Biology and environment (8 articles)

Establishing uncertainty ranges of hydrologic indices across climate and physiographic regions of the Congo River basin

Kabuya, P. M., Hughes, D. A., Tshimanga, R. M., Trigg, M. A., & Bates, P. (2020). Journal of Hydrology: Regional Studies, 30, 100710.

This study aims to determine uncertainty ranges for hydrologic indices in the Congo River basin and to provide a basis for transferring them from gauged to ungauged sub-basins. Viscovery SOMine is used to identify combinations of climate and physiographic attributes that correspond to certain runoff profiles.

European strategies for adaptation to climate change with the Mayors Adapt Initiative by self-organizing maps

Abarca-Alvarez, F. J., Navarro-Ligero, M. L., Valenzuela-Montes, L. M., & Campos-Sánchez, F. S. (2019). Applied Sciences, 9(18), 3859.

On the basis of the European "Mayors Adapt Initiative" dataset, various municipal strategies for adaptation to climate change are analyzed. Viscovery SOMine is used to find the profiles of municipalities with respect to the focus of their best practices, indicating that their geographical location is a major factor in adopting a particular strategy.

Modeling the ecological footprint of nations via evolutionary computation and machine learning models

Mostafa, M. M. (2011). In Environmental Modeling for Sustainable Regional Development: System Approaches and Advanced Methods (pp. 18-37). IGI Global.

This article uses various machine learning techniques to reveal dependences between economic and sociodemographic indicators of nations and their ecological footprints per capita. Viscovery SOMine is used for an explorative cluster analysis to reveal non-linearities and to complement supervised techniques such as gene expression programming and Bayesian linear regression.

Computer aided identification of biological specimens using self-organizing maps

Dean, E. J. (2003).

This master thesis is concerned with the correct classification of different Acacia species. Self-organizing maps were chosen for this purpose, since they handle partial information particularly well. Viscovery SOMine is used throughout the thesis to calculate several classification models and analyze the connection in taxonomy of Acacia species.

Clustering the ecological footprint of nations using Kohonen’s self-organizing maps

Mostafa, M. M. (2010). Expert Systems with Applications, 37(4), 2747-2755.

140 countries are clustered to identify sociodemographic and economic indicators affecting the magnitude of the ecological footprints per capita. Applying Viscovery SOMine's SOM-Ward algorithm, the author finds that geographical position, GDP, urbanization level, export rate, rate of GDP created by services and literacy rate are major correlates with the ecological footprint.

Self-organising map methods in integrated modelling of environmental and economic systems

Shanmuganathan, S., Sallis, P., & Buckeridge, J. (2006). Environmental Modelling & Software, 21(9), 1247-1256.

The paper elaborates on how self-organizing map methodologies using Viscovery SOMine within the connectionist paradigms of artificial neural networks could be applied to disparate data analysis at two different scales of environmental and economic systems: regional (using river-water quality-monitoring data to evaluate ecosystem response to human influence) and global (for modeling environmental and economic system data and trade-off analysis) within an integrated framework to inform sustainable environment management.

Soft systems analysis of ecosystems

Shanmuganathan, S. (2004).

This PhD thesis investigates possibilities of exploratory data analysis of highly diverse and complex ecosystems with self-organizing maps. Viscovery SOMine is used to analyze water quality indicators and their impact on small marine organisms, changes in the atmospheric concentrations of greenhouse and ozone-depleting gases, as well as land use patterns of nations and their effects on biodiversity.

Individuality of wing patterning in giant honey bees (Apis laboriosa)

Kastberger, G., Radloff, S., & Kranner, G. (2003). Apidologie, 34(3), 311-318.

This study investigates whether individual worker bees of a single Apis laboriosa colony can be re-identified by their wing patterns alone. Re-identification is carried out by self-organizing-map (SOM) reclassification and conventional discriminant analysis (DA) using the protocols of recognition (data for training and testing the model are equal or slightly modified by white noise), and prediction (test data are unknown to the model). SOM recognition of wing shaping is found to be more robust than that resulting from DA. The SOM prediction capacity is tested using four test-training data ratios and reaches 90% under a two-step reclassification protocol.

Visualization of multiple influences on ocellar flight control in giant honeybees with the data-mining tool Viscovery SOMine

Kastberger, G., & Kranner, G. (2000). Behavior Research Methods, Instruments, & Computers, 32(1), 157-168.

Viscovery SOMine is used to analyze and visualize multiple influences of the ocellar system on free-flight behavior in giant honeybees. Occlusion of ocelli affects orienting reactivities in relation to flight target, level of disturbance, and position of the bee in the flight chamber; occlusion induces phototaxis and renders orienting imprecise and dependent on motivational settings. Ocelli permit the adjustment of orienting strategies to environmental demands by enforcing abilities, such as centering or flight kinetics, and by providing independent control of posture and flight course.