scholarly journals Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method

2021 ◽  
Vol 26 (3) ◽  
pp. 207-214
Author(s):  
Anisa Nur Azizah ◽  
Dian C.R. Novitasari ◽  
Putroue Keumala Intan ◽  
Fajar Setiawan ◽  
Ghaluh Indah Permata Sari
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Sea Water ◽  
Salinity Level ◽  
Series Data ◽  
Precipitation Data ◽  
Backpropagation Neural Network ◽  
Seawater Salinity ◽  
Salinity Data ◽  
Hidden Layer

Salinity is the level of salt dissolved in water. The salinity level of seawater can affect the hydrological balance and climate change. The salinity level of seawater in each area varies depending on the influencing factors, that is evaporation and precipitation (rainfall). One way to find out the salinity level is by taking seawater samples, which requires a long time and costs a lot. In this study, the salinity level of seawater can be predicted by utilizing time series data patterns from evaporation and precipitation using artificial neural network learning, namely the backpropagation neural network. The evaporation and precipitation data used were derived from the ECMWF dataset, while the salinity data were derived from NOAA where each data was taken at the coordinate point of 9,625 113,625 in the south of Java island. Seawater salinity, evaporation, and precipitation data were formed into a 7-day time series data. This study conducted several backpropagation architectural experiments, that is the learning rate, hidden layer, and the number of nodes in the hidden layer to obtain the best results. The results of the seawater salinity prediction were obtained at a MAPE value of 2.063% with a model architecture using 14 input layers, 2 hidden layers with 10 nodes and 2 nodes, 1 output layer, and a learning rate of 0.7. Predicted sea water salinity data ranging from 33 to 35 ppt. Therefore, the prediction system for seawater salinity using the backpropagation method can be said to be good in providing information about the salinity level of sea water on the island of Java.

scholarly journals PERAMALAN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK

2018 ◽  
Vol 7 (3) ◽  
pp. 264
Author(s):  
MADE NITA DWI SAWITRI ◽  
I WAYAN SUMARJAYA ◽  
NI KETUT TARI TASTRAWATI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Rice Price ◽  
Network Method ◽  
Rice Prices ◽  
Artificial Neural ◽  
The City

The purpose of the study is to forecast the price of rice in the city of Denpasar in 2017 using backpropagation neural network method. Backpropagation neural network is a model of artificial neural network by finding the optimal weight value. Artificial neural networks are information processing systems that have certain performance characteristics similar to that of human neural networks. This analysis uses time series data of rice prices in the city of Denpasar from January 2001 until December 2016. The results of this research, concludes that the lowest rice price is predicted in July 2017 at Rp9791.5 while the highest rice price in April 2017 for Rp9839.4.

scholarly journals Prediksi Kedatangan Turis Asing ke Indonesia Menggunakan Backpropagation Neural Networks

2016 ◽  
Vol 4 (4) ◽  
pp. 485
Author(s):  
Haviluddin Haviluddin ◽  
Zainal Arifin ◽  
Awang Harsa Kridalaksana ◽  
Dedy Cahyadi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Square Error ◽  
Time Series Data ◽  
Alternative Methods ◽  
Series Data ◽  
Mean Square ◽  
Backpropagation Neural Network ◽  
Forecasting Accuracy ◽  
The Mean

In this paper, a backpropagation neural network (BPNN) method with time series data have been explored. The BPNN method to predict the foreign tourist’s arrival to Indonesia datasets have been implemented. The foreign tourist’s arrival datasets were taken from the center agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers were able to forecast foreign tourist’s arrival to Indonesia. Where, the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable to producing good value of forecasting.Keywords - BPNN; foreign tourists; BPS; MSEPemanfaatan backpropagation neural network (BPNN) dengan data deret waktu telah digunakan dalam paper ini. Metode BPNN telah digunakan untuk memprediksi data kedatangan turis asing ke Indonesia, dimana data turis tersebut diambil dari badan pusat statistik Indonesia (BPS). Hasil pengujian menunjukkan bahwa metode BPNN dengan dua lapisan tersembunyi mampu memodelkan dan meramalkan data kedatangan turis asing ke Indonesia yang diindikasikan dengan nilai mean square error (MSE). Penelitian ini merekomendasikan bahwa metode BPNN mampu menjadi alternative metode dalam memprediksi data yang berjenis deret waktu karena metode BPNN efektif dan lebih mudah digunakan serta mampu menghasilkan akurasi nilai peramalan yang baik.

scholarly journals Pendugaan Tinggi Pasang Surut Laut Harian Menggunakan Jaringan Syaraf Tiruan Metode Backpropagation

2017 ◽  
Vol 8 (02) ◽  
pp. 70-82
Author(s):  
Eko Suryana
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Time Series Data ◽  
Historical Data ◽  
Series Data ◽  
Processing Step ◽  
Lead Levels ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Initial Process

Artificial Neural Network Model with methods Backrpopagation authors try to be implemented in the estimation or prediction of high tidal waters in Bengkulu. Neural Network architecture that is built consisting of three layers namely input layer, hidden layer and output layer. Estimation tidal itself always relies on historical data of the place concerned. A person can not make a prediction of ocean tides in a particular region in the absence of historical data in the region in a time sequence. Time series data is used as a basis of the estimation of data so as to recognize the tidal patterns that occur which in turn can be used as a reference to estimate the number of ups and downs that will occur. In this study, preprocessing or initial process carried out by the method of Backpropagation, where there are several steps that lead levels that this initial process can be carried out with the best. Changing the data type in the desired interval by Artificial Neural Networks will determine the next processing step. The sequence of steps taken in this prepocessing is selecting the data, data cleaning, transformation of data

scholarly journals PENERAPAN METODE BACKPROPAGATION UNTUK MEMPREDIKSI JUMLAH KUNJUNGAN WISATAWAN BERDASARKAN TINGKAT HUNIAN HOTEL

JURTEKSI ◽  
2018 ◽  
Vol 4 (2) ◽  
pp. 115-122
Author(s):  
Romy Aulia
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Random Process ◽  
Time Series Data ◽  
Prediction Method ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Input Variables

Tourism is one of the most important factors for the revenue of an area. To attract the interest of tourists takes some supporting factors, one of which is the hotel or the guesthouse. In the process of prediction of tourists visit, required data the number of tourists staying in order to predict for the next time. The prediction method used in this system is a method of Backpropagation Neural Network based on time series data forecasting component variansi random or random process beginning with autocorrelation for determination of input variables. Method of Backpropagation itself is known quite well used in the forecasting of time series data. The result of the method of Backpropagation is a number of predictions that tourists visit can be a reference for related officials in taking decisions for the period ahead.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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