Univariate Artificial Neural Network in Forecasting Demand of Low Cost House in Petaling Jaya

2012 ◽  
pp. 1-16 ◽  
Author(s):  
Norhisham Bakhary ◽  
Khairulzan Yahya ◽  
Chin Nam Ng
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Housing Demand ◽  
Percentage Error ◽  
Potential Applications ◽  
Low Cost Housing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kebelakangan ini ramai penyelidik mendapati ‘Artificial Neural Network’ (ANN) untuk digunakan dalam berbagai bidang kejuruteraan awam. Banyak aplikasi ANN dalam proses peramalan menghasilkan kejayaan. Kajian ini memfokuskan kepada penggunaan siri masa ‘Univariate Neural Network’ untuk meramalkan permintaan rumah kos rendah di daerah Petaling Jaya, Selangor. Dalam kajian ini, beberapa kes bagi sesi latihan dan ramalan telah dibuat untuk mendapatkan model terbaik bagi meramalkan permintaan rumah. Nilai RMSE yang paling rendah yang diperolehi bagi tahap validasi adalah 0.560 dan nilai MAPE yang diperolehi adalah 8.880%. Hasil kajian ini menunjukkan kaedah ini memberikan keputusan yang boleh diterima dalam peramalan permintaan rumah berdasarkan data masa lalu. Kata kunci: Univariate Neural Network, permintaan rumah kos rendah, RMSE, MAPE Recently researchers have found the potential applications of Artificial Neural Network (ANN) in various fields in civil engineering. Many attempts to apply ANN as a forecasting tool has been successful. This paper highlighted the application of Time Series Univariate Neural Network in forecasting the demand of low cost house in Petaling Jaya district, Selangor, using historical data ranging from February 1996 to Appril 2000. Several cases of training and testing were conducted to obtain the best neural network model. The lowest Root Mean Square Error (RMSE) obtained for validation step is 0.560 and Mean Absolute Percentage Error (MAPE) is 8.880%. These results show that ANN is able to provide reliable result in term of forecasting the housing demand based on previous housing demand record. Key words: Time Series Univariate Neural Network, low cost housing demand, RMSE, MAPE

scholarly journals Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5188
Author(s):  
Mitsugu Hasegawa ◽  
Daiki Kurihara ◽  
Yasuhiro Egami ◽  
Hirotaka Sakaue ◽  
Aleksandar Jemcov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Augmentation ◽  
Percentage Error ◽  
Pressure Sensitivity ◽  
Pressure Sensitive Paint ◽  
Pressure Sensitive ◽  
Artificial Neural ◽  
Paint Thickness ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

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 Artificial Neural Network for analyzing the chaotic time series motion: The case of the Lebanese GDP

2021 ◽  
Author(s):  
Jean-François Verne
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Phase Space ◽  
Regression Models ◽  
Chaotic Dynamic ◽  
Linear Regression Models ◽  
Large Fluctuations ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract In this paper, we propose to analyze the motion of the Lebanese GDP over the period 1950-2019. This macroeconomic aggregate reveals large fluctuations notably during the civil war period (1975-1990). By estimating the Lyapunov exponents with the Artificial Neural Network (ANN) procedure, we show that this series exhibits a strange attractor generated by a chaotic dynamic and we use the embedding procedure to shed in light the bizarre structure of such a series. Thus, the ANN method gives better results regarding prediction than other linear regression models and allows to fit with accuracy the chaotic motion followed by the Lebanese GDP in the phase space.

scholarly journals The Use of an Artificial Neural Network for Predicting the Gloss of Thermally Densified Wood Veneers

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Şükrü Özşahin ◽  
Hilal Singer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Densified Wood ◽  
Percentage Error ◽  
Angle Of Incidence ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

scholarly journals Mitigating Construction Deficiencies: An Impact Analysis for Low-Cost Housing Developments Utilizing Artificial Neural Network

2021 ◽  
Author(s):  
Ramene U. Lim ◽  
Dante L. Silva ◽  
Kevin Lawrence M. De Jesus
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Housing Development ◽  
Development Projects ◽  
Computational Tool ◽  
Housing Developments ◽  
Low Cost Housing ◽  
Artificial Neural ◽  
To Come

