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Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern

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AGRICULTURAL WATER MANAGEMENT 2022年第0期270卷

作者： Jamei, Mehdi Maroufpoor, Saman Aminpour, Younes Karbasi, Masoud Malik, Anurag Karimi, Bakhtiar Shahid Chamran Univ Ahvaz Fac Engn Shohadaye Hoveizeh Campus Technol Dashte Azadegan Iran Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Minist Energy Water Res Inst Dept Hydraul Hydroenvironm Engn POB 16765-313 Tehran *** Iran Univ Zanjan Water Engn Dept Fac Agr Zanjan Iran Punjab Agr Univ Reg Res Stn Bathinda Punjab India Univ Kurdistan Dept Water Sci & Engn Fac Agr Sanandaj Iran

One of the critical factors in the optimal design of drip-fertigation systems is determining the distribution of nitrate in the soil. Handling such a complex non-linear process is challenging. The main goal of this study is to develop an accurate hybrid Boruta Random Forest (BRF)-whale optimization algorithm (WOA) integrated with an Artificial Neural Network (ANN) to estimate the nitrate concentration (NO 3) in the distribution system. In addition to applying ANN and support vector regression (SVR) methods, various training algorithms and kernel functions are used as standalone validation models to evaluate the robustness of the WOA-ANN model for nitrate pattern estimation. The algorithm uses 11 variables extracted from the experimental study, which are optimally arranged in five input combinations employing the BRF Feature Selection (FS) and regression analyses. The statistical and diagnostic analyses showed that the BRF-FS is the best approach to optimize the WOA-ANN model. The proposed approach provided the best metrics (i.e. R=0.962, RMSE=0.029 mg/L, MAE=0.024, and U95%=0.056) and improved the ANN's accuracy by 30%. It also outperformed the ANN (R=0.913 and RMSE=0.042 mg/L) and SVR (R=0.901 and RMSE=0.045 mg/L) when applied to estimate the NO 3 values. An external validation analysis showed the robustness of all applied machine learning models. Moreover, the significant scoring assessment also showed that when using the BRF-FS approach, the initial nitrate concentration in soil (N0) and nitrate concentration in irrigation water (F NO 3) had the most influence on the estimation of nitrate pattern, respectively.

关键词： Artificial neural network Drip irrigation Boruta Random forest Nitrate pattern External validation whale optimization algorithm

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Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Cao, Wensi Zhou, Junlong Xu, Qiang Zhen, Juan Huang, Xiaobo North China Univ Water Resources & Elect Power Sch Elect Power Zhengzhou Peoples R China

Aiming to solve the problem that photovoltaic power generation is always accompanied by uncertainty and the short-term prediction accuracy of photovoltaic power (PV) is not high, this paper proposes a method for short-term photovoltaic power forecasting (PPF) and uncertainty analysis using the fuzzy-c-means (FCM), whale optimization algorithm (WOA), bi-directional long short-term memory (BILSTM), and no-parametric kernel density estimation (NPKDE). First, the principal component analysis (PCA) is used to reduce the dimensionality of the daily feature vector, and then the FCM is used to divide the weather into four categories: sunny, cloudy, rainy, and extreme weather. Second, the WOA algorithm is used to train the hyperparameters of BILSTM, and finally, the optimized hyperparameters were used to construct a WOA-BILSTM prediction model to train the four types of weather samples after FCM clustering. The NPKDE method was used to calculate the probability density distribution of PV prediction errors and confidence intervals for PPF. The RMSEs of the FCM-WOA-BILSTM model are 2.46%, 4.89%, and 1.14% for sunny, cloudy, and rainy weather types, respectively. The simulation results of the calculation example show that compared with the BP, LSTM, GRU, PSO-BILSTM, and FCM-PSO-BP models, the proposed FCM-WOA-BILSTM model has higher prediction accuracy under various weather types, which verifies the effectiveness of the method. Moreover, the NPKDE method can accurately describe the probability density distribution of forecast errors.

