Neural Approach to Predict Flow Discharge in River Chenab Pakistan

2016 ◽  
Vol 20 (5) ◽  
pp. 730-734 ◽  
Author(s):  
Tanzila Saba ◽  
Keyword(s):  
Neural Network ◽  
Water Flow ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Trial And Error ◽  
The Neural Network ◽  
Daily Discharge ◽  
Intelligent Model ◽  
Standard Learning

River water flow forecast in general and particularly in floods is of worth importance for monitoring operations of floods in canals and rivers. Floods in rivers bring destructions to road, houses, crops and causes human dislocation. The River Chenab is one of the largest rivers in Pakistan and has a historical recording of heavy floods. Prior to heavy floods, in time warning is mandatory to save lives and property. Accordingly, this paper presents an intelligent model to predict an advance alarming water flow from Chenab River. Standard learning algorithm is applied to train the ANN for this task. Inputs to the neural network are taken from the daily discharge values and the output layer composed of four neurons to represent number of predicted days. Moreover, trial and error approach is adopted to select appropriate number of inputs for time-series data. Two different architecture (single and double hidden layers) of neural network are evaluated and compared to find the most suitable one. Additionally, two activation functions are tested. The results thus achieved reveal well in time warning to the surroundings to secure flood victims. However, during low discharge, neural network miscalculated.

International Real Estate Review

2004 ◽  
Vol 7 (1) ◽  
pp. 121-138
Author(s):  
Xin J. Ge ◽  
◽  
G. Runeson ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hong Kong ◽  
Time Series Data ◽  
Series Data ◽  
Residential Property ◽  
Neural Network Approach ◽  
Price Performance ◽  
The Neural Network ◽  
Property Price

This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

scholarly journals Deciphering anomalous heterogeneous intracellular transport with neural networks

2019 ◽  
Author(s):  
Daniel S Han ◽  
Nickolay Korabel ◽  
Runze Chen ◽  
Mark Johnston ◽  
Viki J. Allan ◽  
...  
Keyword(s):  
Neural Network ◽  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Intracellular Transport ◽  
Time Series Data ◽  
Series Data ◽  
Range Analysis ◽  
The Neural Network ◽  
Rescaled Range Analysis ◽  
Data Points

AbstractBiological intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behaviour. Characterisation of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity adequately. Here, we have developed a deep learning feedforward neural network trained on fractional Brownian motion, which provides a novel, accurate and efficient characterization method for resolving heterogeneous behaviour of intracellular transport both in space and time. Importantly, the neural network requires significantly fewer data points compared to established methods, such as mean square displacements, rescaled range analysis and sequential range analysis. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, we were able to interpret anomalous intracellular dynamics as fractional Brownian motion with a stochastic Hurst exponent.

scholarly journals PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH

2020 ◽  
Vol 9 (2) ◽  
pp. 135-142
Author(s):  
Di Mokhammad Hakim Ilmawan ◽  
Budi Warsito ◽  
Sugito Sugito
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Future Period ◽  
Bee Colony ◽  
Artificial Neural ◽  
Digital Assets

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

scholarly journals Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 1-26
Author(s):  
Nagaraj V. Dharwadkar ◽  
Anagha R. Pakhare ◽  
Vinothkumar Veeramani ◽  
Wen-Ren Yang ◽  
Rajinder Kumar Mallayya Math
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Fusion Process Neural Networks Classifier Oriented Time Series

2019 ◽  
Vol 16 (10) ◽  
pp. 4059-4063
Author(s):  
Ge Li ◽  
Hu Jing ◽  
Chen Guangsheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Feature Extraction ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Time Varying ◽  
Synthetic Control ◽  
Classification Feature

Based on the consideration of complementary advantages, different wavelet, fractal and statistical methods are integrated to complete the classification feature extraction of time series. Combined with the advantage of process neural networks that processing time-varying information, we propose a fusion classifier with process neural network oriented time series. Be taking advantage of the multi-fractal processing nonlinear feature of time series data classification, the strong adaptability of the wavelet technique for time series data and the effect of statistical features on the classification of time series data, we can achieve the classification feature extraction of time series. Additionally, using time-varying input characteristics of process neural networks, the pattern matching of timevarying input information and space-time aggregation operation is realized. The feature extraction of time series with the above three methods is fused to the distance calculation between time-varying inputs and cluster space in process neural networks. We provide the process neural network fusion to the learning algorithm and optimize the calculation process of the time series classifier. Finally, we report the performance of our classification method using Synthetic Control Charts data from the UCI dataset and illustrate the advantage and validity of the proposed method.

scholarly journals Toward an embedded system for gesture recognition based on artificial neural network using reconfigurable target (case study and review)

2020 ◽  
Vol 10 (4) ◽  
pp. 142-148
Author(s):  
Ahmad Reda ◽  
Tareq Alshoufi ◽  
Ahmed Bouzid ◽  
József Vásárhelyi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Embedded System ◽  
Time Series Data ◽  
Reference Signal ◽  
Series Data ◽  
The Neural Network ◽  
Human Arm ◽  
On Chip

With a view to create an intelligent remote control for robot movements, this article treats the study case of dataset creation using RSG (Reference Signal Generator). Using artificial intelligence, the device recognizes the gestures of an operator. Indeed, a neural network can classify time series data coming from accelerometers, and for a beginning 4 gestures are taken into consideration. The most challenging work is to build a reference dataset that is necessary for the learning process. To train the neural network, a huge amount of reference data should be created (hundreds of thousands of time-series vectors per gesture per sensor), which cannot be done manually by an operator. To overcome the issue, an RSG is created. This article also describes how a 1-DoF arm has been designed to emulate the behavior of the human arm doing gestures as well as the data acquisition system. The system is based on a software/hardware co-design implemented on Programmable System on Chip (PSoC).

scholarly journals Modeling Analysis and Comparision of Neural Network Simulation Based on ECM and LSTM

2021 ◽  
Vol 2068 (1) ◽  
pp. 012041
Author(s):  
Lingyun Duan ◽  
Ziyuan Liu ◽  
Wen Yu ◽  
Wei Chen ◽  
Dongyan Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Neural Network Simulation ◽  
Deep Learning Algorithm ◽  
Modeling Analysis ◽  
Simulation Based

Abstract Comparing the prediction effects of traditional econometric algorithm model and deep learning algorithm model, taking regional GDP as an example, two prediction models of ARMA-ECM and LSTM-SVR are established for prediction, and the prediction results of different models are compared and analyzed. The results show that there are some deviations in the prediction results of the two models, but the prediction trends are the same. The prediction accuracy of LSTM-SVR model will decrease significantly with the reduction of time series data samples, while ARMA-ECM model is not so sensitive.

scholarly journals Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

2021 ◽  
Vol 10 (2) ◽  
pp. 870-878
Author(s):  
Zainuddin Z. ◽  
P. Akhir E. A. ◽  
Hasan M. H.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
The State ◽  
Series Data ◽  
Machine Failure ◽  
Gated Recurrent Unit

Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.

scholarly journals SMOOTHING IN NEURAL NETWORK FOR UNIVARIAT TIME SERIES DATA FORECASTING

2020 ◽  
Vol 3 (1) ◽  
pp. 23-30
Author(s):  
Nurfia Oktaviani Syamsiah ◽  
Indah Purwandani
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Exponential Smoothing ◽  
Series Data ◽  
Data Set ◽  
Research Material ◽  
Linear Relationships ◽  
The Neural Network ◽  
Optimal Accuracy

Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.

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