Forecasting Tree Volume Growth using Artificial Neural Networks: The case of Cypresses Lusitania Species
| dc.contributor.advisor | Raimond, Kumudha (PhD) | |
| dc.contributor.author | Gemechu, Fikadu | |
| dc.date.accessioned | 2020-06-03T09:28:17Z | |
| dc.date.accessioned | 2023-11-18T12:45:06Z | |
| dc.date.available | 2020-06-03T09:28:17Z | |
| dc.date.available | 2023-11-18T12:45:06Z | |
| dc.date.issued | 2006-09 | |
| dc.description.abstract | Neural networks are computational models with the capacity to learn or generalize complex relationships that exist among data. Although there are different kinds of networks, the multi-layered feed-forward neural network is the most widely used network that is capable of representing non-linear functional mappings between inputs and outputs. The training of this network is accomplished by the method of error back-propagation In this paper, the feed-forward neural network with back propagation learning algorithm is presented for forecasting tree volume growth of Cupressus lustanica species. The data set for training the neural network was obtained from the Forestry Research Center (FRC). As an input to the neural network, the historical tree volumes are used to train the network. After training the network, the results of forecasting is evaluated using test data set. The result indicates that the model yields good prediction with independent test data set, providing about 86.7% correct forecasts within ±2cm3 of the observed values. This suggests that the neural network is a good candidate for forecasting future value of tree volume given properly and accurately measured historical data. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21410 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Forecasting Tree Volume Growth using Artificial Neural Networks: The case of Cypresses Lusitania Species | en_US |
| dc.type | Thesis | en_US |