The aim of this study is to be able to come up with a supplemental project management policy guidelines and computational tool that will address the two major concerns in construction of low-cost housing, construction delays and workmanship defects. Through assessment of previous studies, factors causing delays and defects from the two major stakeholders involved in housing development projects were identified. With the use of the five-point Likert Scale in survey forms distributed to 60 professionals involved in housing development projects, factors were classified and identified according to its degree of impact on the overall construction efficiency. The statistics of these factors were organized and used to develop an Artificial Neural Network Model. The relative importance of the factors was measured using Garson’s Algorithm. The derived equations from the developed ANN Model were used in formulating the computational tool and supplemental policy guidelines that can now be used to evaluate the workmanship defects and delay ratings of different housing developments. The computational tool was tested by 10 experts with their current projects and was able to receive a 4.6 out of 5 rubric evaluation rating, showing the tool’s effectiveness in identifying and assessing the probability and impact of construction deficiencies on their projects.

scholarly journals Prediction of rubber vulcanization using an artificial neural network

2021 ◽  
Vol 75 (5) ◽  
pp. 277-283
Author(s):  
Jelena Lubura ◽  
Predrag Kojic ◽  
Jelena Pavlicevic ◽  
Bojana Ikonic ◽  
Radovan Omorjan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Dense Layer ◽  
Ann Model ◽  
Different Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

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 Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models

2020 ◽  
pp. 46-54
Author(s):  
Pradeep Mishra ◽  
Chellai Fatih ◽  
Deepa Rawat ◽  
Saswati Sahu ◽  
Sagar Anand Pandey ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Fuzzy Time Series ◽  
Short Term ◽  
Indian Health ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Impact

Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread.  In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.

Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series

2019 ◽  
Vol 13 (1) ◽  
pp. 25-35
Author(s):  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Chaotic System ◽  
Research Work ◽  
Euler Method ◽  
Open Loop ◽  
Training Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features, and can be simulated by nonlinear dynamic time series outputs of chaotic systems. This article presents the research work of chaotic system generator design based on artificial neural network (ANN), for studying the chaotic features of human brain dynamics. The ANN training performances of Nonlinear Auto-Regressive (NAR) model are evaluated for the generation and prediction of chaotic system time series outputs, based on varying the ANN architecture and the precision of the generated training data. The NAR model is trained in open loop form with 1,000 training samples generated using Lorenz system equations and the forward Euler method. The close loop NAR model is used for the generation and prediction of Lorenz chaotic time series outputs. The training results show that better training performance can be achieved by increasing the number of feedback delays and the number of hidden neurons, at the cost of increasing the computational load.

Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network

2019 ◽  
Vol 80 (2) ◽  
pp. 243-253 ◽  
Author(s):  
Qianqian Zhang ◽  
Zhong Li ◽  
Spencer Snowling ◽  
Ahmad Siam ◽  
Wael El-Dakhakhni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Time Series Analysis ◽  
Percentage Error ◽  
Flow Data ◽  
Series Analysis ◽  
Artificial Neural

Abstract Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount of incoming wastewater in WWTPs, wastewater engineers face challenges arising from numerous complexities and uncertainties, such as the nonlinear precipitation-runoff relationships in combined sewer systems, unpredictability due to aging infrastructure, and frequently inconsistent data quality. To address such challenges, a time series analysis model (i.e., the autoregressive integrated moving average, ARIMA) and an artificial neural network model (i.e., the multilayer perceptron neural network, MLPNN) were developed for predicting wastewater inflow. A case study of the Barrie Wastewater Treatment Facility in Barrie, Canada, was carried out to demonstrate the performance of the proposed models. Fifteen-minute flow data over a period of 1 year were collected, and the resampled daily flow data were used to train and validate the developed models. The model performances were examined using root mean square error, mean absolute percentage error, coefficient of determination, and Nash–Sutcliffe efficiency. The results indicate that both models provided reliable forecasts, while ARIMA showed a slightly better performance than MLPNN in this case study. The proposed models can provide useful decision support for the optimization and management of WWTPs.

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