Scaled UKF–NARX hybrid model for multi-step-ahead forecasting of chaotic time series data

Abdulkadir, Said Jadid; Yong, Suet-Peng

doi:10.1007/s00500-015-1833-z

Cited by 23 publications

(7 citation statements)

References 39 publications

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“…NARX networks are a special type of RNNs that describe the modeled process based on a lagged input-output variable. This feature of the NARX network makes it an efficient tool for modeling nonlinear systems [24,25]. NARX networks combine ANNs with ARX (autoregressive models with exogenous input) which is a popular statistical technique for time series analysis and modeling.…”

Section: Non-linear Autoregressive Network With Exogenous Inputmentioning

confidence: 99%

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

Alsumaiei

2020

Water

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The application of a nonlinear autoregressive modeling approach with exogenous input (NARX) neural networks for modeling groundwater level fluctuation has been examined by several researchers. However, the suitability of NARX in modeling groundwater level dynamics in urbanized and arid aquifer systems has not been comprehensively investigated. In this study, a NARX-based modeling approach is presented to establish a robust water management tool to aid urban water managers in controlling the development of shallow water tables induced by artificial recharge activity. Temperature data series are used as exogenous inputs for the NARX network, as they better reflect the intensity of artificial recharge activities, such as excessive lawns irrigation. Input delays and feedback delays for the NARX networks are determined based on the autocorrelation and cross-correlation analyses of detrended groundwater levels and monthly temperature averages. The validation of the proposed approach is assessed through a rolling validation procedure. Four observation wells in Kuwait City are selected to test the applicability of the proposed approach. The results showed the superiority of the NARX-based approach in modeling groundwater levels in such an urbanized and arid aquifer system, with coefficient of determination (R2) values ranging between 0.762 and 0.994 in the validation period. Comparison with other statistical models applied to the same study area shows that NARX models presented here reduced the mean absolute error (MAE) of groundwater levels forecasts by 50%. The findings of this paper are promising and provide a valuable tool for the urban city planner to assist in controlling the problem of shallow water tables for similar climatic and aquifer systems.

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Section: Non-linear Autoregressive Network With Exogenous Inputmentioning

confidence: 99%

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

Alsumaiei

2020

Water

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“…In Equation (18), TP is when the actual and predicted output are both 1; TN is when the actual and predicted output are both 0; FP is when the actual output is 0, but the predicted output is 1; and FN is when the actual output is 1, but the predicted output is 0. The result of the 1D-CNN models for each set in Table 2 is shown in Section 3.…”

Section: Model Evaluation and Validationmentioning

confidence: 99%

“…Deep learning has become the most vital discovery and research hotspot, which can tackle intricate patterns in a massive dataset of any data type [13]. It is not only implemented to model the prediction or classification problem, but it can also be applied for modelling forecasting time-series data [14][15][16][17][18][19][20]. It can automatically apply feature extraction where a separate implementation is needed for shallow machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

One-Dimensional Convolutional Neural Network with Adaptive Moment Estimation for Modelling of the Sand Retention Test

et al. 2021

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Stand-alone screens (SASs) are active sand control methods where compatible screens and slot sizes are selected through the sand retention test (SRT) to filter an unacceptable amount of sand produced from oil and gas wells. SRTs have been modelled in the laboratory using computer simulation to replicate experimental conditions and ensure that the selected screens are suitable for selected reservoirs. However, the SRT experimental setups and result analyses are not standardized. A few changes made to the experimental setup can cause a huge variation in results, leading to different plugging performance and sand retention analysis. Besides, conducting many laboratory experiments is expensive and time-consuming. Since the application of CNN in the petroleum industry attained promising results for both classification and regression problems, this method is proposed on SRT to reduce the time, cost, and effort to run the laboratory test by predicting the plugging performance and sand production. The application of deep learning has yet to be imposed in SRT. Therefore, in this study, a deep learning model using a one-dimensional convolutional neural network (1D-CNN) with adaptive moment estimation is developed to model the SRT with the aim of classifying plugging sign (screen plug, the screen does not plug) as well as to predict sand production and retained permeability using a varying sand distribution, SAS, screen slot size, and sand concentration as inputs. The performance of the proposed 1D-CNN model for the slurry test shows that the prediction of retained permeability and the classification of plugging sign achieved robust accuracy with more than a 90% value of R2, while the prediction of sand production achieved 77% accuracy. In addition, the model for the sand pack test achieved 84% accuracy in predicting sand production. For comparative model performance, gradient boosting (GB), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were also modelled on the same datasets. The results showed that the proposed 1D-CNN model outperforms the other four machine learning models for both SRT tests in terms of prediction accuracy.

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“…Barzegar et al (2017) compared the ability of wavelet group data handling and extreme learning machines to forecast GW level three months ahead, concluding that the best performances can be obtained by the latter. Guzman et al (2017) and Wunsch et al (2018) forecasted daily GW level variations in a well in the Mississippi River Valley aquifer and Germany by using nonlinear autoregressive neural networks (NARX). Their results showed the potential of NARX to predict GW levels effectively.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands

Amaranto

Pianosi

Solomatine

et al. 2020

Journal of Hydrology

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In the last decades, advancements in computational science have greatly expanded the use of artificial neural networks (ANNs) in hydrogeology, including applications on groundwater forecast, variable selection, extended lead-times, and regimespecific analysis. However, ANN-model performance often omits the sensitivity to observational uncertainties in hydroclimate forcings. The goal of this paper is to digitalcommons.unl.edu

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Scaled UKF–NARX hybrid model for multi-step-ahead forecasting of chaotic time series data

Cited by 23 publications

References 39 publications

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

One-Dimensional Convolutional Neural Network with Adaptive Moment Estimation for Modelling of the Sand Retention Test

Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands

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