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Calveiro, R. (2014) Modelling Biodiversity in Highway Stormwater Ponds. Göteborg : Chalmers University of Technology (Examensarbete - Institutionen för bygg- och miljöteknik, Chalmers tekniska högskola, nr: ).
BibTeX
@mastersthesis{
Calveiro2014,
author={Calveiro, Ricardo Francisco Hermida},
title={Modelling Biodiversity in Highway Stormwater Ponds},
abstract={The development of road infrastructures causes great disruptions in the biodiversity of the natural areas. The Norwegian Public Roads Administration is investigating the possibility of employing stormwater ponds for compensating the loss of biodiversity due to the construction of the E39 highway. To define the guidelines for the design of biodiversity-promoting stormwater ponds, a model predicting biodiversity in stormwater ponds based on abiotic and biotic factors is needed. The literature review performed in this thesis showed that specific examples regarding biodiversity prediction models are scarce. However, several modelling approaches were described and one of them was identified as the most suitable: the Machine Learning methods. Using this approach, a model for predicting biodiversity in stormwater ponds was constructed. The model was based on the monitoring data collected during a sampling campaign performed within the NORWAT project at the Norwegian Public Roads Administration. During the sampling campaign several stormwater ponds along several major roads near Oslo in Norway were studied. Due to the different number of samples for water and sediment quality, two different models were built. In order to measure biodiversity three indices were defined: Species richness, Shannon diversity index and inverse Simpson’s index. The models were feedforward Artificial Neural Networks trained with the backpropagation algorithm. The results showed that the prediction capabilities were rather poor in all the cases but one, which performed well. The two models that were built showed very similar performances. The performances were in accordance with other results found in literature. Out of the three biodiversity indices, the species richness presented the best performance. This model confirmed that the Machine Learning models can be useful for biodiversity prediction.},
publisher={Institutionen för bygg- och miljöteknik, Vatten Miljö Teknik, Chalmers tekniska högskola},
place={Göteborg},
year={2014},
series={Examensarbete - Institutionen för bygg- och miljöteknik, Chalmers tekniska högskola, no: },
keywords={Highway, Stormwater, Stormwater Pond, NORWAT, Ecology, Biodiversity, Machine Learning, Artificial Neural Network, E39, Norwegian Public Roads Administration, Statens Vegvesen, Chalmers Open Innovation Networks (COINS), infrastructures},
note={108},
}
RefWorks
RT Generic
SR Electronic
ID 236727
A1 Calveiro, Ricardo Francisco Hermida
T1 Modelling Biodiversity in Highway Stormwater Ponds
YR 2014
AB The development of road infrastructures causes great disruptions in the biodiversity of the natural areas. The Norwegian Public Roads Administration is investigating the possibility of employing stormwater ponds for compensating the loss of biodiversity due to the construction of the E39 highway. To define the guidelines for the design of biodiversity-promoting stormwater ponds, a model predicting biodiversity in stormwater ponds based on abiotic and biotic factors is needed. The literature review performed in this thesis showed that specific examples regarding biodiversity prediction models are scarce. However, several modelling approaches were described and one of them was identified as the most suitable: the Machine Learning methods. Using this approach, a model for predicting biodiversity in stormwater ponds was constructed. The model was based on the monitoring data collected during a sampling campaign performed within the NORWAT project at the Norwegian Public Roads Administration. During the sampling campaign several stormwater ponds along several major roads near Oslo in Norway were studied. Due to the different number of samples for water and sediment quality, two different models were built. In order to measure biodiversity three indices were defined: Species richness, Shannon diversity index and inverse Simpson’s index. The models were feedforward Artificial Neural Networks trained with the backpropagation algorithm. The results showed that the prediction capabilities were rather poor in all the cases but one, which performed well. The two models that were built showed very similar performances. The performances were in accordance with other results found in literature. Out of the three biodiversity indices, the species richness presented the best performance. This model confirmed that the Machine Learning models can be useful for biodiversity prediction.
PB Institutionen för bygg- och miljöteknik, Vatten Miljö Teknik, Chalmers tekniska högskola,
T3 Examensarbete - Institutionen för bygg- och miljöteknik, Chalmers tekniska högskola, no:
LA eng
LK http://publications.lib.chalmers.se/records/fulltext/236727/236727.pdf
OL 30
