Future projection and the sales of industrial wood in Turkey: artificial neural networks

Yıl: 2019 Cilt: 43 Sayı: 3 Sayfa Aralığı: 368 - 377 Metin Dili: İngilizce DOI: 10.3906/tar-1901-20 İndeks Tarihi: 07-05-2020

Future projection and the sales of industrial wood in Turkey: artificial neural networks

Öz:
In this study, it was aimed to determine artificial neural network models with different architectures using artificial neuralnetwork (ANN) methods used in future prediction studies in recent times and forecast the sales quantities of industrial wood in Turkeywith the help of models. The sales quantities of logs, mining poles, other industrial wood, pulpwood, fiber-chip wood, and the total ofthese five wood groups were analyzed separately. The data used in this study was obtained from the General Directorate of Forestryof Turkey and cumulative monthly data covering the period from January 2001 to December 2016 were used. The most suitable ANNmodels were determined using performance criteria such as mean absolute percentage error (MAPE), root mean square error (RMSE),and determination coefficient (R2). As a result, the R2 and MAPE values of the ANN models were found to be above 99% and below 6%,respectively. The ANNs can be used as a good tool in industrial wood sales forecasts.
Anahtar Kelime:

Konular: Ziraat Mühendisliği
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Allende H, Moraga C, Salas R (2002). Artificial neural networks in time series forecasting: a comparative analysis. Kybernetika 38: 685-707.
  • Anandhi V, Manicka CR, Parthiban KT (2012). Forecast of demand and supply of pulpwood using artificial neural network. International Journal of Computer Science and Telecommunications 3: 35-38
  • Akgül I (2003). Analysis of time series and ARIMA models. Turkey: Der Press.
  • Can M (2009). Forecasting with time series analysis in business. PhD, University of Istanbul, Istanbul, Turkey.
  • Cheng JH, Sun DW (2015). Recent applications of spectroscopic and hyperspectral imaging techniques with chemometric analysis for rapid inspection of microbial spoilage in muscle foods. Compr Rev Food Sci F 14: 478-490.
  • Çevik O (1999). Box-Jenkins method in time series analysis and an application on tourism data. PhD, University of Kırıkkale, Kırıkkale, Turkey.
  • Cuhadar M, Cogurcu İ, Kukrer C (2014). Modelling and forecasting cruise tourism demand to İzmir by different artificial neural network architectures. International Journal of Business and Social Research 4: 12-28.
  • Detienne KB, Detienne DH, Joshi SA (2003). Neural networks as statistical tools for business researchers. Organ Res Methods 6: 236- 265.
  • Elmas Ç (2011). Artificial intelligence applications: artificial neural network, fuzzy logic, genetic algorithm. Turkey: Seçkin Press.
  • Erilli NA, Eğrioğlu E, Yolcu U, Aladağ ÇH, Uslu VR (2010). Forecasting of Turkey inflation with hybrid of feed forward and recurrent artificial neural networks. Doğuş University Journal 11: 42-55.
  • GDF (2016). Production and marketing activities of wood-based forest products. , Ankara, Turkey: General Directorate of Forestry, Department of Management and Marketing. Güler D, Saner G, Naseri Z (2017). Forecasting of import quantities of oil seed plants by ARIMA and neural networks methods. Balkan and Near Eastern Journal of Social Sciences 3: 60-70.
  • Güngör İ, Kayacan MC, Korkmaz M (2004). Artificial neural networks use in the forecasting of industrial wood demand and comparison with different estimation methods. In: ORIE XXIV. National Congress 15–18 June; Adana, Turkey: Çukurova University.
  • Hamzaçebi Ç (2011). Artificial neural networks: use for forecasting and an application of matlab and neuro solutions. Turkey: Ekin Press. Kaastra I, Boyd M (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing 10: 215- 236.
  • Kaynar O, Taştan S (2009). Comparison of MLP artificial neural networks and the ARIMA model at the time series analysis. Erciyes University Journal of Faculty of Economics and Administrative Sciences 33: 161-172.
  • Kazemi M, Niknafs A, Ranjbar,V, Forouharfar A (2011). Application of neural networks in forecasting business and managerial processes in comparison with nonlinear models (case study: Iran’s wood industry). International Journal of Social and Economic Research 1: 220-225.
  • Kurt R, Karayılmazlar S, İmren E, Çabuk Y (2017). Forecasting by using artificial neural networks: Turkey’s paper and paperboard industry case. Bartın Orman Fakültesi Dergisi 19: 99-106.
