scholarly journals A non-linear neural network technique for updating of river flow forecasts

2001 ◽  
Vol 5 (4) ◽  
pp. 577-598 ◽  
Author(s):  
A. Y. Shamseldin ◽  
K. M. O’Connor
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
River Flow ◽  
Model Updating ◽  
Ar Model ◽  
Soil Moisture Accounting ◽  
Non Linear ◽  
Auto Regressive ◽  
Input Model ◽  
Updating Procedure

Abstract. A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments having different climatic conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure. The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure. Keywords: Auto-Regressive Exogenous-input model, neural network, output-updating procedure, soil moisture accounting and routing (SMAR) model

scholarly journals Artificial neural network model for river flow forecasting in a developing country

2009 ◽  
Vol 12 (1) ◽  
pp. 22-35 ◽  
Author(s):  
Asaad Y. Shamseldin
Keyword(s):  
River Flow ◽  
External Input ◽  
Rainfall Runoff ◽  
Rainfall Index ◽  
River Flow Forecasting ◽  
Auto Regressive ◽  
Input Model ◽  
Artificial Neural ◽  
Updating Procedure

The present paper deals with exploring the use of Artificial Neural Networks (ANN) for forecasting the Blue Nile river flows in Sudan. Four ANN rainfall–runoff models based on the structure of the well-known multi-layer perceptron are developed. These models use the rainfall index as a common external input, with the rainfall index being a weighted sum of the recent and current rainfall. These models differ in terms of the additional external inputs being used by the model. The additional inputs are basically the seasonal expectations of both the rainfall index and the observed discharge. The results show that the model, which uses both the seasonal expectation of the observed discharge and the rainfall index as additional inputs, has the best performance. The estimated discharges of this model are further updated using a non-linear Auto-Regressive Exogenous-input model (NARXM)-ANN river flow forecasting output-updating procedure. In this way, a real-time river flow forecasting model is developed. The results show that the forecast updating has significantly enhanced the quality of the discharge forecasts. The results also indicate that the ANN has considerable potential to be used for river flow forecasting in developing countries.

scholarly journals Forecasting Vegetation Condition with a Bayesian Auto-regressive Distributed Lags (BARDL) Model

2021 ◽  
Author(s):  
Edward E. Salakpi ◽  
Peter D. Hurley ◽  
James M. Muthoka ◽  
Adam B. Barrett ◽  
Andrew Bowell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Expert Knowledge ◽  
Research Work ◽  
Early Warning Systems ◽  
Ar Model ◽  
Lead Times ◽  
Vegetation Condition ◽  
Early Action ◽  
Auto Regressive

Abstract. Droughts form a large part of climate/weather-related disasters reported globally. In Africa, pastoralists living in the Arid and Semi-Arid Lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish Early Warning Systems (EWS) to save lives and livelihoods. Existing EWS use a combination of Satellite Earth Observation (EO) based biophysical indicators like the Vegetation Condition Index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWS rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like Auto-Regression, Gaussian Processes and Artificial Neural Networks can provide very skilled models for forecasting vegetation condition at short to medium range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. The objective of this research work is to develop models that forecast vegetation conditions at longer lead times on the premise that vegetation condition is controlled by factors like precipitation and soil moisture. To achieve this, we used a Bayesian Auto-Regressive Distributed Lag (BARDL) modelling approach which enabled us to factor in lagged information from Precipitation and Soil moisture levels into our VCI forecast model. The results showed a ∼2-week gain in the forecast range compared to the univariate AR model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85 and 0.74, compared to the AR model's R2 of 0.88, 0.77 and 0.65 for 6, 8 and 10 weeks lead time respectively.

Modelling and control of greenhouse system using neural networks

2016 ◽  
Vol 40 (3) ◽  
pp. 918-929 ◽  
Author(s):  
A Manonmani ◽  
T Thyagarajan ◽  
M Elango ◽  
S Sutha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Moving Average ◽  
Growth Conditions ◽  
Operating Conditions ◽  
Adverse Weather ◽  
Control Schemes ◽  
Non Linear ◽  
Auto Regressive ◽  
And Control

A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

scholarly journals Assessing the performance of eight real-time updating models and procedures for the Brosna River

2005 ◽  
Vol 9 (4) ◽  
pp. 394-411 ◽  
Author(s):  
M. Goswami ◽  
K. M. O'Connor ◽  
K. P. Bhattarai ◽  
A. Y. Shamseldin
Keyword(s):  
Neural Network ◽  
Linear Model ◽  
Lead Time ◽  
Ar Model ◽  
Linear Perturbation ◽  
Discharge Data ◽  
Perturbation Model ◽  
Simple Linear Model ◽  
Auto Regressive ◽  
Linear Perturbation Model

Abstract. The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

scholarly journals Genetic algorithm-based non-linear auto-regressive with exogenous inputs neural network short-term and medium-term uncertainty modelling and prediction for electrical load and wind speed

2018 ◽  
Vol 2018 (8) ◽  
pp. 721-729 ◽  
Author(s):  
Muhammad Jawad ◽  
Sahibzada M. Ali ◽  
Bilal Khan ◽  
Chaudry A. Mehmood ◽  
Umar Farid ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Speed ◽  
Electrical Load ◽  
Uncertainty Modelling ◽  
Medium Term ◽  
Short Term ◽  
Non Linear ◽  
Auto Regressive

Optimized Intelligent Auto-Regressive Neural Network Model (ARNN) for Prediction of Non-Linear Exogenous Signals

