. Nyangi, Patrice China Univ Geosci Key Lab Tecton & Petr Resources Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Sch Earth Resources Dept Petr Engn Wuhan 430074 Peoples R China Mbeya Univ Sci & Technol Coll Engn & Technol Dept Geosci & Min Technol Box 131 Mbeya Tanzania Mbeya Univ Sci & Technol Coll Engn & Technol Civil Engn Dept Box 131 Mbeya Tanzania

Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production. Conventional methods relying on empirical correlations and assumptions often result in high costs, time consumption, inaccuracies, and uncertainties. This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. The group method of data handling with differential evolution (GMDH-DE) algorithm was used to predict permeability due to its capability to manage complex, nonlinear relationships between variables, reduced computation time, and parameter optimization through evolutionary algorithms. Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing, the GMDH-DE outperformed the group method of data handling (GMDH) and random forest (RF) in predicting permeability with higher accuracy and lower computation time. The GMDH-DE achieved an R2 of 0.9985, RMSE of 3.157, MAE of 2.366, and ME of 0.001 during training, and for testing, the ME, MAE, RMSE, and R2 were 1.3508, 12.503, 21.3898, and 0.9534, respectively. Additionally, the GMDH-DE demonstrated a 41% reduction in processing time compared to GMDH and RF. The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin, Tanzania, which lacks core data. Shapley additive explanations (SHAP) analysis identified thermal neutron porosity (TNPH), effective porosity (PHIE), and spectral gamma-ray (SGR) as the most critical parameters in permeability prediction. Therefore, the GMDH-DE model offers a novel, efficient, and accurate approach for fast permeability prediction, enhancing hydrocarbon exploration and production. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/ 4.0/).

关键词： Tanzania Permeability Hydrocarbon Differential evolution Shapley additive explanation (SHAP) group method of data handling Well logs

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Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models

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WATER RESOURCES MANAGEMENT 2023年第9期37卷 3563-3584页

作者： Yarahmadi, Mohammad Bahrami Parsaie, Abbas Shafai-Bejestan, Mahmood Heydari, Mostafa Badzanchin, Marzieh Shahid Chamran Univ Ahvaz Fac Water & Environm Engn Ahvaz Iran Shahid Chamran Univ Ahvaz Fac Water & Environm Engn Civil Engn Water & Hydraul Struct Ahvaz Iran

Flow conditions (flow discharge, flow depth, and flow velocity) in natural streams are mainly determined via the flow resistance formula such as Manning's equation. Evaluating the accurate Manning's roughness coefficient (n), especially in rivers with bed form during floods, to obtain more reliable results has always been of interest to scholars. The interaction between the flow and bed form is very complex since the flow conditions control bed forms, and vice versa. The main goal of the present study is to predict n in rivers with bed forms, using soft computing models, including multilayer perceptron artificial neural network (MLPNN), group method of data handling (GMDH), support vector machine (SVM) model, and genetic programming model (GP). To this end, the energy grade line (Sf), flow Froude number (Fr), the relative submergence (y/d(50);y= flow depth and d(50) = bed sediment size), and the bed form dimensionless parameters ( ?/d(50), ?/A, and ?/?;? = bed form height and ?= bed form length) were used as the input variables, and n was used as the output variable. The results showed that all the test models have acceptable accuracy, while the SVM model showed the highest level of accuracy with the coefficient of determination R-2 = 0.99 in the verification stage. The sensitivity analysis of SVM and MLPNN models and the structural analysis of GMDH and GP models indicated that the most important parameters affecting n are Fr, S-f,S- and ?/?.

关键词： Bed form Flow resistance Genetic programming model group method of data handling Support vector machine

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Development of the Adaptive System for Tool Management

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第2期30卷 648-654页

作者： Chen, Jenn-Yih Lin, Yi-Ling Lee, Bean -Yin Natl Formosa Univ Dept Mech & Comp Aided Engn Yunlin 632 Taiwan Natl Formosa Univ Dept Power Mech Engn Yunlin 632 Taiwan Natl Formosa Univ Dept Mech & Comp Aided Engn Smart Machinery & Intelligent Mfg Res Ceneter Yunlin 632 Taiwan

Technological developments in the manufacturing industry have accelerated recently. Although many traditional manufacturing factories had adopted automated production, owing to the inability to control the status of individual tools, it is challenging for manufacturers to accurately track the real time status of production line tools. To bring smart production into existence, a robust database system needs to be ensured. New models of computer numerical control machines are equipped with a built-in tool management system (TMS), but most versions of this system are only able to set a machining time limit or a total number of workpieces limit for each tool to determine tool replacement intervals. Such system could likely cause early tool replacement, resulting in waste and increased tool costs. In order to implement green manufacturing, the prediction of tool wear is essential to reduce wastage of materials. In this study, we developed a TMS that is based on a not only SQL (NoSQL) database, which not only stores several different types of data, but also can store prediction models to make it suitable for the demands of varying enterprise scales. It will be more helpful to ensure processing safety and effectively reduce manufacturing costs.

