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Modeling Monthly Evaporation in Different Climates Using ANN-coot Hybrid algorithm

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2024年第5期48卷 3495页

作者： Rajabi, Farnaz Alempour Ahmadi, Ehsan Ibrahim, Osama Ragab Al Maghawry, Salma Mohamed Aghamajidi, Rozbeh Univ Tabriz Water Engn Tabriz Iran Sohar Univ Fac Engn Sohar Oman Islamic Azad Univ Sepidan Branches Ardakan Iran

Accurate monitoring and estimation of evaporation of a region is significant for proper planning and management of water resources management. Hence, the prediction of future alteration in evaporation is so critical in the development of irrigation systems, therefore;the current study aims to develop a hybrid artificial neural network that is coupled with coot algorithm (ANN-coot) to prediction the monthly evaporation in three stations in Iran. Utilizing different input combinations of monthly temperature (Tmin and Tmax), sunshine hours (SSH), wind speed (WS) and relative humidity (RH) datasets, the performance of the proposed ANN-coot hybrid model results was compared with standalone ANN model. Statistical performances were calculated and comparison plots were made in the training and testing phases to find the most accurate model for evaporation prediction. Compared with the results of different input combinations, the hybrid ANN-coot4 model at all three stations were found superior with input combinations of Tmin, Tmax, SSH, RH, WS. The testing results revealed that the lowest root mean square error (RMSE) (18.43 mm, 19.36 mm and 8.19 mm) and highest coefficient of correlation (R) (0.99, 0.97 and 0.99) and the highest Nash-Sutcliffe efficiency coefficient (NS) (0.98, 0.93 and 0.99) attained by the ANN-coot4 hybrid model (relative to other ANN and ANN-coot models) tested for three selected stations in Shiraz, Kish and Gorgan sites. In respect to the predictive efficiency, the developed ANN-coot hybrid model, improved the modeling performance at extreme points, which outperforms conventional ANN model, indicating its capability in the prediction of monthly evaporation.

关键词： Artificial neural network coot optimization algorithm Evaporation Iran Modeling

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Seismic structural control using magneto-rheological dampers: A decentralized interval type-2 fractional-order fuzzy PID controller optimized based on energy concepts

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ISA TRANSACTIONS 2023年 137卷 288-302页

作者： Zamani, Abbas-Ali Etedali, Sadegh Tech & Vocat Univ TVU Dept Elect Engn Tehran Iran Birjand Univ Technol Dept Civil Engn POB 97175-569 Birjand Iran

In this paper, a combination of the interval type-2 fuzzy logic controller (IT2FLC) with the fractional -order proportional-integral-derivative (FOPID) controller, namely optimal interval type-2 fractional -order fuzzy proportional-integral-derivative controller (OIT2FOFPIDC), is developed for enhancing the seismic performance and robustness in seismic structural control applications. Based on the energy concepts, a decentralized framework of the OIT2FOFPIDC is proposed for easy and simple implementation in structures during earthquakes. For this purpose, a coot optimization algorithm (COA), as a powerful optimization algorithm, is also applied to adjust the membership functions (MFs), scaling factors, and the main controller parameters. Three controllers, namely optimal type-1 fuzzy proportional-integral-derivative controller (OT1FPIDC), optimal interval type-2 fuzzy proportional- integral-derivative controller (OIT2FPIDC), and optimal proportional-integral-derivative controller (OPIDC), are also proposed for comparison purposes. The seismic performances of the suggested controllers are examined with the evaluation of nine seismic performance indices and different ground accelerations in a 6-story smart structure equipped with two dampers. The robustness of the four controllers in the presence of the stiffness uncertainties is also compared in this study. On average, a reduction of 25.0%, 18.8%, and 18.5% in peak displacement, inter-story drift, and acceleration of stories is obtained for the OIT2FOFPIDC over the OT1FPIDC, respectively. Similarly, these reductions in comparison with the OIT2FPIDC are 16.3%, 13.3%, and 12.0%. Also, these reductions, in comparison with the OPIDC, are 33.3%, 27.8%, and 25.8%. Furthermore, simulation results show that the OIT2FOFPIDC is more robust than the other proposed controllers against uncertainties due to structural stiffness. & COPY;2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Structural control Magneto-rheological damper Interval type-2 fractional-order fuzzy PID & nbsp controller Fractional-order PID control coot optimization algorithm Energy concepts

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A novel parameter identification strategy based on coot optimizer applied to a three-diode model of triple cation perovskite solar cells

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NEURAL COMPUTING & APPLICATIONS 2023年第14期35卷 10197-10219页

