QUANTIFYING IMPACT OF DROUGHTS ON BARLEY YIELD IN NORTH DAKOTA, USA USING MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK

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ODABAŞ M. S., Leelaruban N., Simsek H., Padmanabhan G.

NEURAL NETWORK WORLD, vol.24, no.4, pp.343-355, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 4
  • Publication Date: 2014
  • Doi Number: 10.14311/nnw.2014.24.020
  • Journal Name: NEURAL NETWORK WORLD
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.343-355
  • Keywords: Barley yield, multiple linear regression, artificial neural network, drought impact, CLIMATE-CHANGE, LOGISTIC-REGRESSION, CHLOROPHYLL CONTENT, SOYBEAN YIELD, LEAVES, SCALE, CORN
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

This research investigated the effect of different drought conditions on Barley (Hordeum vulgare L.) yield in North Dakota, USA, using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) methods. Though MLR method is widely used, the ANN method has not been used in the past to investigate the effect of droughts on barley yields to the best of authors' knowledge. It is found from this study that the ANN model performs better than MLR in estimating barley yield. In this paper, the ANN is proposed as a viable alternative method or in combination with MLR to investigate the impact of droughts on crop yields.