Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems

Mirarabi, Ali; Nassery, Hamid Reza; Nakhaei, Mohammad; Adamowski, Jan; Akbarzadeh, Abdolhamid; Alijani, Farshad

doi:10.1007/s12665-019-8474-y

Cited by 69 publications

(18 citation statements)

References 42 publications

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“…Support vector regression (SVR) is a nonparametric method, which was developed by Vapnik (1995). It has been used as a machine learning tool for classification and regression (Mirarabi et al, 2019). The performance of SVR is highly dependent on its kernel.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Band

Heggy

Bateni

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R 2 ), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R 2 of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.

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Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Band

Heggy

Bateni

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…To (1) ANN ANNs are capable of providing promising results in different aspects of hydrological modeling such as groundwater level modeling (Iqbal et al 2020;Mirarabi et al 2019), climate change study (Sabbaghi et al 2020), rainfall forecasting (Liu et al 2019), wind speed forecasting (Liu et al 2018c), streamflow prediction (Ba et al 2018), etc. Qiu et al (2020 developed a novel hydrological implementation of emotional ANN model for daily rainfall-runoff modeling.…”

Section: Model Developmentmentioning

confidence: 99%

An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

Wang

Chau

et al. 2021

Water Resour Manage

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Accurate and consistent annual runoff prediction in regions is a hot topic in the management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, the extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series.Secondly, the sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, we adopt wavelet packet decomposition (WPD) to further decompose the IMF with the maximum SE into several appropriate components and detailed components. Then the LSTM model, a deep learning algorithm based recurrent approach, is employed to predict all components obtained in the previous step. Finally, the forecasting results of all components are aggregated to generate the final prediction.The proposed model, which is applied to five annual series from different areas in China, is evaluated based on four quantitative indexes (R, NSEC, MAPE and RMSE). The results indicate that the ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four quantitative indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, making it an efficient instrument for scientific management and planning of water resources.

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“…Instead, it solely depends on the statistical associations between explanatory and response parameters. The soft computing models commonly used in GWL prediction include Artificial Neural Network (ANN) (Mirarabi et al 2019), Support Vector Machine (SVM) (Tang et al 2019), Adaptive-Network-based Fuzzy Inference System (ANFIS) (Gong et al, 2018), Extreme Learning Machine (ELM) (Alizamir et al, 2018), Random Forest (RF) (Koch et al 2019) and M5 tree (M5P) model (Kisi 2015). Despite their success in GWL modelling efficiently with less data, these techniques suffer from some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Estimation in Northwest Region of Bangladesh Using Hybrid Locally Weighted Linear Regression and Gaussian Process Regression Modeling

Elbeltagi

Salam

Pal

et al. 2022

Preprint

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The knowledge about the role of the underlying variables on groundwater level (GWL) fluctuation at local scale in the drought-prone urban areas of Bangladesh is still not explored. To better insight into the relative contribution of underlying factors on GWL fluctuation, this study proposed a novel hybrid ensemble modeling framework based on locally weighted linear regression (LWLR) and four Gaussian Process Regressions (GPRs) e.g., poly kernel, Pearson universal kernel (PUK), radian basis function (RBF) and normalized poly kernel. The proposed framework has employed to predict GWL at six wells in the drought-prone local areas of North-western urban region of Bangladesh, where GWL is declining rapidly. The rainfall, temperature (Tave), soil moisture (SM), normalized difference vegetation index (NDVI), Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), Nina3.4, and population growth rate for the period 1993-2017 were utilized as inputs to developed GWL models. The best input combination was explored using the best subset regression model and sensitivity analysis, and the optimal input combination was applied in LWLR and GPRs to estimate monthly GWL fluctuation. The hybrid LWLR-GPR-PUK model, on average, improves the prediction accuracy from 10 to 50% during the training stage and 20-70% during the testing stage compared to other models. The proposed modeling approach can act as a promising substitute tool to estimate GWL fluctuation, especially in drought-prone local areas in urban regions where groundwater data scarcity hinders the physical law-based model development.

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Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems

Cited by 69 publications

References 42 publications

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

Groundwater Level Estimation in Northwest Region of Bangladesh Using Hybrid Locally Weighted Linear Regression and Gaussian Process Regression Modeling

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