  • Küçükönder H (2011). Artificial neural networks and an application in agricultural. PhD, University of Kahramanmaraş Sütçü İmam, Kahramanmaraş, Turkey.
  • Lewis CD (1982). Industrial and Business Forecasting Methods. London: Butterworths Press.
  • Özşahin Ş (2012). The use of an artificial neural network for modelling the moisture absorption and thickness swelling of oriented strand board. BioResources 7: 1053-1067.
  • Öztemel E (2006). Artificial Neural Network. Turkey: Papatya Press.
  • Palmer A, Montano JJ, Sese A (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Manage 27: 781-790.
  • Sevinçtekin E (2014). Implementing of artificial neural network to manufacturing sector. Master Thesis, University of Yıldız Technical, Istanbul, Turkey.
  • Sivaram M (2014). Modeling the price trends of teak wood using statistical and artificial neural network techniques. Electronic Journal of Applied Statistical Analysis 7: 180-198.
  • Shu C, Ouarda TBMJ (2007). Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resour Res 43: 1-12.
  • Tesha T, Kichonge B (2015). Analysis of Tanzanian biomass consumption using artificial neural network. Journal of Fundamentals of Renewable Energy and Applications 5: 1-7.
  • Tigkas G, Lefakis P, Ioannou K (2013). Evaluation of artificial neural networks as a model for forecasting consumption of wood products. International Journal of Data Analysis Techniques and Strategies 5: 38-48.
  • Tiryaki S, Aydın A (2014). An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials 62: 102-108.
  • Williams P, Norris K (2001). Near-infrared Technology: In The Agricultural and Food Industries. 2nd ed. USA : American Association of Cereal Chemists, St. Paul, Minn Witt SF, Witt CA (1992). Modeling and Forecasting Demand in Tourism. London: Academic Press.
  • Yıldırım İ, Özşahin Ş, Okan OT (2014). Prediction of non-wood forest products trade using artificial neural networks. J Agric Sci Technol 16: 1493-1504.
APA AKYÜZ I (2019). Future projection and the sales of industrial wood in Turkey: artificial neural networks. , 368 - 377. 10.3906/tar-1901-20
Chicago AKYÜZ ILKER Future projection and the sales of industrial wood in Turkey: artificial neural networks. (2019): 368 - 377. 10.3906/tar-1901-20
MLA AKYÜZ ILKER Future projection and the sales of industrial wood in Turkey: artificial neural networks. , 2019, ss.368 - 377. 10.3906/tar-1901-20
AMA AKYÜZ I Future projection and the sales of industrial wood in Turkey: artificial neural networks. . 2019; 368 - 377. 10.3906/tar-1901-20
Vancouver AKYÜZ I Future projection and the sales of industrial wood in Turkey: artificial neural networks. . 2019; 368 - 377. 10.3906/tar-1901-20
IEEE AKYÜZ I "Future projection and the sales of industrial wood in Turkey: artificial neural networks." , ss.368 - 377, 2019. 10.3906/tar-1901-20
ISNAD AKYÜZ, ILKER. "Future projection and the sales of industrial wood in Turkey: artificial neural networks". (2019), 368-377. https://doi.org/10.3906/tar-1901-20
APA AKYÜZ I (2019). Future projection and the sales of industrial wood in Turkey: artificial neural networks. Turkish Journal of Agriculture and Forestry, 43(3), 368 - 377. 10.3906/tar-1901-20
Chicago AKYÜZ ILKER Future projection and the sales of industrial wood in Turkey: artificial neural networks. Turkish Journal of Agriculture and Forestry 43, no.3 (2019): 368 - 377. 10.3906/tar-1901-20
MLA AKYÜZ ILKER Future projection and the sales of industrial wood in Turkey: artificial neural networks. Turkish Journal of Agriculture and Forestry, vol.43, no.3, 2019, ss.368 - 377. 10.3906/tar-1901-20
AMA AKYÜZ I Future projection and the sales of industrial wood in Turkey: artificial neural networks. Turkish Journal of Agriculture and Forestry. 2019; 43(3): 368 - 377. 10.3906/tar-1901-20
Vancouver AKYÜZ I Future projection and the sales of industrial wood in Turkey: artificial neural networks. Turkish Journal of Agriculture and Forestry. 2019; 43(3): 368 - 377. 10.3906/tar-1901-20
IEEE AKYÜZ I "Future projection and the sales of industrial wood in Turkey: artificial neural networks." Turkish Journal of Agriculture and Forestry, 43, ss.368 - 377, 2019. 10.3906/tar-1901-20
ISNAD AKYÜZ, ILKER. "Future projection and the sales of industrial wood in Turkey: artificial neural networks". Turkish Journal of Agriculture and Forestry 43/3 (2019), 368-377. https://doi.org/10.3906/tar-1901-20