Daily Rainfall Estimation using ANFIS Combination Models Trained by Clustering of Fuzzy c-Means and Evolutionary Algorithms

Najafzadeh, Mohammad; Afroozi, Diako; Barzkar, Ali

doi:10.22034/jewe.2021.237529.1376

Document Type : Research Paper

Authors

¹ Assist. Professor, Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran

² M.Sc. Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran

https://doi.org/10.22034/jewe.2021.237529.1376

Abstract

Nowadays, due to the high uncertainty in estimating precipitation in different geographical areas, the use of computational intelligence methods based on optimization algorithms to accurately estimate daily precipitation has been considered by water engineers. In the present study, the combined Adaptive Neuro Fuzzy Inference System and Wavelet transform (W-ANFIS) method was used as a pre-processor for daily rainfall data to estimate precipitation values. The structure of the W-ANFIS hybrid model was developed using the Fuzzy Clustering Means (FCM) method in the training phase. Moreover, constant coefficients of membership functions applied in the ANFIS model were optimized using four optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony community (ACO). In the present study, rainfall statistics of Izmir basin in the western part of Turkey were used. Through applying five-time delays in daily rainfall statistics as well as decomposing each time delay in the three levels of wavelet transform, each of the W-ANFIS optimal models had twenty input variables. The results of the statistical analysis for both training and testing stages by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) showed that the application of the DE algorithm into W-ANFIS structure had the best performance (RMSE = 22.22 and MAE = 17.11mm) than other combined models with PSO (RMSE = 28.11 and MAE = 24.11 mm), ACO (RMSE = 30.41 and MAE = 26.50 mm), and GA (RMSE = 25.70 and MAE = 18.11 mm).

Keywords

Main Subjects

water resource management

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Environment and Water Engineering

Daily Rainfall Estimation using ANFIS Combination Models Trained by Clustering of Fuzzy c-Means and Evolutionary Algorithms

References

References

Volume 7, Issue 2
June 2021
Pages 255-266

Daily Rainfall Estimation using ANFIS Combination Models Trained by Clustering of Fuzzy c-Means and Evolutionary Algorithms

References

References

Volume 7, Issue 2June 2021Pages 255-266

Volume 7, Issue 2
June 2021
Pages 255-266