A time-varying autoregressive model for groundwater depth prediction
•A TVAR model is introduced to predict groundwater depth.•The implementation of parameter estimation of the TVAR model is summarized.•The TVAR exhibits better prediction performance than the ARI and SARI models. The nonstationarity of hydrological variables makes the application of autoregressive (A...
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| Vydané v: | Journal of hydrology (Amsterdam) Ročník 613; s. 128394 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.10.2022
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| ISSN: | 0022-1694, 1879-2707 |
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| Abstract | •A TVAR model is introduced to predict groundwater depth.•The implementation of parameter estimation of the TVAR model is summarized.•The TVAR exhibits better prediction performance than the ARI and SARI models.
The nonstationarity of hydrological variables makes the application of autoregressive (AR) models challenging. Therefore, this study introduces a new time-varying AR (TVAR) model in the field of hydrology. Specifically, in this study, we focus the parameter estimation of the TVAR model and exploring the model’s performance for predicting groundwater depth. We demonstrate the application of the model to the monthly groundwater depth series obtained on the Guanzhong Plain, China. We summarize the process of parameter estimation of the TVAR model. First, the TVAR model is transformed into the time-invariance regression problem by expanding the time-varying coefficients into a set of Fourier or Legendre basis functions. Then, a fading memory recursive least squares (FMRLS) algorithm is used to estimate the parameters of the regression problem. In this process, the model order and dimension of the basis function are determined by minimizing our proposed improved Bayesian information criterion (IBIC) with a range of dimensions greater than 0. To further demonstrate the effectiveness of the parameter estimation method and the generalizable performance of the model, the method is applied to nonstationary series simulated in statistical experiments. The study results indicate that the TVAR model based on such a parameter estimation process exhibits better prediction performance, lower model complexity and more straight-forward application compared with the autoregressive integrated (ARI) and seasonal ARI (SARI) models. In conclusion, using the TVAR model as an alternative to the time-invariance ARI and SARI models results in a model that is more flexible and suitable for nonstationary groundwater depth prediction. |
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| AbstractList | •A TVAR model is introduced to predict groundwater depth.•The implementation of parameter estimation of the TVAR model is summarized.•The TVAR exhibits better prediction performance than the ARI and SARI models.
The nonstationarity of hydrological variables makes the application of autoregressive (AR) models challenging. Therefore, this study introduces a new time-varying AR (TVAR) model in the field of hydrology. Specifically, in this study, we focus the parameter estimation of the TVAR model and exploring the model’s performance for predicting groundwater depth. We demonstrate the application of the model to the monthly groundwater depth series obtained on the Guanzhong Plain, China. We summarize the process of parameter estimation of the TVAR model. First, the TVAR model is transformed into the time-invariance regression problem by expanding the time-varying coefficients into a set of Fourier or Legendre basis functions. Then, a fading memory recursive least squares (FMRLS) algorithm is used to estimate the parameters of the regression problem. In this process, the model order and dimension of the basis function