scholarly journals Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

2020 ◽  
Vol 11 (21) ◽  
pp. 55-70
Author(s):  
Murat Cuhadar
Keyword(s):  
Demand Forecasting ◽  
Tourism Industry ◽  
Percentage Error ◽  
Accurate Estimation ◽  
Tourism Demand ◽  
Tourist Flows ◽  
Ann Models ◽  
Optimal Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

scholarly journals Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach

2018 ◽  
Vol 7 (4.30) ◽  
pp. 454 ◽  
Author(s):  
Diyana Izyan Amir Hamzah ◽  
Maria Elena Nor ◽  
Sabariah Saharan ◽  
Noor Fariza Mohd Hamdan ◽  
Nurul Asmaa Izzati Nohamad
Keyword(s):  
Demand Forecasting ◽  
Information Criterion ◽  
Research Paper ◽  
Tourism Industry ◽  
Percentage Error ◽  
Tourism Demand ◽  
Absolute Deviation ◽  
Correct Judgment ◽  
Accurate Forecast ◽  
Future Strategy

Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry.

Models of Artificial Neural Networks Applied to Demand Forecasting in Nonconsolidated Tourist Destinations

Methodology ◽  
2015 ◽  
Vol 11 (2) ◽  
pp. 35-44 ◽  
Author(s):  
Tomás Molinet Berenguer ◽  
José Antonio Molinet Berenguer ◽  
María Elena Betancourt García ◽  
Alfonso Palmer Pol ◽  
Juan José Montaño Moreno
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Ann Model ◽  
Tourist Destinations ◽  
Different Seasons ◽  
Auto Regressive ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

This article focuses on a new proposed artificial neural network (ANN) model for tourism demand forecasting using time-series which, unlike previous models, uses different seasons of arrivals and values of months with similar behavior as input variables and achieves a forecast up to a year in advance. We demonstrate the validity and greater precision of the proposed model in forecasting a nonconsolidated destination with marked seasonality, which has been scarcely dealt with in other research. We achieve a comparatively greater quality of results and a longer period in advance than previously used auto-regressive integrated moving average (ARIMA) and ANN models. Highly accurate results were also obtained in destinations such as Portugal, which also proves its validity for mature destinations.

scholarly journals Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN) and Supporting Vector Machine (SVR)

2016 ◽  
Vol 3 (1) ◽  
pp. 37
Author(s):  
Seyedehelham Sadatiseyedmahalleh ◽  
Nasim Heidari Bateni ◽  
Nazanin Heidari Bateni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tourism Industry ◽  
Support Vector ◽  
Tourism Demand ◽  
Tourism Research ◽  
Prediction Bias ◽  
Alternative Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This research examines and proves this effectiveness connected with artificial neural networks (ANNs) as an alternative approach to the use of Support Vector Machine (SVR) in the tourism research. This method can be used for the tourism industry to define the turism’s demands in Iran. The outcome reveals the use of ANNs in tourism research might result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand evaluation is needed to establish and validate the effects.

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 Data Preprocessing for Load Forecasting using Artificial Neural Network

2020 ◽  
Author(s):  
Rafael S. F. Ferraz ◽  
Renato S. F. Ferraz ◽  
Lucas F. S. Azeredo ◽  
Benemar A. de Souza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Demand Forecasting ◽  
Energy Resource ◽  
Data Preprocessing ◽  
Electric Power System ◽  
Percentage Error ◽  
Gradual Change ◽  
Artificial Neural

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the  preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

scholarly journals Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2787
Author(s):  
Ahmed Gowida ◽  
Salaheldin Elkatatny ◽  
Khaled Abdelgawad ◽  
Rahul Gajbhiye
Keyword(s):  
Rheological Properties ◽  
Drilling Fluid ◽  
Rheological Parameters ◽  
Percentage Error ◽  
Cleaning Efficiency ◽  
Periodic Monitoring ◽  
Data Points ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Marsh Funnel

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (YP), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.

scholarly journals Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market

2020 ◽  
Vol 12 (8) ◽  
pp. 3243
Author(s):  
Giovanni De Luca ◽  
Monica Rosciano
Keyword(s):  
Demand Forecasting ◽  
Tourism Industry ◽  
Tourism Demand ◽  
Campania Region ◽  
Travel And Tourism ◽  
Tourist Destinations ◽  
High Quantiles ◽  
International Tourist ◽  
Long Time ◽  
Hotel Accommodation

Travel and tourism is an important economic activity in most countries around the world. In 2018, international tourist arrivals grew 5% to reach the 1.4 billion mark and at the same time export earnings generated by tourism have grown to USD 1.7 trillion. The rapid growth of the tourism industry has globally attracted the interest of researchers for a long time. The literature has tried to model tourism demand to analyze the effects of different factors and predict the future behavior of the demand. Forecasting of tourism demand is crucial not only for academia but for tourism industries too, especially in line with the principles of sustainable tourism. The hospitality branch is an important part of the tourism industry and accurate passenger flow forecasting is a key link in the governance of the resources of a destination or in revenue management systems. In this context, the paper studies the interdependence of tourism demand in one of the main Italian tourist destinations, the Campania region, using a quantile-on-quantile approach between overall and specific tourism demand. Data are represented by monthly arrivals and nights spent by residents and non-residents in hotels and complementary accommodations from January 2008 to December 2018. The results of the analysis show that the hotel-accommodation component of the tourism demand appears to be more vulnerable than extra-hotel accommodation component to the fluctuations of the overall tourism demand and this feature is more evident for the arrivals than for nights spent. Moreover, the dependence on high quantiles suggests strategy of diversification or market segmentation to avoid overtourism phenomena and/or carrying capacity problems. Conversely, dependence on low quantiles suggests the use of push strategies to stimulate tourism demand. Finally, the results suggest that it could be very useful if the stakeholders of the tourism sector in Campania focused their attention on the collaboration theory.

scholarly journals Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 134-143 ◽  
Author(s):  
Tuan Nguyen Thanh ◽  
Tai Dinh Tien ◽  
Hai Long Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Northeast China ◽  
Tree Height ◽  
Pinus Koraiensis ◽  
Accurate Estimation ◽  
Korean Pine ◽  
Ann Models ◽  
Artificial Neural ◽  
Forest Farm

Korean pine (Pinus koraiensis Sieb. et Zucc.) is one of the highly commercial woody species in Northeast China. In this study, six nonlinear equations and artificial neural network (ANN) models were employed to model and validate height-diameter (H-DBH) relationship in three different stand densities of one Korean pine plantation. Data were collected in 12 plots in a 43-year-old even-aged stand of P. koraiensis in Mengjiagang Forest Farm, China. The data were randomly split into two datasets for model development (9 plots) and for model validation (3 plots). All candidate models showed a good perfomance in explaining H-DBH relationship with error estimation of tree height ranging from 0.61 to 1.52 m. Especially, ANN models could reduce the root mean square error (RMSE) by the highest 40%, compared with Power function for the density level of 600 trees. In general, our results showed that ANN models were superior to other six nonlinear models. The H-DBH relationship appeared to differ between stand density levels, thus it is necessary to establish H-DBH models for specific stand densities to provide more accurate estimation of tree height.

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