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Article

Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations

1
Department of Water Science and Engineering, University of Zanjan, Zanjan 45371-38791, Iran
2
Research and Education Department (RED), RSS-Hydro, 3593 Dudelange, Luxembourg
3
Institute of Civil and Environmental Engineering (INCEEN), Faculty of Science, Technology and Communication (FSTC), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
4
School of Geographical Sciences, University of Bristol, Bristol BS8 1TL, UK
*
Authors to whom correspondence should be addressed.
Atmosphere 2021, 12(1), 9; https://doi.org/10.3390/atmos12010009
Received: 30 November 2020 / Revised: 17 December 2020 / Accepted: 21 December 2020 / Published: 23 December 2020
In order to optimize the management of groundwater resources, accurate estimates of groundwater level (GWL) fluctuations are required. In recent years, the use of artificial intelligence methods based on data mining theory has increasingly attracted attention. The goal of this research is to evaluate and compare the performance of adaptive network-based fuzzy inference system (ANFIS) and Wavelet-ANFIS models based on FCM for simulation/prediction of monthly GWL in the Maragheh plain in northwestern Iran. A 22-year dataset (1996–2018) including hydrological parameters such as monthly precipitation (P) and GWL from 25 observation wells was used as models input data. To improve the prediction accuracy of hybrid Wavelet-ANFIS model, different mother wavelets and different numbers of clusters and decomposition levels were investigated. The new hybrid model with Sym4-mother wavelet, two clusters and a decomposition level equal to 3 showed the best performance. The maximum values of R2 in the training and testing phases were 0.997 and 0.994, respectively, and the best RMSE values were 0.05 and 0.08 m, respectively. By comparing the results of the ANFIS and hybrid Wavelet-ANFIS models, it can be deduced that a hybrid model is an acceptable method in modeling of GWL because it employs both the wavelet transform and FCM clustering technique. View Full-Text
Keywords: artificial intelligence; groundwater level; Wavelet-ANFIS; decomposition level artificial intelligence; groundwater level; Wavelet-ANFIS; decomposition level
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MDPI and ACS Style

Jafari, M.M.; Ojaghlou, H.; Zare, M.; Schumann, G.J.-P. Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations. Atmosphere 2021, 12, 9. https://doi.org/10.3390/atmos12010009

AMA Style

Jafari MM, Ojaghlou H, Zare M, Schumann GJ-P. Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations. Atmosphere. 2021; 12(1):9. https://doi.org/10.3390/atmos12010009

Chicago/Turabian Style

Jafari, Mohammad Mahdi, Hassan Ojaghlou, Mohammad Zare, and Guy Jean-Pierre Schumann. 2021. "Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations" Atmosphere 12, no. 1: 9. https://doi.org/10.3390/atmos12010009

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