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

Abstract

Finding an efficient and reliable streamflow forecasting model has always been an important challenge for managers and planners of freshwater resources. The current study, based on an adaptive neuro-fuzzy inference system (ANFIS) model, was designed to predict the Warta river (Poland) streamflow for 1 day, 2 days, and 3 days ahead for a data set from the period of 1993–2013. The ANFIS was additionally combined with the ant colony optimization (ACO) algorithm and employed as a meta-heuristic ANFIS-ACO model, which is a novelty in streamflow prediction studies. The investigations showed that on a daily scale, precipitation had a very weak and insignificant effect on the river’s flow variation, so it was not considered as a predictor input. The predictor inputs were selected by the autocorrelation function from among the daily streamflow time lags for all stations. The predictions were evaluated with the actual streamflow data, using such criteria as root mean square error (RMSE), normalized RMSE (NRMSE), and R2. According to the NRMSE values, which ranged between 0.016–0.006, 0.030–0.013, and 0.038–0.020 for the 1-day, 2-day, and 3-day lead times, respectively, all predictions were classified as excellent in terms of accuracy (prediction quality). The best RMSE value was 1.551 m3/s and the highest R2 value was equal to 0.998, forecast for 1-day lead time. The combination of ANFIS with the ACO algorithm enabled to significantly improve streamflow prediction. The use of this coupling can averagely increase the prediction accuracies of ANFIS by 12.1%, 12.91%, and 13.66%, for 1-day, 2-day, and 3-day lead times, respectively. The current satisfactory results suggest that the employed hybrid approach could be successfully applied for daily streamflow prediction in other catchment areas.

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

“…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.…”

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

“…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.…”

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

“…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.…”