are determined by minimizing our proposed improved Bayesian information criterion (IBIC) with a range of dimensions greater than 0. To further demonstrate the effectiveness of the parameter estimation method and the generalizable performance of the model, the method is applied to nonstationary series simulated in statistical experiments. The study results indicate that the TVAR model based on such a parameter estimation process exhibits better prediction performance, lower model complexity and more straight-forward application compared with the autoregressive integrated (ARI) and seasonal ARI (SARI) models. In conclusion, using the TVAR model as an alternative to the time-invariance ARI and SARI models results in a model that is more flexible and suitable for nonstationary groundwater depth prediction. The nonstationarity of hydrological variables makes the application of autoregressive (AR) models challenging. Therefore, this study introduces a new time-varying AR (TVAR) model in the field of hydrology. Specifically, in this study, we focus the parameter estimation of the TVAR model and exploring the model’s performance for predicting groundwater depth. We demonstrate the application of the model to the monthly groundwater depth series obtained on the Guanzhong Plain, China. We summarize the process of parameter estimation of the TVAR model. First, the TVAR model is transformed into the time-invariance regression problem by expanding the time-varying coefficients into a set of Fourier or Legendre basis functions. Then, a fading memory recursive least squares (FMRLS) algorithm is used to estimate the parameters of the regression problem. In this process, the model order and dimension of the basis function are determined by minimizing our proposed improved Bayesian information criterion (IBIC) with a range of dimensions greater than 0. To further demonstrate the effectiveness of the parameter estimation method and the generalizable performance of the model, the method is applied to nonstationary series simulated in statistical experiments. The study results indicate that the TVAR model based on such a parameter estimation process exhibits better prediction performance, lower model complexity and more straight-forward application compared with the autoregressive integrated (ARI) and seasonal ARI (SARI) models. In conclusion, using the TVAR model as an alternative to the time-invariance ARI and SARI models results in a model that is more flexible and suitable for nonstationary groundwater depth prediction. |
| ArticleNumber | 128394 |
| Author | Song, Songbai Yan, Yating Guo, Tianli |
| Author_xml | – sequence: 1 givenname: Tianli surname: Guo fullname: Guo, Tianli email: guotianli@nwsuaf.edu.cn organization: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China – sequence: 2 givenname: Songbai surname: Song fullname: Song, Songbai email: ssb6533@nwafu.edu.cn organization: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China – sequence: 3 givenname: Yating surname: Yan fullname: Yan, Yating email: 2019050804@nwafu.edu.cn organization: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China |
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| Cites_doi | 10.1109/TBME.2019.2906688 10.1007/s00477-016-1306-7 10.1016/j.specom.2019.03.002 10.1016/j.neucom.2015.04.128 10.1037/met0000085 10.1016/j.jhydrol.2014.11.065 10.1016/j.physa.2015.08.060 10.1016/j.neucom.2015.08.022 10.1371/journal.pcbi.1007566 10.1109/ACCESS.2019.2950798 10.1109/LSP.2008.2001559 10.1080/13504851.2020.1791793 10.1016/j.jneumeth.2016.12.018 10.1109/78.258089 10.1007/s00521-020-05330-7 10.1504/IJMIC.2010.032802 10.1016/j.dsp.2016.08.001 10.1016/j.jhydrol.2014.10.039 10.1029/2021WR030209 10.1109/TCST.2010.2052257 10.1007/s40313-018-0370-2 10.1111/jmcb.12402 10.1027/1015-5759/a000589 10.1111/gwat.12968 10.1016/j.sigpro.2011.04.021 10.1007/s11269-012-0194-y 10.1016/j.jeconom.2013.10.009 10.1016/j.scitotenv.2022.153030 10.1002/acs.3066 10.1111/jopy.12528 10.1016/j.neucom.2016.01.062 10.1016/0304-4076(92)90104-Y |
