A Hybrid Model for Forecasting Groundwater Levels Based on Fuzzy C-Mean Clustering and Singular Spectrum Analysis

Abstract

Abstract:Having the ability to forecast groundwater levels is very significant because of their vital role in basic functions related to efficiency and the sustainability of water supplies. The uncertainty which dominates our understanding of the functioning of water supply systems is of great significance and arises as a consequence of the time-unbalanced water consumption rate and the deterioration of the recharge conditions of captured aquifers. The aim of this paper is to present a hybrid model based on fuzzy C-mean clustering and singular spectrum analysis to forecast the weekly values of the groundwater level of a groundwater source. This hybrid model demonstrates how the fuzzy C-mean can be used to transform the sequence of the observed data into a sequence of fuzzy states, serving as a basis for the forecasting of future states by singular spectrum analysis. In this way, the forecasting efficiency is improved, because we predict the interval rather than the crisp value where the level will be. It gives much more flexibility to the engineers when managing and planning sustainable water supplies. A model is tested by using the observed weekly time series of the groundwater source, located near the town ofČačak in south-western Serbia.

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“…In addition, when m ¼ 1, the partition is hard, and for m . 1, the partition is fuzzy and increasing m causes the partition to become fuzzie (Aree et al 2001;Liou et al 2003;Dzung et al 2015;Janmenjoy et al 2017;Polomcǐćet al 2017;Geranmehr et al 2019;Tolentino and Gerardo 2019;Xing & Li 2019).…”

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