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Tradition and the Individual Canadian Talent

Studia Anglica Posnaniensia, 2020-12, Vol.55 (1), p.253-271

Mount, Nick

2020

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A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C

Climatic change, 2017-04, Vol.141 (3), p.577-595

2017

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Participatory modelling for stakeholder involvement in the development of flood risk management intervention options

Environmental modelling & software : with environment data news, 2016-08, Vol.82, p.275-294

2016

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Doing flood risk modelling differently: Evaluating the potential for participatory techniques to broaden flood risk management decision‐making

Journal of flood risk management, 2022-03, Vol.15 (1), p.n/a

2022

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Improved validation framework and R-package for artificial neural network models

Environmental modelling & software : with environment data news, 2017-06, Vol.92, p.82-106

2017

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Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

Progress in physical geography, 2012-08, Vol.36 (4), p.480-513

2012

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Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

Environmental research letters, 2018-06, Vol.13 (6), p.65015

2018

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Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh

Geomorphology (Amsterdam, Netherlands), 2013-02, Vol.183, p.82-95

2013

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When Canadian literature moved to New York

When Canadian literature moved to New York, 2005

Mount, Nick

1, 2005

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Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

Hydrological processes, 2014-01, Vol.28 (3), p.1055-1070

2014

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Load or concentration, logged or unlogged? Addressing ten years of uncertainty in neural network suspended sediment prediction

Hydrological processes, 2011-09, Vol.25 (20), p.3144-3157

2011

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Erratum to: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C

Climatic change, 2017-04, Vol.141 (3), p.597-598

2017

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Discussion of ‘Reservoir Computing approach to Great Lakes water level forecasting’ by P. Coulibaly [J. Hydrol. 381(2010) 76–88]

Journal of hydrology (Amsterdam), 2012-02, Vol.422-423, p.76-80

2012

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DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

Journal of hydrology (Amsterdam), 2011-11, Vol.409 (3), p.596-611

2011

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The Return of Beauty

Queen's quarterly, 2019-12, Vol.126 (4), p.564-581

Mount, Nick

2019

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Neuroemulation: definition and key benefits for water resources research

Hydrological sciences journal, 2012-04, Vol.57 (3), p.407-423

2012

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Who Needs Books? Reading in the Digital Age. CLS Kreisel Lecture Series by Lynn Coady

University of Toronto quarterly, 2018-08, Vol.87 (3), p.284-286

Mount, Nick

2018

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Who Needs Books? Reading in the Digital Age by Lynn Coady (review)

University of Toronto quarterly, 2018, Vol.87 (3), p.284-286

Mount, Nick

2018

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Plantation forestry impacts on sediment yields and downstream channel dynamics in the UK: a review

Progress in physical geography, 2004-06, Vol.28 (2), p.197-240

2004

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Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia

PloS one, 2024-07, Vol.19 (7), p.e0305721

2024

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Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135–3152]

Water resources management, 2012-09, Vol.26 (12), p.3653-3662

2012

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Discussion of “Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers”

Hydrological sciences journal, 2011-01, Vol.56 (7), p.1325-1329

2011

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Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

Environmental modelling & software : with environment data news, 2019-04, Vol.114, p.112-128

2019

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Interview: Elephants Are Not Giraffes: A Conversation with Margaret Atwood, More or Less about Northrop Frye

University of Toronto quarterly, 2012-12, Vol.81 (1), p.60-70

Mount, Nick

2012

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discrete Bayesian network to investigate suspended sediment concentrations in an Alpine proglacial zone

Hydrological processes, 2008-08, Vol.22 (18), p.3772-3784

2008

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Tradition and the Individual Canadian Talent

A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C

Participatory modelling for stakeholder involvement in the development of flood risk management intervention options

Doing flood risk modelling differently: Evaluating the potential for participatory techniques to broaden flood risk management decision‐making

Improved validation framework and R-package for artificial neural network models

Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh

When Canadian literature moved to New York

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

Load or concentration, logged or unlogged? Addressing ten years of uncertainty in neural network suspended sediment prediction

Erratum to: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C

Discussion of ‘Reservoir Computing approach to Great Lakes water level forecasting’ by P. Coulibaly [J. Hydrol. 381(2010) 76–88]

DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

The Return of Beauty

Neuroemulation: definition and key benefits for water resources research

Who Needs Books? Reading in the Digital Age. CLS Kreisel Lecture Series by Lynn Coady

Who Needs Books? Reading in the Digital Age by Lynn Coady (review)

Plantation forestry impacts on sediment yields and downstream channel dynamics in the UK: a review

Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia

Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135–3152]

Discussion of “Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers”

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

Interview: Elephants Are Not Giraffes: A Conversation with Margaret Atwood, More or Less about Northrop Frye

discrete Bayesian network to investigate suspended sediment concentrations in an Alpine proglacial zone

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