Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression

Raghavendra, N. Sujay; Deka, Paresh Chandra

doi:10.1080/23311916.2014.999414

Cited by 55 publications

(19 citation statements)

References 37 publications

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“…The WPD is a generalization of the WD where the original signal passes through more filters than WD, capturing more details of the signal, and thereby offers a richer range of possibilities for signal analysis (Gokhale and Khanduja 2010). Recently, the conjunction of the WPD and machine learning techniques has been successfully applied in various fields (Amiri and Asadi 2009;Chen et al 2010;Gokhale and Khanduja 2010;Liu et al 2013;Ravikumar and Tamilselvan 2014;Raghavendra and Deka 2015;Seo 2015). Amiri and Asadi (2009) compared wavelet and wavelet packet transforms in processing ground motion records.…”

Section: Introductionmentioning

confidence: 97%

“…Ravikumar and Tamilselvan (2014) proposed the prediction of trends in nonlinear time series data based on wavelet packet transform. Raghavendra and Deka (2015) demonstrated the state-of-the-art ability of wavelet packet analysis in enhancing the prediction efficiency of support vector regression (SVR) through the improvement of a novel hybrid wavelet packet-support vector regression (WP-SVR) model for predicting monthly groundwater level fluctuation. Seo (2015) proposed a hybrid model, wavelet packet-ANN (WPANN), for river stage forecasting and investigated its precision.…”

Section: Introductionmentioning

confidence: 99%

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River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Seo

Kim

Kisi

et al. 2016

Water Resour Manage

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This study develops and applies three hybrid models, including wavelet packetartificial neural network (WPANN), wavelet packet-adaptive neuro-fuzzy inference system (WPANFIS) and wavelet packet-support vector machine (WPSVM), combining wavelet packet decomposition (WPD) and machine learning models, ANN, ANFIS and SVM models, for forecasting daily river stage and evaluates their performance. The WPANN, WPANFIS and WPSVM models using inputs decomposed by the WPD are found to produce higher efficiency based on statistical performance criteria than the ANN, ANFIS and SVM models using original inputs. Performance evaluation for various mother wavelets indicates that the model Water Resour Manage performance is dependent on mother wavelets and the WPD using Symmlet-10 and Coiflet-18 is more effective to enhance the efficiency of the conventional machine learning models than other mother wavelets. It is found that the WPANFIS model outperforms the WPANN and WPSVM models, and the WPANFIS14-coif18 model produces the best performance among all other models in terms of model efficiency. Therefore, the WPD can significantly enhance the accuracy of the conventional machine learning models, and the conjunction of the WPD and machine learning models can be an effective tool for forecasting daily river stage accurately .

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Seo

Kim

Kisi

et al. 2016

Water Resour Manage

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“…Recent studies have reported the impressive predictive accuracy of empirical models based on machine learning methods in groundwater level simulation (Behzad et al, 2010;Mohanty et al, 2010;Shortridge et al, 2016). Among the machine learning methods, SVM performs better in groundwater level simulations because it can model highly non-linear relationships (Behzad et al, 2010;Sudheer et al, 2011;Raghavendra and Deka, 2015;Tapak et al, 2014). Therefore, SVM was used to model the functional (f) dependence of groundwater level (y) on different independent variables (x i ),…”

Section: Methodsmentioning

confidence: 99%

Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh

Salem

Kazama

Shahid

et al. 2017

Hydrological Research Letters

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Abstract:Changes in hydrological processes due to rising temperatures and related effects on the socio-economy and people's livelihood are major concerns in Bangladesh. A study has been performed to assess the effects of increasing temperature on the groundwater levels and consequent changes in irrigation costs for groundwater-dependent irrigated agriculture in Northwest Bangladesh. A support vector machine (SVM) was used to model the temporal variations in groundwater level from rainfall, evapotranspiration, groundwater abstraction, and agricultural return flow. A multiple linear regression (MLR) model was developed to define the functional relationship between irrigation costs and groundwater levels. The model showed that average groundwater level during the major irrigation period (January-April) decreased by 0.15-2.01 m due to an increase in temperature of 1-5°C, which increased irrigation costs by 0.05-0.54 thousand Bangladesh Taka (BDT) per hector.

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“…Many models and techniques have been proposed to forecast time series in hydrogeology: the nonlinear optimization technique, the multiple linear regression method, the hybrid soft-computing technique, the hybrid wavelet packet-support vector regression method, artificial neural-network techniques, the adaptive neuro-fuzzy inference system method, and hydrodynamic modeling [2][3][4][5][6][7]. The singular spectrum analysis was used in this paper but is also implemented by various other authors [8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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

Polomčić

Gligorić

Bajić

et al. 2017

Water

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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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Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression

Cited by 55 publications

References 37 publications

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh

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

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