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| Keywords | Groundwater depth prediction Improved Bayesian information criterion Basis functions Fading memory recursive least squares algorithm Time-varying autoregressive model |
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| References | Tsatsanis, Giannakis (b0165) 1993; 41 Lanne, Luoto (b0085) 2017; 49 Li, Cui, Guo, Huang, Yang, Wei (b0110) 2018; 29 Kwiatkowski, Phillips, Schmidt, Shin (b0080) 1992; 54 Cui, Singh (b0025) 2016; 442 Guo, Song, Ma (b0055) 2021; 57 Huang, Hou, Chang, Huang, Chen (b0065) 2014; 519 Lee, Chon (b0090) 2011; 58 Shirmohammadi, Vafakhah, Moosavi, Moghaddamnia (b0145) 2013; 27 Cui, Singh (b0030) 2017; 31 Gutierrez, Salazar-Varas (b0060) 2011; 2011 Su, Zou, Jiang, Qian (b0150) 2021; 9 Mirhoseini, Tabatabaei (b0125) 2018; 29 Bringmann, Hamaker, Vigo, Aubert, Borsboom, Tuerlinckx (b0010) 2017; 22 Li, Luo, Li (b0105) 2016; 193 Wei, Billings, Liu (b0180) 2010; 9 Zhang, Xu, Qian (b0195) 2019; 16 Fuentealba, Illanes, Ortmeier (b0035) 2019; 7 Li, Lei, Cui, Guo, Wei (b0115) 2019; 66 Cui, Singh (b0020) 2015; 521 Sun, Ji, Ren, Xie, Yan (b0155) 2019; 12 Jiang, Qian, Pan, Chai (b0070) 2020; 34 Pascucci, Rubega, Plomp, Battaglia (b0140) 2020; 16 Casini, Richetin, Preti, Bringmann (b0015) 2020; 88 Guo, Guo, Billings, Wei (b0045) 2016; 173 Albers, Bringmann (b0005) 2020; 36 Sung, Lee (b0160) 2019; 8 Wang, Wei, Wang, Xu (b0175) 2021; 33 Li, Yuan, Yuan, Xu (b0120) 2021; 28 Giraitis, Kapetanios, Yates (b0040) 2014; 179 Li, Wei, Billings (b0095) 2011; 19 Guo, Song, Shi, Li (b0050) 2020; 58 Paleologu, Benesty, Ciochina (b0130) 2008; 15 Khorshidi, Karimi, Nematollahi (b0075) 2011; 91 Parchami, Amindavar, Zhu (b0135) 2019; 109 Wan, Xiao (b0170) 2016; 59 Xu, Li, Guo, Yang, Chan (b0185) 2017; 278 Li, Liu, Tan, Chan (b0100) 2016; 195 Zhang, Su, Zhang, Wu, Wang, Chu (b0190) 2022; 819 Cui (10.1016/j.jhydrol.2022.128394_b0030) 2017; 31 Guo (10.1016/j.jhydrol.2022.128394_b0045) 2016; 173 Zhang (10.1016/j.jhydrol.2022.128394_b0195) 2019; 16 Lee (10.1016/j.jhydrol.2022.128394_b0090) 2011; 58 Cui (10.1016/j.jhydrol.2022.128394_b0025) 2016; 442 Li (10.1016/j.jhydrol.2022.128394_b0105) 2016; 193 Guo (10.1016/j.jhydrol.2022.128394_b0055) 2021; 57 Bringmann (10.1016/j.jhydrol.2022.128394_b0010) 2017; 22 Cui (10.1016/j.jhydrol.2022.128394_b0020) 2015; 521 Zhang (10.1016/j.jhydrol.2022.128394_b0190) 2022; 819 Lanne (10.1016/j.jhydrol.2022.128394_b0085) 2017; 49 Paleologu (10.1016/j.jhydrol.2022.128394_b0130) 2008; 15 Kwiatkowski (10.1016/j.jhydrol.2022.128394_b0080) 1992; 54 Guo (10.1016/j.jhydrol.2022.128394_b0050) 2020; 58 Sun (10.1016/j.jhydrol.2022.128394_b0155) 2019; 12 Sung (10.1016/j.jhydrol.2022.128394_b0160) 2019; 8 Albers (10.1016/j.jhydrol.2022.128394_b0005) 2020; 36 Jiang (10.1016/j.jhydrol.2022.128394_b0070) 2020; 34 Mirhoseini (10.1016/j.jhydrol.2022.128394_b0125) 2018; 29 Wan (10.1016/j.jhydrol.2022.128394_b0170) 2016; 59 Gutierrez (10.1016/j.jhydrol.2022.128394_b0060) 2011; 2011 Li (10.1016/j.jhydrol.2022.128394_b0110) 2018; 29 Wang (10.1016/j.jhydrol.2022.128394_b0175) 2021; 33 Li (10.1016/j.jhydrol.2022.128394_b0095) 2011; 19 Li (10.1016/j.jhydrol.2022.128394_b0100) 2016; 195 Fuentealba (10.1016/j.jhydrol.2022.128394_b0035) 2019; 7 Li (10.1016/j.jhydrol.2022.128394_b0120) 2021; 28 Li (10.1016/j.jhydrol.2022.128394_b0115) 2019; 66 Khorshidi (10.1016/j.jhydrol.2022.128394_b0075) 2011; 91 Shirmohammadi (10.1016/j.jhydrol.2022.128394_b0145) 2013; 27 Giraitis (10.1016/j.jhydrol.2022.128394_b0040) 2014; 179 Parchami (10.1016/j.jhydrol.2022.128394_b0135) 2019; 109 Wei (10.1016/j.jhydrol.2022.128394_b0180) 2010; 9 Huang (10.1016/j.jhydrol.2022.128394_b0065) 2014; 519 Pascucci (10.1016/j.jhydrol.2022.128394_b0140) 2020; 16 Tsatsanis (10.1016/j.jhydrol.2022.128394_b0165) 1993; 41 Xu (10.1016/j.jhydrol.2022.128394_b0185) 2017; 278 Su (10.1016/j.jhydrol.2022.128394_b0150) 2021; 9 Casini (10.1016/j.jhydrol.2022.128394_b0015) 2020; 88 |
