Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models

Barzegar, Rahim; Fijani, Elham; Moghaddam, Asghar Asghari; Tziritis, Evangelos

doi:10.1016/j.scitotenv.2017.04.189

Cited by 180 publications

(73 citation statements)

References 62 publications

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“…Several numerical (physically based) models [e.g., Modular Finite‐Difference Groundwater Flow Model (MODFLOW) and Modular Three‐Dimensional Multispecies Transport Model (MT3DMS)) (Harbaugh et al ) have been developed for groundwater quality modeling purposes. However, numerical models require large amounts of specific data, have a complex structure, and are time‐consuming to calibrate (Barzegar et al , ). These limitations restrict their application, especially in the context of developing countries (Coppola et al ; Alagha et al ).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

Khosravi¹,

Barzegar

Miraki

et al. 2019

Groundwater

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While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance‐based K‐nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree‐based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., normalCa2+,normalMg2+,normalNa+,normalK+,normalHCO3−,normalCO32−,normalSO42−,andnormalCl− concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.

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Section: Introductionmentioning

confidence: 99%

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

Khosravi¹,

Barzegar

Miraki

et al. 2019

Groundwater

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“…All the weighting coefficients involved in this iterative function are calculated by a common least square method. More details about GMDH method can be found in Ivakhnenko [43] and Barzegar et al [44]. Table 2 that each environmental variable has high linear correlation with ET.…”

Section: Group Methods Of Datamentioning

confidence: 99%

Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Dou

Yang

2018

Advances in Meteorology

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Remarkable progress has been made over the last decade toward characterizing the mechanisms that dominate the exchange of water vapor between the biosphere and the atmosphere. This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. Three novel machine learning approaches, namely, group method of data handling, extreme learning machine (ELM), and adaptive neurofuzzy inference system (ANFIS), were developed to simulate and forecast the daily evapotranspiration (ET) at four different grassland sites based on the flux tower data using the eddy covariance method. These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). Moreover, the influences of internal functions on their corresponding models (SVM, ELM, and ANFIS) were investigated together. It was demonstrated that most developed models did good job of simulating and forecasting daily ET at the four sites. In addition to strengths of robustness and simplicity, the newly proposed methods achieved the estimates comparable to those of the conventional approaches and accordingly can be used as promising alternatives to traditional methods. It was further discovered that the generalization performance of the ELM, ANFIS, and SVM models strongly depended on their respective internal functions, especially for SVM.

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“…an aggregated multiwavelet forecast without treating their models probabilistically nor studying the reliability of the forecasts (such as in Alizadeh, Jafari Nodoushan, et al (2017); Barzegar, Asghari Moghaddam, Adamowski, et al (2018), Barzegar et al (2017)).…”

Section: Water Resources Researchmentioning

confidence: 99%

A Stochastic Data‐Driven Ensemble Forecasting Framework for Water Resources: A Case Study Using Ensemble Members Derived From a Database of Deterministic Wavelet‐Based Models

Quilty

Adamowski

Boucher

2019

Water Resources Research

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In water resources applications (e.g., streamflow, rainfall‐runoff, urban water demand [UWD], etc.), ensemble member selection and ensemble member weighting are two difficult yet important tasks in the development of ensemble forecasting systems. We propose and test a stochastic data‐driven ensemble forecasting framework that uses archived deterministic forecasts as input and results in probabilistic water resources forecasts. In addition to input data and (ensemble) model output uncertainty, the proposed approach integrates both ensemble member selection and weighting uncertainties, using input variable selection and data‐driven methods, respectively. Therefore, it does not require one to perform ensemble member selection and weighting separately. We applied the proposed forecasting framework to a previous real‐world case study in Montreal, Canada, to forecast daily UWD at multiple lead times. Using wavelet‐based forecasts as input data, we develop the Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, the first multiwavelet ensemble stochastic forecasting framework that produces probabilistic forecasts. For the considered case study, several variants of Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, produced using different input variable selection methods (partial correlation input selection and Edgeworth Approximations‐based conditional mutual information) and data‐driven models (multiple linear regression, extreme learning machines, and second‐order Volterra series models), are shown to outperform wavelet‐ and nonwavelet‐based benchmarks, especially during a heat wave (first time studied in the UWD forecasting literature).

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Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models

Cited by 180 publications

References 62 publications

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

A Stochastic Data‐Driven Ensemble Forecasting Framework for Water Resources: A Case Study Using Ensemble Members Derived From a Database of Deterministic Wavelet‐Based Models

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