关键词： fuzzy c-means clustering whale optimization algorithm BiLSTM photovoltaic power forecasting uncertainty analysis

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Non-destructive determination of beef freshness based on colorimetric sensor array and multivariate analysis

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SENSORS AND ACTUATORS B-CHEMICAL 2022年 369卷

作者： Xu, Weidong He, Yingchao Li, Jiaheng Deng, Yong Xu, Enbo Feng, Jinsong Ding, Tian Liu, Donghong Wang, Wenjun Zhejiang Univ Coll Biosyst Engn & Food Sci Ningbo Zhejiang Peoples R China Fuli Inst Food Sci Zhejiang R&D Ctr Food Technol & Equipment Natl Local Joint Engn Lab Intelligent Food Technol Zhejiang Key Lab Agrofood Proc Hangzhou 310058 Peoples R China Zhejiang Univ Ningbo Res Inst Ningbo 315100 Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China

Rapid and reliable detection of beef freshness is essential for diet safety and resource-saving. In this study, the colorimetric sensor array (CSA) integrated with whale optimization algorithm (WOA) and back-propagation neural network (BPNN) had been innovatively developed for the quantitative determination of beef freshness, regarding the total volatile basic nitrogen (TVB-N) and total viable counts (TVC) contents. Firstly, the CSA comprising twelve color-sensitive dyes was designed to acquire the scent fingerprints (RGB triplets) during beef storage. Secondly, WOA-BPNN was used to optimize the color components combination from the preprocessed CSA to acquire the dominant color components. Finally, the BPNN models were constructed based on the optimized color components, with the BPNN topology of 8-12-1 for TVB-N prediction and 6-6-1 for TVC prediction. The results revealed that the BPNN model combined with optimized color components could be utilized to quantitatively determine beef freshness. The overall results demonstrated that the CSA integrated with appropriate multivariable analysis methods could realize rapid and reliable quantitative determination of beef freshness. Furthermore, the WOA-BPNN could effectively extract the dominant color components, which was beneficial for improving detection accuracy and robustness of the BPNN, as well as time-and cost-saving.

关键词： Food safety Beef freshness Colorimetric sensors array whale optimization algorithm Quantitative detection

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Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2021年第2期19卷 2050074-2050074页

作者： Prakaash, A. S. Sivakumar, K. Sathyabama Inst Sci & Technol Dept Math Chennai 600119 Tamil Nadu India

Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of whale optimization algorithm (WOA) and Cat Swarm optimization (CSO), which is called the whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis.

关键词： Data prediction application-oriented hybrid machine learning algorithms Neural Network Fuzzy Regression model whale Updated Seek Mode-based Cat Swarm optimization data acquisition feature extraction data normalization Recurrent Neural Network whale optimization algorithm

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Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 223卷 107044-107044页

作者： Seyyedabbasi, Amir Aliyev, Royal Kiani, Farzad Gulle, Murat Ugur Basyildiz, Hasan Shah, Mohammed Ahmed Beykent Univ Fac Engn & Architecture Dept Comp Engn Istanbul Turkey Istanbul Aydin Univ Fac Engn Dept Comp Engn Istanbul Turkey Istanbul Arel Univ Fac Engn & Architecture Dept Comp Engn Istanbul Turkey Istanbul Ayvansaray Univ Fac Engn Dept Comp Engn Istanbul Turkey Istinye Univ Fac Engn Software Engn Dept Istanbul Turkey

This paper introduces three hybrid algorithms that help in solving global optimization problems using reinforcement learning along with metaheuristic methods. Using the algorithms presented, the search agents try to find a global optimum avoiding the local optima trap. Compared to the classical metaheuristic approaches, the proposed algorithms display higher success in finding new areas as well as exhibiting a more balanced performance while in the exploration and exploitation phases. The algorithms employ reinforcement agents to select an environment based on predefined actions and tasks. A reward and penalty system is used by the agents to discover the environment, done dynamically without following a predetermined model or method. The study makes use of Q-Learning method in all three metaheuristic algorithms, so-called RLI-GWO, RLEx-GWO, and RLWOA algorithms, so as to check and control exploration and exploitation with Q-Table. The Q-Table values guide the search agents of the metaheuristic algorithms to select between the exploration and exploitation phases. A control mechanism is used to get the reward and penalty values for each action. The algorithms presented in this paper are simulated over 30 benchmark functions from CEC 2014, 2015 and the results obtained are compared with well-known metaheuristic and hybrid algorithms (GWO, RLGWO, I-GWO, Ex-GWO, and WOA). The proposed methods have also been applied to the inverse kinematics of the robot arms problem. The results of the used algorithms demonstrate that RLWOA provides better solutions for relevant problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Metaheuristic algorithm Reinforcement learning algorithm Grey wolf optimization algorithm whale optimization algorithm Q-learning

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Analysis of Multi-Phase Flow Through Porous Media for Imbibition Phenomena by Using the LeNN-WOA-NM algorithm

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IEEE ACCESS 2020年 8卷 196425-196458页