| References_xml | – volume: 54 start-page: 159 year: 1992 end-page: 178 ident: b0080 article-title: Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? publication-title: Journal of Econometrics. – volume: 195 start-page: 96 year: 2016 end-page: 103 ident: b0100 article-title: High-resolution time-frequency analysis of EEG signals using multiscale radial basis functions publication-title: Neurocomputing. – volume: 27 start-page: 419 year: 2013 end-page: 432 ident: b0145 article-title: Application of Several Data-Driven Techniques for Predicting Groundwater Level publication-title: Water Resour. Manag. – volume: 28 start-page: 995 year: 2021 end-page: 999 ident: b0120 article-title: Algorithms comparison on intraday index return prediction: evidence from China publication-title: Applied Economics Letters. – volume: 33 start-page: 5525 year: 2021 end-page: 5541 ident: b0175 article-title: A novel time-varying modeling and signal processing approach for epileptic seizure detection and classification publication-title: Neural Comput. Appl. – volume: 173 start-page: 715 year: 2016 end-page: 723 ident: b0045 article-title: Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems publication-title: Neurocomputing. – volume: 16 year: 2019 ident: b0195 article-title: Assessment of Groundwater Quality and Human Health Risk (HHR) Evaluation of Nitrate in the Central-Western Guanzhong Basin publication-title: China. Int. J. Env. Res. Pub. He. – volume: 16 start-page: e1007566 year: 2020 ident: b0140 article-title: Modeling time-varying brain networks with a self-tuning optimized Kalman filter publication-title: PLoS Comput. Biol. – volume: 2011 start-page: 6585 year: 2011 end-page: 6588 ident: b0060 article-title: EEG signal classification using time-varying autoregressive models and common spatial patterns. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society publication-title: Annual International Conference. – volume: 109 start-page: 1 year: 2019 end-page: 14 ident: b0135 article-title: Speech reverberation suppression for time-varying environments using weighted prediction error method with time-varying autoregressive model publication-title: Speech Commun. – volume: 9 year: 2021 ident: b0150 article-title: Research on Adaptive Hybrid Energy Consumption Model Based on Data Driven under Variable Working Conditions. Frontiers in Energy publication-title: Research. – volume: 442 start-page: 91 year: 2016 end-page: 99 ident: b0025 article-title: Maximum entropy spectral analysis for streamflow forecasting publication-title: Physica A-Statistical Mechanices and Its Applications. – volume: 91 start-page: 2359 year: 2011 end-page: 2370 ident: b0075 article-title: New autoregressive (AR) order selection criteria based on the prediction error estimation publication-title: Signal Process. – volume: 19 start-page: 656 year: 2011 end-page: 663 ident: b0095 article-title: Identification of Time-Varying Systems Using Multi-Wavelet Basis Functions publication-title: IEEE T. Contr. Syst. T. – volume: 58 start-page: 790 year: 2011 end-page: 794 ident: b0090 article-title: Time-Varying Autoregressive Model-Based Multiple Modes Particle Filtering Algorithm for Respiratory Rate Extraction From Pulse Oximeter publication-title: IEEE T. Bio.