Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

Tatomir

et al. 2020

Water

Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R2) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 × 10−4, and the values of R2 = 0.944 and MSE = 2.4 × 10−4 were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development.

Section: Nar Modelmentioning

confidence: 99%

Nonlinear Autoregressive Neural Networks to Predict Hydraulic Fracturing Fluid Leakage into Shallow Groundwater

Tatomir

et al. 2020

Water

International Journal of Antennas and Propagation

“…Some studies closely related to the model set out in this work are [7][8][9][10][11][12]. Wang et al [7] analysethe phenomenon of tuberculosis incidence by means of a hybrid ARIMA model and nonlinear autoregressive neural network and compare its estimates to those of a single ARIMA model. In [8], a hybrid seasonal ARIMA (SARIMA) and neurofuzzy system network is used to predict the monthly inflow of water, as it is an extremely important variable in water resource planning.…”

Section: Introductionmentioning

confidence: 99%

Hybrid ARIMA and Neural Network Modelling Applied to Telecommunications in Urban Environments in the Amazon Region

Lopes

Fraiha

Lima

et al. 2020

is study explores the use of a hybrid Autoregressive Integrated Moving Average (ARIMA) and Neural Network modelling for estimates of the electric field along vertical paths (buildings) close to Digital Television (DTV) transmitters. e work was carried out in Belém city, one of the most urbanized cities in the Brazilian Amazon and includes a case study of the application of this modelling within the subscenarios found in Belém. Its results were compared with the ITU recommendations P. 1546-5 and proved to be better in every subscenario analysed. In the worst case, the estimate of the model was approximately 65% better than that of the ITU. We also compared this modelling with a classic modelling technique: the Least Squares (LS) method. In most situations, the hybrid model achieved better results than the LS.

“…However, time series in real-world are often uncertain and complex [10], especially the epidemic time series [11]. It may contain both linear and nonlinear structures [12][13].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hand-Foot-and-Mouth Disease Cases Using a Wavelet-based SARIMA-NNAR Hybrid Model

Feng

Shuang

et al. 2020

Preprint

Background: Hand-foot-and-mouth disease(HFMD) is one of the most common diseases in children, which has high morbidity. Reliable forecasting is significant for prevention and control. Recently, hybrid models have been becoming popular and wavelet analysis has been widely used. Better prediction accuracy may be achieved with wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.Materials and methods: We fitted a wavelet-based SARIMA(seasonal autoregressive integrated moving average)-NNAR(neural network nonlinear autoregressive) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. At the same time, single SARIMA model, simplex NNAR model and pure SARIMA-NNAR hybrid model were established as well for comparison and estimation.Results: The wavelet-based SARIMA-NNAR hybrid model had an excellent performance whether in fitting or in forecasting compared to other models. Its fitted and forecasting time series were approximate to the actual observed time series.Conclusions: This wavelet-based SARIMA-NNAR hybrid model that we fitted is suitable for forecasting number of HFMD cases. It will facilitate prevention and control of HFMD.