作者： Rezk, Hegazy Elsenety, Mohamed M. M. Ferahtia, Seydali Falaras, Polycarpos Zaky, Alaa A. A. Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Wadi Alddawasir Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya Egypt Al Azhar Univ Fac Sci Chem Dept Cairo 11884 Egypt Univ Msila Dept Elect Engn Msila Algeria Natl Ctr Sci Res Demokritos Inst Nanosci & Nanotechnol 15341 Agia Paraskevi Attikis Athens 15341 Greece Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafr Al Sheikh 33511 Egypt

The remarkable optoelectronic characteristics of hybrid metal halide perovskite semiconductors, such as high defect tolerance, extended carrier lifetime and diffusion length, and adjustable optical bandgap, have garnered much interest in the last decade. Therefore, this paper considers the experimental and mathematical modeling of triple-cation perovskite solar cells (PSCs) with two different device structures. It is challenging to construct a reliable mathematical model of triple-cation perovskite solar cells based on the three-diode model due to its complex nature. This is related to the perovskite materials' dynamics, nonlinearity, and sensitivity. This paper proposes a novel method incorporating a recent metaheuristic algorithm named coot optimizer to estimate the optimal parameters of the three-diode equivalent circuit of triple-cation perovskite solar cells. The key idea is to use the swarm intelligence-based coot to optimally achieve the PV panel's optimal parameters. The identification method benefits from the exploration and exploitation abilities of the coot algorithm to obtain its parameters effortlessly and precisely. Two experiments are conducted in this work;the first is measured I-V datasets for a triple-cation perovskite (TC-per) solar cell at standard conditions. The second consists of the measured I-V datasets for a triple-cation modified perovskite (TCM-per) perovskite solar cell. During the optimization process, the nine unknown parameters of the three-diode model (TDM) are used as decision variables. The objective function to be minimized is the root-mean-square error (RMSE) between the measured and estimated data. An extensive comparative study is presented with other optimizers of the whale optimization algorithm (WOA), seagull optimization algorithm (SOA), sine cosine algorithm (SCA), ant lion optimization (ALO), and dragonfly algorithm (DA). Furthermore, statistical analysis of ANOVA is performed. The obtained results confirm the superiority

关键词： coot optimization algorithm Perovskite solar cell Triple-diode model

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Hybrid binary coot algorithm with simulated annealing for feature selection in high-dimensional microarray data

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NEURAL COMPUTING & APPLICATIONS 2023年第1期35卷 353-374页

作者： Pashaei, Elnaz Pashaei, Elham Istanbul Aydin Univ Dept Software Engn Istanbul Turkey Istanbul Gelisim Univ Dept Comp Engn Istanbul Turkey

Microarray analysis of gene expression can help with disease and cancer diagnosis and prognosis. Identification of gene biomarkers is one of the most difficult issues in microarray cancer classification due to the diverse complexity of different cancers and the high dimensionality of data. In this paper, a new gene selection strategy based on the binary coot (Bcoot) optimization algorithm is proposed. The coot algorithm is a newly proposed optimizer whose ability to solve gene selection problems has yet to be explored. Three binary variants of the coot algorithm are suggested to search for the targeting genes to classify cancer and diseases. The proposed algorithms are Bcoot, Bcoot-C, and Bcoot-CSA. In the first method, a hyperbolic tangent transfer function is used to convert the continuous version of the coot algorithm to binary. In the second approach, a crossover operator (C) is used to improve the global search of the Bcoot algorithm. In the third method, Bcoot-C is hybridized with simulated annealing (SA) to boost the algorithm's local exploitation capabilities in order to find robust and stable informative genes. Furthermore, minimum redundancy maximum relevance (mRMR) is used as a prefiltering technique to eliminate redundant genes. The proposed algorithms are tested on ten well-known microarray datasets and then compared to other powerful optimization algorithms, and recent state-of-the-art gene selection techniques. The experimental results demonstrate that the Bcoot-CSA approach surpasses Bcoot and Bcoot-C and outperforms other techniques in terms of prediction accuracy and the number of selected genes in most cases.

关键词： Cancer classification Feature selection Gene selection coot optimization algorithm

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Broad learning system based ensemble deep model

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SOFT COMPUTING 2022年第15期26卷 7029-7041页

作者： Zhang, Chenglong Ding, Shifei Guo, Lili Zhang, Jian China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Peoples Republ China Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China

Broad learning system (BLS) demonstrates a novel structure of neural networks based on random vector functional link network (RVFL), which has a faster modeling speed, better generalization ability, higher regression accuracy for solving regression tasks. To improve the feature representation capacity of BLS and guarantee the training efficiency, a novel ensemble deep model based on broad learning system is proposed, named as EDBLS. Unlike other deep or ensemble models of BLS, the results of EDBLS can be calculated by training the model only once. Meanwhile, the performance of BLS mainly depends on its structure, it is the same as EDBLS, we need to select network parameters for different real-world problems. As the lower efficiency of grid search, it is no longer suitable for EDBLS with more parameters. Therefore, coot optimization algorithm (coot) is applied to determine the network structure of EDBLS for regression problems, it is important to note that the coot is only a tool for determining the structure of EDBLS. Finally, the experimental results on MNIST, NORB and some regression benchmark datasets show that EDBLS has a better classification and regression performance, coot is beneficial to select the network parameters of EDBLS for regression.