-Med. Eng. – volume: 59 start-page: 1 year: 2016 end-page: 8 ident: b0170 article-title: Variational Bayesian learning for robust AR modeling with the presence of sparse impulse noise publication-title: Digtal Signal Processing. – volume: 57 year: 2021 ident: b0055 article-title: Point and Interval Forecasting of Groundwater Depth Using Nonlinear Models publication-title: Water Resour. Res. – volume: 66 start-page: 3509 year: 2019 end-page: 3525 ident: b0115 article-title: A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs publication-title: IEEE T. Bio.-Med. Eng. – volume: 41 start-page: 3512 year: 1993 end-page: 3523 ident: b0165 article-title: TIME-VARYING SYSTEM-IDENTIFICATION AND MODEL VALIDATION USING WAVELETS publication-title: IEEE T. Signal Proces. – volume: 521 start-page: 1 year: 2015 end-page: 17 ident: b0020 article-title: Configurational entropy theory for streamflow forecasting publication-title: J. Hydrol. – volume: 7 start-page: 159754 year: 2019 end-page: 159772 ident: b0035 article-title: Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment publication-title: IEEE Access – volume: 88 start-page: 806 year: 2020 end-page: 821 ident: b0015 article-title: Using the time-varying autoregressive model to study dynamic changes in situation perceptions and emotional reactions publication-title: J. Pers. – volume: 12 year: 2019 ident: b0155 article-title: Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery publication-title: Energies. – volume: 8 year: 2019 ident: b0160 article-title: Implementation of SOH Estimator in Automotive BMSs Using Recursive Least-Squares publication-title: Electronics. – volume: 49 start-page: 969 year: 2017 end-page: 995 ident: b0085 article-title: A New Time-Varying Parameter Autoregressive Model for US Inflation Expectations publication-title: Journal of money credit and banking. – volume: 36 start-page: 492 year: 2020 end-page: 499 ident: b0005 article-title: Inspecting Gradual and Abrupt Changes in Emotion Dynamics With the Time-Varying Change Point Autoregressive Model publication-title: European Journal of Psychological Assessment. – volume: 15 start-page: 597 year: 2008 end-page: 600 ident: b0130 article-title: A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification publication-title: IEEE Signal Proc. Let. – volume: 9 start-page: 215 year: 2010 ident: b0180 article-title: Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets publication-title: Int. J. Model. Ident. Control – volume: 58 start-page: 749 year: 2020 end-page: 758 ident: b0050 article-title: Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input publication-title: Groundwater. – volume: 519 start-page: 3204 year: 2014 end-page: 3213 ident: b0065 article-title: Copulas-based probabilistic characterization of the combination of dry and wet conditions in the Guanzhong Plain publication-title: China. J. Hydrol. – volume: 29 start-page: 2960 year: 2018 end-page: 2972 ident: b0110 article-title: Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG publication-title: IEEE T. Neur. Net. Lear. – volume: 819 year: 2022 ident: b0190 article-title: Evaluation of the impacts of human activities on propagation from meteorological drought to hydrological drought in the Weihe River Basin publication-title: China. Science of The Total Environment. – volume: 22 start-page: 409 year: 2017 end-page: 425 ident: b0010 article-title: Changing Dynamics: Time-Varying Autoregressive Models Using Generalized Additive Modeling publication-title: Psychol. Methods – volume: 179 start-page: 46 year: 2014 end-page: 65 ident: b0040 article-title: Inference on stochastic time-varying coefficient models publication-title: J. Econometrics. – volume: 31 start-page: 587 year: 2017 end-page: 608 ident: b0030 article-title: Application of minimum relative entropy theory for streamflow forecasting publication-title: Stoch. Env. Res. Risk A. – volume: 278 start-page: 46 year: 2017 end-page: 56 ident: b0185 article-title: Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions publication-title: J. Neurosci. Meth. – volume: 193 start-page: 106 year: 2016 end-page: 114 ident: