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Adegboye OR, Feda AK, Agyekum EB, Mbasso WF, Kamel S. Towards greener futures: SVR-based CO 2 prediction model boosted by SCMSSA algorithm. Heliyon 2024; 10:e31766. [PMID: 38845912 PMCID: PMC11154620 DOI: 10.1016/j.heliyon.2024.e31766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic perturbation and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance the convergence accuracy and speed of the optimization algorithm. The study assesses the SCMSSA algorithm's performance against other optimization algorithms using six test functions to show the efficacy of the enhancement strategies. Furthermore, its efficacy in improving Support Vector Regression (SVR) models for CO2 prediction is assessed. The results reveal that the SVR-SCMSSA hybrid model surpasses other hybrid models and standard SVR in terms of training and prediction accuracy by obtaining 95 % accuracy. Its swift convergence, precision, and resistance to local optima position make it an excellent choice for addressing complex problems such as CO2 prediction, with critical implications for sustainability efforts. Moreover, feature importance analysis by SVR-SCMSSA offers valuable insights into the key contributors to CO2 prediction in the dataset, emphasizing the significance and impact of factors such as fossil fuel, Biomass, and Wood as major contributors to CO2 emission. The research suggests the adoption of the SVR-SCMSSA hybrid model for more accurate and reliable CO2 prediction to researchers and policymakers, which is essential for environmental sustainability and climate change mitigation.
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Affiliation(s)
| | - Afi Kekeli Feda
- Advanced Research Centre, European University of Lefke, Mersin-10, Turkey
| | - Ephraim Bonah Agyekum
- Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, 19 Mira Street, Ekaterinburg, 620002, Russia
| | - Wulfran Fendzi Mbasso
- Laboratory of Technology and Applied Sciences, University Institute of Technology, University of Douala, PO Box: 8698 Douala, Cameroon
| | - Salah Kamel
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
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2
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Van Thieu N, Nguyen NH, Sherif M, El-Shafie A, Ahmed AN. Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction. Sci Rep 2024; 14:13597. [PMID: 38866871 PMCID: PMC11169458 DOI: 10.1038/s41598-024-63908-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge-Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson's correlation index (R) of 0.9374, and a Nash-Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.
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Affiliation(s)
- Nguyen Van Thieu
- Faculty of Computer Science, PHENIKAA University, Yen Nghia, Ha Dong, Hanoi, 12116, Viet Nam.
| | - Ngoc Hung Nguyen
- Artificial Intelligence Independent Research Group, Hanoi, Viet Nam
| | - Mohsen Sherif
- Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
- National Water and Energy Center, United Arab Emirate University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
- National Water and Energy Center, United Arab Emirate University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Ali Najah Ahmed
- Department of Engineering, School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Selangor Darul Ehsan, Malaysia
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Mijwel AAS, Ahmed AN, Afan HA, Alayan HM, Sherif M, Elshafie A. Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes. Sci Rep 2023; 13:18260. [PMID: 37880280 PMCID: PMC10600184 DOI: 10.1038/s41598-023-45032-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/14/2023] [Indexed: 10/27/2023] Open
Abstract
This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550 °C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 × 10-4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater.
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Affiliation(s)
- Abd-Alkhaliq Salih Mijwel
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Ali Najah Ahmed
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia.
- Institute of Energy Infrastructure, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia.
| | | | - Haiyam Mohammed Alayan
- Chemical Engineering Department, University of Technology, Al-Sinaa Street 52, Baghdad, 10066, Iraq.
| | - Mohsen Sherif
- National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
- Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, 15551, Al Ain, United Arab Emirates
| | - Ahmed Elshafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
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Lai V, Huang YF, Koo CH, Ahmed AN, Sherif M, El-Shafie A. Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique. Sci Rep 2023; 13:6966. [PMID: 37117263 PMCID: PMC10147929 DOI: 10.1038/s41598-023-33801-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023] Open
Abstract
To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09.
