Coupling ANFIS with ant colony optimization (ACO) algorithm for 1-, 2-, and 3-days ahead forecasting of daily streamflow, a case study in Poland

Aghelpour, Pouya; Graf, Renata; Tomaszewski, Edmund

doi:10.1007/s11356-023-26239-3

Cited by 9 publications

(3 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Alquraish et al ( 2021 ) demonstrated the versatility of combining ANFIS with meta-heuristics across multiple domains by using ANFIS with meta-heuristic algorithms to forecast reservoir inflow forecasting for the King Fahd Dam, Saudi Arabia, which supports the notion that improving predicting results can be achieved by combining ANFIS with meta-heuristic optimization techniques. The hybridization of ANFIS with meta-heuristic optimization algorithms such as genetic algorithm (GA) (Alquraish et al 2021 ) and ant colony optimization (ACO) (Aghelpour et al 2023 ) has been studied in the context of PET forecasting. As per Mehdizadeh et al ( 2021 ), shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO) have demonstrated their ability to estimate daily reference evapotranspiration accurately.…”

Section: Resultsmentioning

confidence: 99%

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Akiner,

Ghasri

2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, $${R}_{adj}^2$$ R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and $${R}_{adj}^2$$ R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and $${R}_{adj}^2$$ R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and $${R}_{adj}^2$$ R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model’s capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model’s superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Akiner,

Ghasri

2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…Zhao [42] proposed a value prediction and analysis method of network documents based on ant colony algorithm. Aghelpour et al [43] coupled adaptive neuro-fuzzy inference system with ant colony optimization algorithm to realize the 1-, 2-, and 3-days ahead forecasting of daily streamflow. Albashish and Aburomman [44] proposed a heterogeneous ensemble classifier configuration based on ant colony optimization for a multiclass intrusion detection problem.…”

Section: Introductionmentioning

confidence: 99%

Combination prediction method of students’ performance based on ant colony algorithm

Xu,

Kim

2024

PLoS ONE

View full text Add to dashboard Cite

Students’ performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students’ performance can guide students’ learning in time. Aiming at the low accuracy problem of single model in students’ performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students’ learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

show abstract

“…They compared the results of these methods to those of classical ANFIS and found that these methods were more successful than classical ANFIS for modeling and predicting air pollution. To find an efficient and reliable streamflow forecasting model, Aghelpour et al [25] developed an ANFIS model coupled with an ant colony optimization (ACO) algorithm to predict the streamflow of a river for 1 day, 2 days, and 3 days ahead. They found that the accuracy of the simple ANFIS model obtained was good.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

Kabengele,

Olayode,

Tartibu

2023

Applied Sciences

View full text Add to dashboard Cite

The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision making, and streamline power production planning. This paper explores the novel application of machine learning techniques to predict the performance of hybrid thermal power plants, specifically the integrated solar combined cycle power plant (ISCCPP). These plants combine conventional thermal power generation with renewable energy sources, making them crucial in the context of carbon reduction and enhanced efficiency. We employ three machine learning approaches: the adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized via particle swarm optimization (ANFIS-PSO), and ANFIS optimized through a genetic algorithm (ANFIS-GA). These methods are applied to the complex ISCCPP, comprising steam and gas turbine sections and a concentrated solar power system. The results highlight the accuracy of ANFIS-based models in evaluating and predicting plant performance, with an exceptional overall correlation coefficient of 0.9991. Importantly, integrating evolutionary algorithms (PSO and GA) into ANFIS significantly enhances performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA, with ANFIS-GA outperforming the others. This research provides a robust tool for designers, energy managers, and decision makers, offering valuable support in assessing the performance of hybrid thermal power plants. As the world transitions to cleaner energy sources, the insights gained here are poised to have a significant impact on the growing number of these thermal power plants globally.

show abstract

Coupling ANFIS with ant colony optimization (ACO) algorithm for 1-, 2-, and 3-days ahead forecasting of daily streamflow, a case study in Poland

Cited by 9 publications

References 75 publications

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Combination prediction method of students’ performance based on ant colony algorithm

Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

Contact Info

Product

Resources

About