关键词： Broad learning system Ensemble deep learning coot optimization algorithm Neural network optimization

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Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

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Computer Systems Science & Engineering 2024年第3期48卷 627-643页

作者： Maha Farouk Sabir Mahmoud Ragab Adil O.Khadidos Khaled H.Alyoubi Alaa O.Khadidos Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia 3Department of MathematicsFaculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz UniversityJeddahSaudi Arabia

Big data and information and communication technologies can be important to the effectiveness of smart *** on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability ***-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make *** to air pollution occurs a main environmental problem in smart city *** effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution ***,this article develops a new coot optimization algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart *** projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city *** achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input *** air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL *** simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

关键词： Sustainability smart cities air pollution prediction ensemble learning coot optimization algorithm

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Predictive Modeling of Student Performance Through Classification with Gaussian Process Models

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第6期15卷 1214-1227页

作者： Zhang, Xiaowei Yue, Junlin Luoyang Inst Sci & Technol Dept Educ Sci & Mus Luoyang 471023 Peoples R China Luoyang Normal Univ Dept Foreign Language Teaching & Res Luoyang 471934 Peoples R China

In the contemporary educational landscape, proactively engaging in predictive assessment has become indispensable for academic institutions. This strategic imperative involves evaluating students based on their innate aptitude, preparing them adequately for impending examinations, and fostering both academic and personal development. Alarming statistics underscore a notable failure rate among students, particularly in courses. This article aims to employ predictive methodologies to assess and anticipate the academic performance of students in language courses during the G2 and G3 academic exams. The study utilizes the Gaussian Process Classification (GPC) model in conjunction with two optimization algorithms, the Population-based Vortex Search algorithm (PVS) and the coot optimization algorithm (COA), resulting in the creation of GPPV and GPCO models. The classification of students into distinct performance categories based on their language scores reveals that the GPPV model exhibits the highest concordance between measured and predicted outcomes. In G2, the GPPV model demonstrated the notable 51.1% correct categorization of students as Poor, followed by 25.57% in the Acceptable category, 14.17% in the good category, and 7.7% in the Excellent category. This performance surpasses both the optimized GPCO model and the singular GPC model, signifying its efficacy in predictive analysis and educational advancement.

关键词： Academic performance language hybrid algorithms Gaussian Process Classification population-based Vortex Search algorithm coot optimization algorithm

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A new hybrid optimization prediction model for PM2.5 concentration considering other air pollutants and meteorological conditions

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CHEMOSPHERE 2022年第Part3期307卷 135798页

作者： Yang, Hong Liu, Zehang Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

With the development of economy, the problem of air pollution has become increasingly serious. As an important detection index of air pollutants, how to accurately and effectively predict PM2.5 concentration is a significant issue related to human health and development. In this paper, a new hybrid optimization prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEM-DAN), variational mode decomposition optimized by coot optimization algorithm (coot-VMD), and least square support vector machine (LSSVM) optimized by the JAYA optimization algorithm (JAYA-LSSVM), named CEEMDAN-coot-VMD-JAYA-LSSVM, is proposed. To avoid artificially setting the limits of the decomposition layer and penalty factor of VMD parameters, an improved VMD by coot optimization algorithm, named coot-VMD, is proposed. First, the original sequence of PM2.5 concentration is decomposed by CEEMDAN. Second, the high complexity component with low prediction accuracy after once decomposition is decomposed by coot-VMD. Third, a prediction model of optimized LSSVM by JAYA optimization algorithm, named JAYA-LSSVM, is proposed. JAYA-LSSVM is used to predict all components considering other air pollutants such as PM10, SO2, NO2, CO, and O3 and meteorological conditions such as wind speed, temperature, sunlight, relative humidity and average air pressure. Finally, all predicted values are reconstructed to obtain the final prediction results. PM2.5 concentration from April 1, 2016 to March 29, 2021 in Xi'an and Shenyang is used as the experimental data to verify the proposed model. The results of experiment in Xi'an show that the RMSE, MAE, MAPE and R(2 )are 2.843, 1.8344, 2.94%, and 0.99525 respectively. The results of experiment in Shenyang show that the RMSE, MAE, MAPE and R2 are 2.2714, 1.673, 3.13%, and 0.99573 respectively. Compared to other single and hybrid models, the proposed model can accurately predict PM2.5 concentration. Diebold Mar

关键词： PM2.5 concentration prediction Secondary decomposition coot optimization algorithm Least square support vector machine JAYA optimization algorithm

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