scholarly journals Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

2012 ◽  
Vol 14 (3) ◽  
pp. 574-584 ◽  
Author(s):  
B. Bhattacharya ◽  
T. van Kessel ◽  
D. P. Solomatine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Series Data ◽  
Ann Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Temporal Prediction ◽  
Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

scholarly journals Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

2020 ◽  
Vol 68 (2) ◽  
pp. 143-147
Author(s):  
Abira Sultana ◽  
Murshida Khanam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Rice Production ◽  
Series Data ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

scholarly journals Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil

2007 ◽  
Vol 10 ◽  
pp. 67-76 ◽  
Author(s):  
P. S. Lucio ◽  
F. C. Conde ◽  
I. F. A. Cavalcanti ◽  
A. I. Serrano ◽  
A. M. Ramos ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Low Frequency ◽  
Reconstruction Method ◽  
Ann Model ◽  
Monthly Total ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
South Of Brazil

Abstract. Climatological records users, frequently, request time series for geographical locations where there is no observed meteorological attributes. Climatological conditions of the areas or points of interest have to be calculated interpolating observations in the time of neighboring stations and climate proxy. The aim of the present work is the application of reliable and robust procedures for monthly reconstruction of precipitation time series. Time series is a special case of symbolic regression and we can use Artificial Neural Network (ANN) to explore the spatiotemporal dependence of meteorological attributes. The ANN seems to be an important tool for the propagation of the related weather information to provide practical solution of uncertainties associated with interpolation, capturing the spatiotemporal structure of the data. In practice, one determines the embedding dimension of the time series attractor (delay time that determine how data are processed) and uses these numbers to define the network's architecture. Meteorological attributes can be accurately predicted by the ANN model architecture: designing, training, validation and testing; the best generalization of new data is obtained when the mapping represents the systematic aspects of the data, rather capturing the specific details of the particular training set. As illustration one takes monthly total rainfall series recorded in the period 1961–2005 in the Rio Grande do Sul – Brazil. This reliable and robust reconstruction method has good performance and in particular, they were able to capture the intrinsic dynamic of atmospheric activities. The regional rainfall has been related to high-frequency atmospheric phenomena, such as El Niño and La Niña events, and low frequency phenomena, such as the Pacific Decadal Oscillation.

scholarly journals Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network

2012 ◽  
Vol 14 (4) ◽  
pp. 974-991 ◽  
Author(s):  
Shouke Wei ◽  
Depeng Zuo ◽  
Jinxi Song
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
River Discharge ◽  
Prediction Accuracy ◽  
Hybrid Approach ◽  
Series Data ◽  
Discharge Time ◽  
Artificial Neural

This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.

scholarly journals Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds

2009 ◽  
Vol 73 (1-3) ◽  
pp. 49-59 ◽  
Author(s):  
Thomas J. Glezakos ◽  
Theodore A. Tsiligiridis ◽  
Lazaros S. Iliadis ◽  
Constantine P. Yialouris ◽  
Fotis P. Maris ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Time Series Data ◽  
Training Model ◽  
Series Data ◽  
Artificial Neural ◽  
Mountainous Watersheds

scholarly journals Aplikasi Artificial Neural Network (ANN) dalamPeramalan Time Series Data: StudiKasus Lama SinarMatahari

2018 ◽  
pp. 207
Author(s):  
Sulistyarini Sulistyarini
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Series Data ◽  
Marriage Ceremony ◽  
Library Research ◽  
Wedding Ceremony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper discusses wedding ceremony in Central Lombok village of Plambik, which is potential to be a cultural attraction that supports the development of tourism. Marriage ceremony in Plambik has a number of stages, which are not necessarily similar to those customly practiced by other groups of Sasak people. in order to hold a wedding ceremony. This paper aimed to explore merariq tradition which is uniquely held by Sasak community in Plambik.  Data of this research were collected through library research and interviews with Plambik natives. The data were then analyzed by comparing the documentary notes with the actual practices of merariq by Plambik villagers. The finding indicated unique features of merariq stages in Plambik.

