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

Yadav, Basant; Ch, Sudheer; Mathur, Shashi; Adamowski, Jan

doi:10.1515/jwld-2017-0012

Cited by 67 publications

(26 citation statements)

References 18 publications

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“…The extreme learning machine model has shown better forecasting ability as compared to the support vector machine model for forecasting monthly groundwater levels at two observation wells located in Canada [YADAV et al 2017]. A hybrid least square support vector regression-gravitational search algorithm (HLGSA) was successfully used for predicting monthly river flows in Astor and Shyok catchments (Pakistan) [ADNAN et al 2017].…”

Section: Introductionmentioning

confidence: 99%

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Tadesse

Dinka

2017

Journal of Water and Land Development

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Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann-Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan-Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3) 12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

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Section: Introductionmentioning

confidence: 99%

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Tadesse

Dinka

2017

Journal of Water and Land Development

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“…Among these approaches, machine learning is the most effective inversion technique since the other techniques have limited application areas as well as depend on area-specific surface parameters and experimental equations [13,14]. Machine learning methods include partial least squares regression, support vector machines, extreme learning machine, the cubist regression, Bayes and neural networks, are the mostly employed inversion techniques for SWC prediction [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach

Acar¹,

Özerdem²

2020

Turk J Elec Eng & Comp Sci

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Soil water content (SWC) performs an important role in many areas including agriculture, drought cases, usage of water resources, hydrology, crop diseases and aerology. However, the measurement of the SWC over large terrains with standard computational techniques is very hard. In order to overcome this situation, remote sensing tools are preferred, which can produce much more successful results in less time than standard calculation techniques. Among all remote sensing tools, synthetic aperture radar (SAR) has a significant impact on determining SWC over large terrains. The main objective of this study is to predict SWC on a yearly basis over the vegetation-covered terrains with the aid of different machine learning techniques and SAR based Radarsat-2 data, which obtained in 2015 and 2016 years.The proposed system consists of several stages, respectively. In the feature extraction stage, the backscatter coefficients of different polarizations and the parameters obtained from different models of decomposition (Freeman-Durden and H/A/ α) were combined and nine polarimetric features were formed for each sample point. In the next stage, support vector regression (SVR), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) were employed for the prediction of SWC. In the last stage, a machine learning based feature selection was implemented to the obtained feature vectors for determining optimal feature sets. Finally, a feature set with 6 parameters was determined as most optimal feature set over the SWC prediction and a slightly better performance was observed thanks to this feature set compared to the other results.

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“…Sun et al () analyzed the ability of ANNs to predict water table depth in a swamp forest in Singapore, establishing that accurate estimates could be obtained with a daily LT, whereas the performance decreased for the LT of a week. Yadav et al () compared the performance of extreme learning machines and support vector machines in forecasting monthly GW levels in two different wells in Canada, discovering that extreme learning machines outperformed support vector machines in both analyzed case studies. We carried out a study to compare the predicting capabilities of five different DDMs to forecast seasonal (1‐ to 4‐month) GW levels in different hydrological regimes (Amaranto et al, ).…”

Section: Introductionmentioning

confidence: 99%

A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

Amaranto

Muñoz-Arriola

Solomatine

et al. 2019

Water Resources Research

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The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE > 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.tT

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

Cited by 67 publications

References 18 publications

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach

A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

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