作者： Khan, Naveed Ahmad Sulaiman, Muhammad Aljohani, Abdulah Jeza Kumam, Poom Alrabaiah, Hussam Abdul Wali Khan Univ Mardan Dept Math Mardan 23200 Pakistan King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst Jeddah 21589 Saudi Arabia King Mongkuts Univ Technol Thonburi KMUTT Ctr Excellence Theoret & Computat Sci TaCS CoE Bangkok 10140 Thailand King Mongkuts Univ Technol Thonburi KMUTT Dept Math Fac Sci Bangkok 10140 Thailand China Med Univ China Med Univ Hosp Dept Med Res Taichung 40402 Taiwan Al Ain Univ Coll Engn Al Ain U Arab Emirates Tafila Tech Univ Dept Math Tafila 66110 Jordan

The flow of fluids in multi-phase porous media results due to many interesting natural phenomena. The counter-current water imbibition phenomena, that occur during oil extraction through a cylindrical well is an interesting problem in petroleum engineering. During the secondary oil recovery process, water is injected into a porous media having heterogenous and homogenous characteristics. Due to the difference in viscosities of fluids in oil wells, the counter-current imbibition phenomenon occurs. At that moment, the imbibition equation V-i= -V-n is satisfied by the viscosities of oil and water. In this article, we have analyzed the governing mathematical model of the imbibition phenomenon occurring during the secondary oil recovery process. A new soft computing algorithm is designed and adapted to analyze the mathematical model of dual-phase flow in detail. Weighted Legendre polynomials based artificial neural networks are hybridized with an efficient global optimizer the whale optimization algorithm (WOA) and a local optimizer the Nelder-Mead algorithm. It is established, that our algorithm LeNN-WOA-NM is efficient and reliable in calculating high-quality solutions in less time. We have compared our experimental outcome with state-of-the-art results. The quality of our solutions is judged based on values of absolute errors, MAD, TIC, and ENSE. It is obvious that LeNN-WOA-NM algorithm can solve real application problems efficiently and accurately.

关键词： Oils Media Mathematical model Neural networks optimization Reservoirs Rocks Counter-current imbibition phenomena homogeneous porous media heterogeneous porous media capillary pressure soft computing algorithm weighted Legendre neural networks Nelder-Mead algorithm whale optimization algorithm

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Multi-objective evolutionary method for multi-area dynamic emission/economic dispatch considering energy storage and renewable energy units

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Heliyon 2024年第18期10卷 e37476页

作者： Shahri, Hassan Yaghubi Hosseini, Seyed Ali Pourhossein, Javad Department of Electrical Engineering Islamic Azad University Gonabad Branch Gonabad Iran

Reducing thermal unit operating costs and emissions is the goal of the multi-objective issue known as multi-area economic/emission dispatch (MAEED) in smart grids. Using renewable energy (RE) have significantly lowered greenhouse gas emissions and ensured the sustainability of the environment. With regard to constraints such as prohibited operating zones (POZs), valve point effect (VPE), transmission losses in the network, ramp restrictions, tie-line capacity, this study aims to minimize operating costs and emission objectives by solving the multi-area dynamic economic/emission dispatch (MADEED) problem in the presence of RE units and energy storage (ES) systems. The conventional economic dispatch (ED) optimization approach has the following shortcomings: It is only designed to solve the single-objective optimization problem with a cost objective, in addition, it also does not have high calculation accuracy and speed. Therefore, to address this multi-objective MADEED problem with non-linear constraints, this paper introduces hybrid particle swarm optimization (PSO)-whale optimization algorithms (WOA). The reason for combining two algorithms is to use the advantages of both algorithms in solving the desired optimization problem. The introduced method is tested in two separate scenarios on a test network of 10 generators. Using the suggested hybrid methodology in this study, the MADED and MADEED problems are resolved and contrasted with other evolutionary techniques, such as original WOA, and PSO methods. Examining the results of the proposed method shows the efficiency and better performance of the proposed method compared to other methods. Finally, the results obtained by simulations indicate that integrating the necessary system restrictions gives the system legitimacy and produces dependable output. With regard to the results obtained from the introduced approach, the value of the overall cost function has clearly decreased by about 3 % compared to other methods.

