Hybrid of SOM-Clustering Method and Wavelet-ANFIS Approach to Model and Infill Missing Groundwater Level Data

Abstract

This paper describes the potential use of artificial intelligence (AI)-based techniques to predict monthly groundwater level (GWL) and filling missing GWL data. The adaptive neuro-fuzzy inference system (ANFIS) model conjugated to data pre-processing methods was used for GWL modeling of a Tampa Bay plain at three distinct scenarios. In the proposed methodology, the self-organizing map (SOM) clustering method, as spatial preprocessor, and the discrete wavelet transform (DWT), as temporal data preprocessor, were linked to the ANFIS framework. The original time series of precipitation, runoff, and GWL parameters were decomposed into multifrequency subseries by DWT, and then the subseries were imposed as input data into an ANFIS model for each cluster identified by the SOM, for prediction of the GWL value one and multitime step ahead and to fill missing GWL data. The wavelet transform coherence (WTC) technique was also used for selecting dominant input variables of the ANFIS model. The performance of the ANFIS model was compared to the newly proposed hybrid WTC-ANFIS (WANFIS) model under three different scenarios. The obtained results show that the proposed model can predict GWL with reliable accuracy because the SOM-based spatial clustering method could decrease dimensionality of the inputs matrix, and on the other hand, application of DWT and WTC could enhance the performance of the model by exploring the important periods of the process.