scholarly journals Comparison and Performance Evaluation of Low Pass Filter Design for various samples using Artificial Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 74-79
Author(s):  
Suchi Sharma ◽  
Anjana Goen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Filter Design ◽  
Back Propagation Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Feed Forward Back Propagation ◽  
Window Method ◽  
Low Pass ◽  
Artificial Neural

To design any type of filters complex calculation is needed. But with the help of window method, it become simple. In this paper a Low pass filter is designed by window method with the help of Artificial Neural Network. Here, Hann window and Blackman window is used to design filter and Feed Forward Back Propagation algorithm has been taken for neural network. In this paper different data sets for cut off frequency are consider for training and testing purpose to get best results.

scholarly journals Using artificial neural network in simulating of the streamflow in the Srepok watershed

2016 ◽  
Vol 19 (2) ◽  
pp. 114-120 ◽  
Author(s):  
Khoi Nguyen Dao ◽  
Phuong Ai Huynh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Analysis ◽  
Input Data ◽  
Time Lag ◽  
Series Data ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, artificial neural network (ANN) model was used to simulate the streamflow in the Srepok watershed, Vietnam. Correlation analysis of time series for precipitation and streamflow was employed to determine input data for the ANN model. This result indicated a significant correlation up to 2 day time lag and 1 day time lag for the precipitation and streamflow series data, respectively. According to the correlation analysis, three ANN models including ANN1, ANN2, and ANN3 were investigated. A 3-year data record for the precipitation and streamflow was used for ANN training and testing. The result of ANN training and testing showed that the ANN2 with 3 input data (P(t), P(t-1), and Q(t- 1)) gave the best simulation (NSE = 0.95 for training period and NSE = 0.96 for testing period) comparing to those of ANN1 and ANN3. In addition, the comparison of ANNs showed that the increase of the input data did not offer the better result.

Artificial neural network forecasting of nonlinear Markov processes

2007 ◽  
Vol 85 (3) ◽  
pp. 279-294 ◽  
Author(s):  
Sean W Fleming
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
River Flow ◽  
Time Series Data ◽  
Series Data ◽  
Underlying Structure ◽  
Good Prediction ◽  
Data Set ◽  
Artificial Neural

I assessed the performance characteristics of the feed-forward artificial neural network (ANN) as a first-order nonlinear Markov modelling technique. The ability to recover the underlying structure of five synthetic random time series was first tested. The method was then applied to an observed geophysical time series, and the results were compared against external empirical constraints and a simple representation of the underlying physics. The Monte Carlo experiments suggested that the ANN–Markov technique: (i) yields good prediction skill; (ii) in general, accurately retrieves the form of the iterative mapping, even for extremely noisy data; (iii) accomplishes the foregoing without any need to consider or adjust for the distributional characteristics of the data or driving noise; and (iv) accurately estimates the distribution of the strictly stochastic signal component. Application to a historical river-flow record again showed good forecast skill. Moreover, the robustness, flexibility, and simplicity of the method permitted easy identification of the fundamental nonlinear physical dynamics of this environmental system directly from the time series data, perhaps belying the common perception of ANNs as a strictly black-box prediction technique. The ANN–Markov technique may thus serve as a valuable data-driven tool for guiding the development of both process-based and parameteric statistical models. The lack of specific distributional assumptions and requirements notwithstanding, it was also found that manual distributional transformations may permit the method to be tuned to particular applications by emphasizing or de-emphasizing certain features of the data. Drawbacks to the method include substantial data-set length requirements, a general limitation of ANNs, as well as an inconsistent but potentially troubling tendency to partially imprint the form of the ANN activation function upon the estimated recursion relationship. PACS Nos.: 02.50.Ga, 05.10.–a, 05.45.Tp, 07.05.Mh, 02.50.Ey, 92.40.Fb

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