关键词： Energy storage systems Multi-area dynamic economic/emission dispatch Particle swarm algorithm Renewable energy units whale optimization algorithm

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Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, whale Optimisation algorithm, and Elman Network

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ENERGIES 2022年第9期15卷 3055-3055页

作者： Zhu, Anfeng Zhao, Qiancheng Wang, Xian Zhou, Ling Hunan Univ Sci & Technol Engn Res Ctr Hunan Prov Min & Utilizat Wind Turbi Xiangtan 411201 Peoples R China

Accurate wind power forecasting helps relieve the regulation pressure of a power system, which is of great significance to the power system's operation. However, achieving satisfactory results in wind power forecasting is highly challenging due to the random volatility characteristics of wind power sequences. This study proposes a novel ultra-short-term wind power combined prediction method based on complementary ensemble empirical mode decomposition, the whale optimization algorithm (WOA), and the Elman neural network model. The model can not only solve the phenomenon of easy modal mixing in decomposition but also avoid the problems of reconstruction error and low efficiency in the decomposition process. Furthermore, a new metaheuristic algorithm, WOA, was introduced to optimize the model and improve the accuracy of wind power prediction. Considering a wind farm as an example, several wind turbines were selected to simulate and analyse wind power by using the established prediction model, and the experimental results suggest that the proposed method has a higher prediction accuracy of ultra-short-term wind power than other prediction models.

关键词： ultra-short-term wind power forecast complementary ensemble empirical mode decomposition whale optimization algorithm combination model

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A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine

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ENERGY 2022年第PartC期238卷 122073-122073页

作者： Gao, Zhikun Yu, Junqi Zhao, Anjun Hu, Qun Yang, Siyuan Xian Univ Architecture & Technol Xian Shaanxi Peoples R China

Air conditioning system is extensively used in large commercial buildings. The fast and accurate building cooling load forecasting is the basis for improving the operation efficiency of the air conditioning system, which is conducive to implement the effective management of the air conditioning system. Therefore, a hybrid prediction model based on random forest-improvement parallel whale optimizing-extreme learning machine neural network (RF-IPWOA-ELM) is proposed to predict the cooling load of large commercial buildings. First, the influence of different parameters on the cooling load is analyzed, and the random forest (RF) method is used to extract the parameters with high degree of influence as the input variables of prediction model. Then, the extreme learning machine (ELM) optimized by the improved parallel whale optimization algorithm (IPWOA) is established to predict. Finally, a simulation experiment is carried out using measured data of two large commercial buildings in north of China. The experimental results show that the root mean square error (RMSE) and mean average percentage error (MAPE) of RFIPWOA-ELM predicting the cooling load for these two buildings are 2.8735, 0.2% and 4.7721, 0.45%, respectively. Compared with the other prediction model, the RMSE and MAPE of this model are reduced by 66.17%-90.62%, 81.48%-95.79% and 71.91%-84.40%, 74.14%-86.15%, respectively, which has higher prediction accuracy. Simultaneously, for different prediction models, RF-IPWOA-ELM has a shorter prediction time, which presents superiority in time complexity. And when there are few training samples, RF-IPWOA-ELM can still effectively predict the cooling load in different months, indicating that it possesses strong generalization ability. Therefore, the proposed hybrid model can be used as a reliable tool for cooling load prediction in the energy conservation of air conditioning system and energy management. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Large commercial building cooling load Random forest whale optimization algorithm Extreme learning machine Prediction accuracy

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A Qualitative and Quantitative Analysis Strategy for Continuous Turbulent Gas Mixture Monitoring

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CHEMOSENSORS 2022年第12期10卷 499-499页

作者： Chen, Yinsheng Xia, Wanyu Chen, Deyun Zhang, Tianyu Song, Tingting Zhao, Wenjie Song, Kai Harbin Univ Sci & Technol Postdoctoral Res Stn Comp Sci & Technol Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Measurement & Commun Engn Harbin 150080 Peoples R China Harbin Inst Technol Sch Elect Engn & Automation Harbin 150080 Peoples R China

Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the atmosphere on the quantitative and qualitative analytical performance of the electronic nose. In light of this, this paper presents a quantitative and qualitative analysis strategy for gas mixture monitoring. This strategy adopts baseline manipulation of the raw sensor data to reduce drift interference, and then performs feature extraction on the multidimensional response signals of the MOS gas sensor array using principal component analysis (PCA). In order to improve gas mixture recognition accuracy, the whale optimization algorithm (WOA) is used to optimize the network structure of the long short-term memory (LSTM) model for turbulent gas mixture composition recognition. The least squares support vector machine (LSSVM) algorithm is adopted to implement turbulent gas mixture concentration prediction. This paper focuses on two aspects of hyper-parameter optimization for the development of an LSSVM with particle swarm optimization (PSO) and for improved training sample selection for the LSSVM which should subsequently increase the accuracy of concentration estimation. The effectiveness of the proposed strategy is evaluated with a dataset from a chemical sensor array exposed to turbulent gas mixtures. Experimental results revealed that the proposed strategy for turbulent gas mixtures has satisfactory outcomes for both qualitative gas composition recognition and quantitative gas concentration prediction.

关键词： electronic nose turbulence gas mixture detection long short-term memory least squares support vector machine whale optimization algorithm particle swarm optimization

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