b0105 article-title: A multiwavelet-based time-varying model identification approach for time-frequency analysis of EEG signals publication-title: Neurocomputing. – volume: 29 start-page: 136 year: 2018 end-page: 152 ident: b0125 article-title: Bi-loop Matrix Forgetting Factor-Based Coupled Recursive Least Squares Algorithm for Identification of Multivariable Plants publication-title: Journal of Control, Automation and Electrical Systems. – volume: 34 start-page: 15 year: 2020 end-page: 31 ident: b0070 article-title: The research of superheated steam temperature control based on generalized predictive control algorithm and adaptive forgetting factor publication-title: Int. J. Adapt. Control. – volume: 66 start-page: 3509 issue: 12 year: 2019 ident: 10.1016/j.jhydrol.2022.128394_b0115 article-title: A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs publication-title: IEEE T. Bio.-Med. Eng. doi: 10.1109/TBME.2019.2906688 – volume: 31 start-page: 587 issue: 3 year: 2017 ident: 10.1016/j.jhydrol.2022.128394_b0030 article-title: Application of minimum relative entropy theory for streamflow forecasting publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-016-1306-7 – volume: 8 issue: 123711 year: 2019 ident: 10.1016/j.jhydrol.2022.128394_b0160 article-title: Implementation of SOH Estimator in Automotive BMSs Using Recursive Least-Squares publication-title: Electronics. – volume: 109 start-page: 1 year: 2019 ident: 10.1016/j.jhydrol.2022.128394_b0135 article-title: Speech reverberation suppression for time-varying environments using weighted prediction error method with time-varying autoregressive model publication-title: Speech Commun. doi: 10.1016/j.specom.2019.03.002 – volume: 195 start-page: 96 issue: SI year: 2016 ident: 10.1016/j.jhydrol.2022.128394_b0100 article-title: High-resolution time-frequency analysis of EEG signals using multiscale radial basis functions publication-title: Neurocomputing. doi: 10.1016/j.neucom.2015.04.128 – volume: 22 start-page: 409 issue: 3 year: 2017 ident: 10.1016/j.jhydrol.2022.128394_b0010 article-title: Changing Dynamics: Time-Varying Autoregressive Models Using Generalized Additive Modeling publication-title: Psychol. Methods doi: 10.1037/met0000085 – volume: 521 start-page: 1 year: 2015 ident: 10.1016/j.jhydrol.2022.128394_b0020 article-title: Configurational entropy theory for streamflow forecasting publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.11.065 – volume: 442 start-page: 91 year: 2016 ident: 10.1016/j.jhydrol.2022.128394_b0025 article-title: Maximum entropy spectral analysis for streamflow forecasting publication-title: Physica A-Statistical Mechanices and Its Applications. doi: 10.1016/j.physa.2015.08.060 – volume: 173 start-page: 715 year: 2016 ident: 10.1016/j.jhydrol.2022.128394_b0045 article-title: Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems publication-title: Neurocomputing. doi: 10.1016/j.neucom.2015.08.022 – volume: 16 start-page: e1007566 issue: 8 year: 2020 ident: 10.1016/j.jhydrol.2022.128394_b0140 article-title: Modeling time-varying brain networks with a self-tuning optimized Kalman filter publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1007566 – volume: 7 start-page: 159754 year: 2019 ident: 10.1016/j.jhydrol.2022.128394_b0035 article-title: Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2950798 – volume: 15 start-page: 597 year: 2008 ident: 10.1016/j.jhydrol.2022.128394_b0130 article-title: A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification publication-title: IEEE Signal Proc. Let. doi: 10.1109/LSP.2008.2001559 – volume: 12 issue: 224212 year: 2019 ident: 10.1016/j.jhydrol.2022.128394_b0155 article-title: Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery publication-title: Energies. – volume: 28 start-page: 995 issue: 12 year: 2021 ident: 10.1016/j.jhydrol.2022.128394_b0120 