title: A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

authors: Hamilton, David P.

subjects: DYRESM-CAEDYM

abstract: A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest.

text: A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest.

year: 2015

title: Gradient boosted trees for evolving data streams

authors: Gunasekara, Nuwan Amila

subjects: computer science

abstract: Gradient Boosting is a widely-used machine learning technique that has proven highly effective in batch learning. However, its effectiveness in stream learning contexts lags behind bagging-based ensemble methods, which currently dominate the field. One reason for this discrepancy is the challenge of adapting the booster to new concept following a concept drift. Resetting the entire booster can lead to significant performance degradation as it struggles to learn the new concept. Resetting only some parts of the booster can be more effective, but identifying which parts to reset is difficult, given that each boosting step builds on the previous prediction. To overcome these difficulties, we propose Streaming Gradient Boosted Trees (Sgbt), which is trained using weighted squared loss elicited in XGBoost. Sgbt exploits trees with a replacement strategy to detect and recover from drifts, thus enabling the ensemble to adapt without sacrificing the predictive performance. Our empirical evaluation of Sgbt on a range of streaming datasets with challenging drift scenarios demonstrates that it outperforms current state-of-the-art methods for evolving data streams.

text: Gradient boosted trees for evolving data streams Gradient Boosting is a widely-used machine learning technique that has proven highly effective in batch learning. However, its effectiveness in stream learning contexts lags behind bagging-based ensemble methods, which currently dominate the field. One reason for this discrepancy is the challenge of adapting the booster to new concept following a concept drift. Resetting the entire booster can lead to significant performance degradation as it struggles to learn the new concept. Resetting only some parts of the booster can be more effective, but identifying which parts to reset is difficult, given that each boosting step builds on the previous prediction. To overcome these difficulties, we propose Streaming Gradient Boosted Trees (Sgbt), which is trained using weighted squared loss elicited in XGBoost. Sgbt exploits trees with a replacement strategy to detect and recover from drifts, thus enabling the ensemble to adapt without sacrificing the predictive performance. Our empirical evaluation of Sgbt on a range of streaming datasets with challenging drift scenarios demonstrates that it outperforms current state-of-the-art methods for evolving data streams.

year: 2024

title: Indigenous peoples, data, and the coloniality of surveillance

authors: Cormack, Donna

abstract: In Aotearoa New Zealand (Aotearoa NZ), Māori (the Indigenous peoples of New Zealand) have long been objects of surveillance by state institutions and agents. State representations have centred on constructions of difference and deviance, on understandings of Indigenous peoples as dangerous, and on the management of Indigenous resistance to colonialism. This chapter considers how contemporary state surveillance practices in Aotearoa NZ, enabled by the expanded use of big data and linked government datasets, function to regulate and manage Māori. Through this lens, we explore continuities of current data practices for Indigenous peoples with the racialised logics and social orders set in place as part of global systems of imperialism and colonialism. Recognising that resistance has always been a part of Indigenous responses to colonialism, we also explore how Māori Data Sovereignty (MDSov), as part of broader Indigenous Data Sovereignty (IDS) movements globally, provides opportunities to counter and disrupt prevailing data relations and to imagine alternative futures.

text: Indigenous peoples, data, and the coloniality of surveillance In Aotearoa New Zealand (Aotearoa NZ), Māori (the Indigenous peoples of New Zealand) have long been objects of surveillance by state institutions and agents. State representations have centred on constructions of difference and deviance, on understandings of Indigenous peoples as dangerous, and on the management of Indigenous resistance to colonialism. This chapter considers how contemporary state surveillance practices in Aotearoa NZ, enabled by the expanded use of big data and linked government datasets, function to regulate and manage Māori. Through this lens, we explore continuities of current data practices for Indigenous peoples with the racialised logics and social orders set in place as part of global systems of imperialism and colonialism. Recognising that resistance has always been a part of Indigenous responses to colonialism, we also explore how Māori Data Sovereignty (MDSov), as part of broader Indigenous Data Sovereignty (IDS) movements globally, provides opportunities to counter and disrupt prevailing data relations and to imagine alternative futures.

year: 2022