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

Mohammad Mahdi Jafari,Guy Jean-Pierre Schumann,Hassan Ojaghlou,Mohammad Zare

doi:10.3390/atmos12010009

Mohammad Mahdi Jafari, Guy Jean-Pierre Schumann + Show 2 more

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https://doi.org/10.3390/atmos12010009

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Abstract

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.

Highlights

Hydrological changes, linked to global climate change, will have devastating effects on surface water and groundwater resources in many areas of the world [1]
The main purpose of the present study is to evaluate and compare the performance of adaptive network-based fuzzy inference system (ANFIS), and WaveletANFIS models based on fuzzy c-means clustering (FCM) for the simulation and prediction of monthly groundwater levels (GWL) in some parts of the Maragheh plain in northwestern Iran
The performance of the ANFIS and Wavelet-ANFIS models were evaluated by calculating R2 and RMSE statistical parameters

Summary

Introduction

Hydrological changes, linked to global climate change, will have devastating effects on surface water and groundwater resources in many areas of the world [1]. Recent droughts, coupled with irregular groundwater withdrawal through deep and semi-deep wells, have caused a severe drop in groundwater levels (GWL) and water quality degradation, soil erosion, drying of wells, reducing river discharge, increasing cost of pumping, and desertification [4]. In this regard, an effective water resources management is vital. The use of indirect methods such as artificial intelligence (AI) and machine learning (ML) models has been developed to analyze the trends of hydrological parameters The latter have been used in many hydro(geo)logical studies, in GWL predictions [5,6,7,8,9,10,11]. Finding suitable models that can simulate changes in GL oscillation is an ongoing challenge for hydrogeologists

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