Prediction and simulation of monthly groundwater levels by genetic programming

Fallah-Mehdipour, Elahe; Bozorg‐Haddad, Omid; Mariňo, Miguel A.

doi:10.1016/j.jher.2013.03.005

Cited by 146 publications

(45 citation statements)

References 29 publications

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“…GP has been successfully used in many water management problems and researchers have concluded that GP simulation equations decrease computational effort by using common simulation packages that can yield results with acceptable accuracy [FALLAH-MEHDIPOUR et al 2013;2014;RABU-NAL et al 2007;SAVIC et al 1999]. ANNs have been successfully used in many fields and are capable of describing highly nonlinear and complex hydrological processes.…”

Section: Previous Researchmentioning

confidence: 99%

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

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Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

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Section: Previous Researchmentioning

confidence: 99%

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

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show abstract

“…Recently, data-driven models such as artificial neural networks (ANN), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS) and genetic programming (GP), and time series methods such as autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) have been proved efficient in forecasting hydrologic time series (e.g., groundwater level, water demand and inflow) [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Yoon et al [5] developed ANN and SVM models to forecast groundwater level fluctuations in a coastal aquifer.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that the ANFIS and GP models can be applied successfully in groundwater depth prediction. Fallah-Mehdipour et al [6] investigated the capability of ANFIS and GP to forecast and simulate groundwater levels in the Karaj plain of Iran. These results indicated that GP is an effective method for predicting groundwater levels.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Gong

Wang

et al. 2018

Water

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Abstract:The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

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“…Chang & Chang, 2006;Elhatip & Kömür, 2008;Fallah-Mehdipour, Bozorg Haddad, & Mariño, 2013;Wei, 2012). Its history for water level modeling back to 1998 with the earliest research accomplished in water level forecasting (Thirumalaiah & Deo, 1998), who conducted real-time streamflow stage using artificial neural network.…”

Section: Introductionmentioning

confidence: 99%