2021 ◽  
Author(s):  
Lubna Farhi ◽  
Agha Yasir
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Computational Cost ◽  
Error Component ◽  
Design Parameters ◽  
Non Linear ◽  
Auto Regressive ◽  
Comparison Of The Results ◽  
Intelligent Approach

Abstract The paper presents a prediction of non-linear exogenous signal by optimized intelligent auto-regressive neural network model (ARNN). A signal comprises of two sets of data called deterministic and error. The former type of data represents the degradation index of a signal, while the error is the uncertainties associated with the signal. To understand and predict signals, a intelligent approach is taken through the use of ARNN model. In this approach, the rst step is to diagnose whether a time series signal is normally distributed or not by utilizing the Jarque-Bera test. The high and low volatility data ele- ments can be separated via kurtosis hypothesis. The deterministic component of the signal is also predicted by developing a neural network based non-linear autoregressive model (NN-NARX) and the error component by using a linear model. The nal forecast is formed by combining the results determined from each of the models and evaluated using the mean square error results. Vali- dation of the prediction is obtained through a comparison of the results with other models such as ARNN, traditional ARMX, and NARX models. The re- sults show that the proposed model provides improved predictions, minimize high dependence on design parameters with low computational cost.

Prothesis Movements Pattern Recognition Based on Auto-Regressive Model and Wavelet Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 2156-2161 ◽  
Author(s):  
Cheng Gao ◽  
Jiao Ying Huang ◽  
Wei Guo
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Ar Model ◽  
Wavelet Neural Networks ◽  
Time Frequency ◽  
The Neural Network ◽  
Time Frequency Localization ◽  
Auto Regressive ◽  
The Right

Wavelet neural networks (WNN) combine the functions of time–frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. Based on auto-regressive (AR) model and WNN, pattern recognition of prothesis movements was studied in this paper. Firstly, an AR model was used to analysis the surface myoelectric signals (SMES) which recorded on the ulnar flexor carpi and extensor carpi region of the right hand in resting position. Four types of prosthesis movements are recognized by extracting four-order AR coefficient and construct them as eigenvector into WNN, which was used to study the correlation between SMES and wristwork. This paper compares the classification accuracy of four movements such as hand open (HO), hand close (HC), forearm intorsion (FI) and forearm extorsion (FE).The experimental results show that the proposed method can classify correctly for at least 93.75% of the test data.

scholarly journals Are Neural Network Models Truly Effective at Forecasting? An Evaluation of Forecast Performance of Traditional Models with Neural Network Model for the Macroeconomic Data of G-7 Countries

2020 ◽  
Vol 11 (3) ◽  
pp. 49-52
Author(s):  
Tayyab Raza Fraz ◽  
Samreen Fatima
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Economic Variables ◽  
Forecast Performance ◽  
Non Linear ◽  
Auto Regressive

Forecasting macroeconomic and financial data are always difficult task to the researchers. Various statisticaland econometrics techniques have been used to forecast these variables more accurately. Furthermore, in the presenceof structural break, linear models are failed to model and forecast. Therefore, this study examines the forecastingperformance of economic variables of G7 countries: France, Italy, Canada, Germany, Japan, United Kingdom andUnited States of America using non-linear autoregressive neural network (ARNN) model, linear auto regressive (AR)and Auto regressive integrated moving average model (ARIMA) models. The economic variables are inflation,exchange rate and Gross Domestic Product (GDP) growth for the period from 1970 to 2015. To measure theperformance of the considered model Root, Mean Square Error, Mean Absolute Error and Mean Absolute PercentageError are used. The results show that the forecasts from the non-linear neural network model are undoubtedly better ascompared to the AR and the Box–Jenkins ARIMA models.

scholarly journals Data-Driven Model Updating of an Offshore Wind Jacket Substructure

2021 ◽  
Author(s):  
Dawid Augustyn ◽  
Martin Dalgaard Ulriksen
Keyword(s):  
Parameter Estimation ◽  
Model Updating ◽  
Offshore Wind ◽  
Modal Parameters ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Modal Parameter ◽  
In Situ Data ◽  
Non Linear ◽  
Updating Procedure

The present paper provides a model updating application study concerning the jacket substructure of an o?shore wind turbine. Theupdating is resolved in a sensitivity-based parameter estimation setting, where a cost function expressing the discrepancy betweenexperimentally obtained modal parameters and model-predicted ones is minimized. The modal parameters of the physical systemare estimated through stochastic subspace identification (SSI) applied to vibration data captured for idling and operational states ofthe turbine. From a theoretical outset, the identification approach relies on the system being linear and time-invariant (LTI) and theinput white noise random processes; criteria which are violated in this application due to sources such as operational variability, theturbine controller, and non-linear damping. Consequently, particular attention is given to assess the feasibility of extracting modalparameters through SSI under the prevailing conditions and subsequently using these parameters for model updating. On this basis,it is deemed necessary to disregard the operational turbine states—which severely promote non-linear and time-variant structuralbehaviour and, as such, imprecise parameter estimation results—and conduct the model updating based on modal parametersextracted solely from the idling state. The uncertainties associated with the modal parameter estimates and the model parameters tobe updated are outlined and included in the updating procedure using weighting matrices in the sensitivity-based formulation. Byconducting the model updating based on in-situ data harvested from the jacket substructure during idling conditions, the maximumeigenfrequency deviation between the experimental estimates and the model-predicted ones is reduced from 30% to 1%.

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