Ecosystem modeling using artificial neural networks: An archaeological tool. (April 2018)
- Record Type:
- Journal Article
- Title:
- Ecosystem modeling using artificial neural networks: An archaeological tool. (April 2018)
- Main Title:
- Ecosystem modeling using artificial neural networks: An archaeological tool
- Authors:
- Burry, Lidia Susana
Marconetto, Bernarda
Somoza, Mariano
Palacio, Patricia
Trivi, Matilde
D'Antoni, Hector - Abstract:
- Abstract: Prediction of past Normalized Difference Vegetation Index (paleo-NDVI) in Valle de Ambato (Catamarca, Argentina) in the periods of 550–650 and 1550–1650 CE was carried out to test the efficacy of Artificial Neural Network (ANN) to predict past environments for Archaeology. This work shows that both subtropical Yunga and xerophytic Chaqueña vegetations respond in contrasting fashion to changes in climate forcings. To predict the past an ANN perceptron multilayer model was used. Modern NDVI data and Tree-Ring data were obtained from NOAA-Paleoclimate, and other public sources. These data were used to train the model. Real data and predictions were close (Pearson correlation 0.83–0.90) and warranted the following step, hindcasting. Important paleo-NDVI fluctuations lasting 15 to 20 years were identified in both periods under study. The paleo-NDVI fluctuations in the earlier period were probably related to the unidentified eruption of 583. The fluctuations in the later period appear related to the eruption of 1600 of the Huaynaputina volcano (SW Peru). These findings suggest that the model accurately identified vegetation fluctuations in response to changes in the volcanic forcing. Hence, the ANNs may be considered as apt tools for modeling past environments in support of archaeology. Highlights: Artificial neural networks are useful tools for modeling past environments to support archaeology. NDVI hindcasting was made for the periods of 550–650 and 1550–1650 CE of NWAbstract: Prediction of past Normalized Difference Vegetation Index (paleo-NDVI) in Valle de Ambato (Catamarca, Argentina) in the periods of 550–650 and 1550–1650 CE was carried out to test the efficacy of Artificial Neural Network (ANN) to predict past environments for Archaeology. This work shows that both subtropical Yunga and xerophytic Chaqueña vegetations respond in contrasting fashion to changes in climate forcings. To predict the past an ANN perceptron multilayer model was used. Modern NDVI data and Tree-Ring data were obtained from NOAA-Paleoclimate, and other public sources. These data were used to train the model. Real data and predictions were close (Pearson correlation 0.83–0.90) and warranted the following step, hindcasting. Important paleo-NDVI fluctuations lasting 15 to 20 years were identified in both periods under study. The paleo-NDVI fluctuations in the earlier period were probably related to the unidentified eruption of 583. The fluctuations in the later period appear related to the eruption of 1600 of the Huaynaputina volcano (SW Peru). These findings suggest that the model accurately identified vegetation fluctuations in response to changes in the volcanic forcing. Hence, the ANNs may be considered as apt tools for modeling past environments in support of archaeology. Highlights: Artificial neural networks are useful tools for modeling past environments to support archaeology. NDVI hindcasting was made for the periods of 550–650 and 1550–1650 CE of NW Argentina. Ecosystem model showed capacity to identify changes in the paleo-NDVI. The paleo-NDVI changes have been related to volcanic eruptions of global influence. Different types of vegetation had different responses to the same climate forcing. … (more)
- Is Part Of:
- Journal of archaeological science. Volume 18(2018)
- Journal:
- Journal of archaeological science
- Issue:
- Volume 18(2018)
- Issue Display:
- Volume 18, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 18
- Issue:
- 2018
- Issue Sort Value:
- 2018-0018-2018-0000
- Page Start:
- 739
- Page End:
- 746
- Publication Date:
- 2018-04
- Subjects:
- Paleo-NDVI -- Hindcasting -- Artificial Neural Network -- Ecosystem modeling -- Argentina
Archaeology -- Periodicals
Archaeology -- Research -- Periodicals
930.1 - Journal URLs:
- http://www.sciencedirect.com/science/journal/2352409X ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.jasrep.2017.07.013 ↗
- Languages:
- English
- ISSNs:
- 2352-409X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11600.xml