article-title: Algorithms comparison on intraday index return prediction: evidence from China publication-title: Applied Economics Letters. doi: 10.1080/13504851.2020.1791793 – volume: 278 start-page: 46 year: 2017 ident: 10.1016/j.jhydrol.2022.128394_b0185 article-title: Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions publication-title: J. Neurosci. Meth. doi: 10.1016/j.jneumeth.2016.12.018 – volume: 41 start-page: 3512 issue: 12 year: 1993 ident: 10.1016/j.jhydrol.2022.128394_b0165 article-title: TIME-VARYING SYSTEM-IDENTIFICATION AND MODEL VALIDATION USING WAVELETS publication-title: IEEE T. Signal Proces. doi: 10.1109/78.258089 – volume: 33 start-page: 5525 issue: 11 year: 2021 ident: 10.1016/j.jhydrol.2022.128394_b0175 article-title: A novel time-varying modeling and signal processing approach for epileptic seizure detection and classification publication-title: Neural Comput. Appl. doi: 10.1007/s00521-020-05330-7 – volume: 9 start-page: 215 issue: 3 year: 2010 ident: 10.1016/j.jhydrol.2022.128394_b0180 article-title: Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets publication-title: Int. J. Model. Ident. Control doi: 10.1504/IJMIC.2010.032802 – volume: 59 start-page: 1 year: 2016 ident: 10.1016/j.jhydrol.2022.128394_b0170 article-title: Variational Bayesian learning for robust AR modeling with the presence of sparse impulse noise publication-title: Digtal Signal Processing. doi: 10.1016/j.dsp.2016.08.001 – volume: 16 issue: 424621 year: 2019 ident: 10.1016/j.jhydrol.2022.128394_b0195 article-title: Assessment of Groundwater Quality and Human Health Risk (HHR) Evaluation of Nitrate in the Central-Western Guanzhong Basin publication-title: China. Int. J. Env. Res. Pub. He. – volume: 519 start-page: 3204 issue: D year: 2014 ident: 10.1016/j.jhydrol.2022.128394_b0065 article-title: Copulas-based probabilistic characterization of the combination of dry and wet conditions in the Guanzhong Plain publication-title: China. J. Hydrol. doi: 10.1016/j.jhydrol.2014.10.039 – volume: 57 issue: 12 year: 2021 ident: 10.1016/j.jhydrol.2022.128394_b0055 article-title: Point and Interval Forecasting of Groundwater Depth Using Nonlinear Models publication-title: Water Resour. Res. doi: 10.1029/2021WR030209 – volume: 19 start-page: 656 issue: 3 year: 2011 ident: 10.1016/j.jhydrol.2022.128394_b0095 article-title: Identification of Time-Varying Systems Using Multi-Wavelet Basis Functions publication-title: IEEE T. Contr. Syst. T. doi: 10.1109/TCST.2010.2052257 – volume: 29 start-page: 136 issue: 2 year: 2018 ident: 10.1016/j.jhydrol.2022.128394_b0125 article-title: Bi-loop Matrix Forgetting Factor-Based Coupled Recursive Least Squares Algorithm for Identification of Multivariable Plants publication-title: Journal of Control, Automation and Electrical Systems. doi: 10.1007/s40313-018-0370-2 – volume: 49 start-page: 969 issue: 5 year: 2017 ident: 10.1016/j.jhydrol.2022.128394_b0085 article-title: A New Time-Varying Parameter Autoregressive Model for US Inflation Expectations publication-title: Journal of money credit and banking. doi: 10.1111/jmcb.12402 – volume: 58 start-page: 790 issue: 32 year: 2011 ident: 10.1016/j.jhydrol.2022.128394_b0090 article-title: Time-Varying Autoregressive Model-Based Multiple Modes Particle Filtering Algorithm for Respiratory Rate Extraction From Pulse Oximeter publication-title: IEEE T. Bio.