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Affiliation(s)
- V Lai
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia
| | - Y F Huang
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia.
| | - C H Koo
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure and Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Mohsen Sherif
- Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
- National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
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Turgut OE, Turgut MS, Kırtepe E. A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08481-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Ikram RMA, Hazarika BB, Gupta D, Heddam S, Kisi O. Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08163-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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An Improved Sea Lion Optimization for Workload Elasticity Prediction with Neural Networks. INT J COMPUT INT SYS 2022. [PMCID: PMC9617542 DOI: 10.1007/s44196-022-00156-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
The work in this paper presents a study into nature-inspired optimization applied to workload elasticity prediction using neural networks. Currently, the trend is for proactive decision support in increasing or decreasing the available resource in cloud computing. The aim is to avoid overprovision leading to resource waste and to avoid resource under-provisioning. The combination of optimization and neural networks has potential for the performance, accuracy, and stability of the prediction solution. In this context, we initially proposed an improved variant of sea lion optimization (ISLO) to boost the efficiency of the original in solving optimization problems. The designed optimization results are validated against eight well-known metaheuristic algorithms on 20 benchmark functions of CEC’2014 and CEC’2015. After that, improved sea lion optimization (ISLO) is used to train a hybrid neural network. Finally, the trained neural model is used for resource auto-scaling based on workload prediction with 4 real and public datasets. The experiments show that our neural network model provides improved results in comparison with other models, especially in comparison with neural networks trained using the original sea lion optimization. The proposed ISLO proved efficiency and improvement in solving problems ranging from global optimization with swarm intelligence to the prediction of workload elasticity.
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Liaquat S, Zia MF, Saleem O, Asif Z, Benbouzid M. Performance analysis of distance metrics on the exploitation properties and convergence behaviour of the conventional firefly algorithm. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Statistical determination of significant particle swarm optimization parameters: the case of Weibull distribution. Soft comput 2022. [DOI: 10.1007/s00500-022-07253-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms. Sci Rep 2022; 12:3883. [PMID: 35273236 PMCID: PMC8913629 DOI: 10.1038/s41598-022-07693-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/04/2022] [Indexed: 11/16/2022] Open
Abstract
Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly significant for the purpose of municipal and environmental damage mitigation. In the present study, machine learning (ML) models based on the support vector machine (SVM), artificial neural network (ANN), and long short-term memory (LSTM), are tested and developed to predict SF for 11 different rivers throughout Peninsular Malaysia. SF data sets for the rivers were collected from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a universal model that is most capable of predicting SFs for rivers within Peninsular Malaysia. Based on the findings, the ANN3 model which was developed using the ANN algorithm and input scenario 3 (inputs consisting of previous 3 days SF) is deduced as the best overall ML model for SF prediction as it outperformed all the other models in 4 out of 11 of the tested data sets; and obtained among the highest average RMs with a score of 3.27, hence indicating that the model is very adaptable and reliable in accurately predicting SF based on different data sets and river case studies. Therefore, the ANN3 model is proposed as a universal model for SF prediction within Peninsular Malaysia.
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11
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Essam Y, Huang YF, Birima AH, Ahmed AN, El-Shafie A. Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms. Sci Rep 2022; 12:302. [PMID: 34997183 PMCID: PMC8741754 DOI: 10.1038/s41598-021-04419-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/22/2021] [Indexed: 01/16/2023] Open
Abstract
High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia.
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Affiliation(s)
- Yusuf Essam
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, 43000, Selangor, Malaysia
| | - Yuk Feng Huang
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malaysia.
| | - Ahmed H Birima
- Department of Civil Engineering, College of Engineering, Qassim University, Unaizah, Saudi Arabia
| | - Ali Najah Ahmed
- Department of Civil Engineering, College of Engineering, Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, 43000, Selangor, Malaysia
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
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13
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Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia. SUSTAINABILITY 2021. [DOI: 10.3390/su13115900] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.
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