关键词： group method of data handling NoSQL database polynomial network tool management system

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Prediction of ultimate bearing capacity of concrete filled steel tube stub columns via machine learning

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SOFT COMPUTING 2023年第7-8期28卷 5953页

作者： Deng, Chubing Xue, Xinhua Tao, Li Sichuan Univ Coll Architecture & Environm Chengdu 610065 Sichuan Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Wenzhou Polytech Sch Civil Engn & Architecture Wenzhou 325000 Peoples R China

In this study, three artificial intelligence models, namely group method of data handling, gene expression programming and random forest, are proposed to predict the ultimate bearing capacity of concrete filled steel tube stub columns. A total of 220 data samples collected from the literature was used to construct the three models. Five statistical indices were used to evaluate the performance of the three models and the other five existing design codes and two reference models. Compared with the optimal model among the seven existing models, the coefficient of variation, mean absolute percentage error, root relative squared error and integral of absolute error values of all datasets of the three models (i.e., group method of data handling, gene expression programming and random forest) were decreased by 46.47%, 56.49%, 71.35% and 65.78%;49.82%, 59.49%, 66.88% and 64.51%;79.27%, 84.83%, 78.64% and 83.71%, respectively;while the determination coefficient values of all datasets of the three models were increased by 7.36%, 6.79% and 8.22%, respectively. The results show that the predicted results of the three models agree well with the experimental results, and the random forest model has the best comprehensive performance.

关键词： Ultimate bearing capacity Concrete filled steel tube group method of data handling Gene expression programming Random forest

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Experimental Measurement and Accurate Prediction of Crude Oil Viscosity Utilizing Advanced Intelligent Approaches

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NATURAL RESOURCES RESEARCH 2023年第4期32卷 1657-1682页

作者： Sadi, Maryam Shahrabadi, Abbas Res Inst Petr Ind RIPI Tehran Iran

In this study, experimental measurements and modeling investigations were performed to predict crude oil viscosity under a wide range of conditions. For this purpose, after measuring the viscosity of a considerable number of Iranian crude oils, three advanced intelligent models, including group method of data handling optimized by genetic algorithm, artificial neural network and Gaussian process regression were developed to estimate saturated and under-saturated oil viscosity by considering crude oil API, solution gas oil ratio, bubble point pressure, molecular weight and specific gravity of C12+ fraction, mole percent of C?11com-ponents, temperature and pressure as input parameters. To assess the ability of the proposed intelligent approaches, a wide variety of statistical and graphical error analyses were applied. The results demonstrated that the Gaussian process regression model with average absolute relative errors of 0.18 and 0.07% for saturated and under-saturated oil, respectively, had the best performance in viscosity prediction under different circumstances. Also, the findings of the Leverage technique, which was implemented for detection of suspected data, indicated the reliability of all measured data. Moreover, the results of sensitivity analysis showed that API, pressure and temperature had the greatest effect on oil viscosity in both saturated and under-saturated conditions.

关键词： Crude oil viscosity group method of data handling Artificial neural network Gaussian process regression Genetic algorithm

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A GMDH Approach for Forecast Monthly Rainfall in Southeast of Para

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IEEE LATIN AMERICA TRANSACTIONS 2023年第6期21卷 707-714页

作者： Alves, Elton Rafael Lopes, Marcio Nirlando Gomes Sales, Fabricio Silva Balieiro, Andson Marreiros Leal, Adonis Ferreira Raiol Univ Fed & Sudeste Para Fac Engn Computacao Maraba PA Brazil Ctr Gestor & Operac Sistema Protecao Amazonia Ciencia & Tecnol Belem PA Brazil Engn Computacao Maraba PA Brazil Univ Fed Pernambuco Ctr Informat Recife PE Brazil Univ Fed Para Fac Engn Elect & Biomed Belem PA Brazil

Modeling for rainfall forecasting is widely used in climate forecasting. A high volume of precipitation or scarcity brings serious problems for society. Thus, a methodology for monthly rainfall forecast is presented using group method of data handling (GMDH) and sea surface temperature (SST). The intelligent model gets the mean monthly SST, in predefined and temporally lagged areas, after a variable selection step. For model training, precipitation data from the Climate Prediction Center were used. The methodology was applied in a certain area of the municipality of Maraba, located in the southeastern region of the Para state. The results obtained with GMDH overcame those got by using a conventional multilayer Artificial Neural those got by using a conventional multilayer Artificial Neural Network (RNA), reaching values for regression coefficient (R) and root mean square error (RMSE) equal to 0.96363 and 30.8100 in the test stage, while the RNA-based model got 0.81712 and 68.2607, respectively. Results show the GMHDs effectiveness for the monthly rainfall prediction, constituting an important tool for the planning and assistance to decision makers.