-Med. Eng. – volume: 36 start-page: 492 issue: 3SI year: 2020 ident: 10.1016/j.jhydrol.2022.128394_b0005 article-title: Inspecting Gradual and Abrupt Changes in Emotion Dynamics With the Time-Varying Change Point Autoregressive Model publication-title: European Journal of Psychological Assessment. doi: 10.1027/1015-5759/a000589 – volume: 58 start-page: 749 issue: 5 year: 2020 ident: 10.1016/j.jhydrol.2022.128394_b0050 article-title: Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input publication-title: Groundwater. doi: 10.1111/gwat.12968 – volume: 91 start-page: 2359 issue: 10 year: 2011 ident: 10.1016/j.jhydrol.2022.128394_b0075 article-title: New autoregressive (AR) order selection criteria based on the prediction error estimation publication-title: Signal Process. doi: 10.1016/j.sigpro.2011.04.021 – volume: 27 start-page: 419 issue: 2 year: 2013 ident: 10.1016/j.jhydrol.2022.128394_b0145 article-title: Application of Several Data-Driven Techniques for Predicting Groundwater Level publication-title: Water Resour. Manag. doi: 10.1007/s11269-012-0194-y – volume: 179 start-page: 46 issue: 1 year: 2014 ident: 10.1016/j.jhydrol.2022.128394_b0040 article-title: Inference on stochastic time-varying coefficient models publication-title: J. Econometrics. doi: 10.1016/j.jeconom.2013.10.009 – volume: 2011 start-page: 6585 year: 2011 ident: 10.1016/j.jhydrol.2022.128394_b0060 article-title: EEG signal classification using time-varying autoregressive models and common spatial patterns. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society publication-title: Annual International Conference. – volume: 819 year: 2022 ident: 10.1016/j.jhydrol.2022.128394_b0190 article-title: Evaluation of the impacts of human activities on propagation from meteorological drought to hydrological drought in the Weihe River Basin publication-title: China. Science of The Total Environment. doi: 10.1016/j.scitotenv.2022.153030 – volume: 29 start-page: 2960 issue: 7 year: 2018 ident: 10.1016/j.jhydrol.2022.128394_b0110 article-title: Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG publication-title: IEEE T. Neur. Net. Lear. – volume: 9 issue: 738556 year: 2021 ident: 10.1016/j.jhydrol.2022.128394_b0150 article-title: Research on Adaptive Hybrid Energy Consumption Model Based on Data Driven under Variable Working Conditions. Frontiers in Energy publication-title: Research. – volume: 34 start-page: 15 issue: 1 year: 2020 ident: 10.1016/j.jhydrol.2022.128394_b0070 article-title: The research of superheated steam temperature control based on generalized predictive control algorithm and adaptive forgetting factor publication-title: Int. J. Adapt. Control. doi: 10.1002/acs.3066 – volume: 88 start-page: 806 issue: 4 year: 2020 ident: 10.1016/j.jhydrol.2022.128394_b0015 article-title: Using the time-varying autoregressive model to study dynamic changes in situation perceptions and emotional reactions publication-title: J. Pers. doi: 10.1111/jopy.12528 – volume: 193 start-page: 106 year: 2016 ident: 10.1016/j.jhydrol.2022.128394_b0105 article-title: A multiwavelet-based time-varying model identification approach for time-frequency analysis of EEG signals publication-title: Neurocomputing. doi: 10.1016/j.neucom.2016.01.062 – volume: 54 start-page: 159 issue: 1–3 year: 1992 ident: 10.1016/j.jhydrol.2022.128394_b0080 article-title: Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? publication-title: Journal of Econometrics. doi: 10.1016/0304-4076(92)90104-Y |
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| Snippet | •A TVAR model is introduced to predict groundwater depth.•The implementation of parameter estimation of the TVAR model is summarized.•The TVAR exhibits better... The nonstationarity of hydrological variables makes the application of autoregressive (AR) models challenging. Therefore, this study introduces a new... |
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| SubjectTerms | algorithms Basis functions Bayesian theory China Fading memory recursive least squares algorithm Groundwater depth prediction Improved Bayesian information criterion memory model validation prediction Time-varying autoregressive model water table |
| Title | A time-varying autoregressive model for groundwater depth prediction |
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