关键词： Predictive models data models data handling Computational modeling Precipitation Nonhomogeneous media IEEE transactions group method of data handling Sea Surface Temperature Multilayer Neural Networks Rainfall Forecast

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Remote sensing, artificial neural networks, and spatial interpolation methods for modelling soil chemical characteristics

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2024年第4期10卷 5063-5078页

作者： Abbasi, Naqash Taj Zarin, Riaz Raziq, Abdur Al-Quraishi, Ayad M. Fadhil Natl Univ Sci & Technol Pakistan Sch Civil & Environm Engn SCEE Islamabad Pakistan Govt Khyber Pakhtunkhwa Elementary & Secondary Educ Dept Chitral 17200 Pakistan Islamia Coll Peshawar Dept Geog Peshawar Pakistan Tishk Int Univ Fac Engn Petr & Min Engn Dept Erbil 44001 Kurdistan Iraq

The increase in global population, rapid urbanization, and continuous soil degradation have disrupted the balance between food demand and supply. Precision agriculture (PA) is an effective solution for overcoming this challenge. The first step in the PA was to determine the physicochemical attributes of the soil. This research aimed to predict soil chemical properties using Geographic Information Systems (GIS), Remote Sensing (RS), and artificial neural networks (ANN). This research explored soil chemical properties using classical statistics, Geostatistics, ANN, and Spatial Interpolation (SI). Multiple Linear Regression (MLR), Ordinary Least Square Regression (OLS), group method of data handling (GMDH) Neural Networks, Inverse Distance Weighting (IDW), and Kriging Interpolation were used to model and predict soil chemical properties. These modeling techniques accurately predicted soil organic matter (SOM) and pH. This study compared and evaluated models based on Landsat-8 and Sentinel-2 to predict soil chemical properties. Among the soil chemical properties, SOM was significantly modeled using MLR, with an accuracy of 0.54 Sentinel and 0.39 Landsat. Landsat band-TIRS2 and Sentinel band-9 significantly defined the SOM. The exploratory regression technique predicts SOM by incorporating more variables, such as Landsat bands 10 and 11 and Sentinel bands 1, 2, 5, 9, and 10. OLS regression predicted SOM with an accuracy of 0.55 and 0.54 for Sentinel and Landsat, respectively. Using GMDH Neural Network, a more accurate model was developed incorporating Sentinel bands 10, 9, and 8 and Landsat bands 5, 10, and 11, with the best accuracy of 0.68%.

关键词： Precision agriculture MLR OLSR group method of data handling ANN Geostatistics

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Classification of Pathologies on Medical Images Using the Algorithm of Random Forest of Optimal-Complexity Trees

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CYBERNETICS AND SYSTEMS ANALYSIS 2023年第2期59卷 346-358页

作者： Babenko, V. Nastenko, Ie. Pavlov, V. Horodetska, O. Dykan, I. Tarasiuk, B. Lazoryshinets, V. Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine Natl Acad Med Sci Ukraine Inst Nucl Med & Radiat Diagnost Kiev Ukraine Natl Acad Med Sci Ukraine Amosov Natl Inst Cardiovasc Surg Kiev Ukraine

The authors propose an approach to the construction of classifiers in the class of Random Forest algorithms. A genetic algorithm is used to determine the optimal combination and composition of ensembles of features in the construction of forest trees. The principles of the group method of data handling are used to optimize the structure of the trees. Optimization of the tree voting procedure in the forest is implemented by the analytic hierarchy process. Examples of the use of the proposed algorithm for the detection of pathologies on medical images are provided, as well as the classification results in comparison with other known analogs.

关键词： pathology classification medical images Random Forest genetic algorithm group method of data handling analytic hierarchy process

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Deep Learning and Neural Network-Based Wind Speed Prediction Model

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2022年第3期30卷 403-425页

作者： Mohammed, Ahmed Salahuddin Mohammed, Amin Salih Kareem, Shahab Wahhab Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Krg Iraq Salahaddin Univ Erbil Dept Software & Informat Engn Krg Iraq Lebanese French Univ Coll Engn & Comp Sci Dept Comp Engn Erbil Krg Iraq Erbil Polytech Univ Erbil Tech Engn Coll Dept Informat Syst Engn Krg Iraq

This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Long Short-Term Memory (LSTM) architecture is a type of artificial Recurrent Neural Network (RNN) used in deep learning is the first method plots the predicting Wind Speed based on the dataset and predicts the future spread. A dataset from a real-time weather station is used in the implementation model. The dataset consists of information from the weather station implements of the recurrent neural network model that plots the past spread and predicts the future stretch of the weather. The performance of the recurrent neural network model is presented and compared with Adaline neural network, Autoregressive Neural Network (NAR), and group method of data handling (GMDH). The NAR used three hidden layers. The performance of the model is analyzed by presenting the Wind Speeds of Erbil city. The dataset consists of the Wind Speed of (1992-2020) years, and each year consist of twelve months (from January to December).

关键词： Wind speed prediction Autoregressive Neural Network group method of data handling adaline neural network

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