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This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We find payments data with a factor model can help improve accuracy up to 25% in predicting GDP, retail, and wholesale sales; and nonlinear ML models can further improve the accuracy up to 20%. Furthermore, we find the retail payments data are more useful than the data from the wholesale system; and they add more value during crisis and at the nowcasting horizon due to the timeliness. The contribution of the payments data and ML models is small and linear during low and normal economic growth periods. However, their contribution is large, asymmetrical, and nonlinear during crises such as COVID-19. Moreover, we propose a cross-validation approach to mitigate overfitting and use tools to overcome interpretability in the ML models to improve their effectiveness for policy use. [ABSTRACT FROM AUTHOR]

With the increase in penetration of wind generation on interconnected power systems, the importance of wind power ramp forecasting has continuously grown. Large power ramps caused by sudden weather changes raise more concerns due to their significant impact on the power system economics and stability. Correct wind power ramp forecasts can help the system operators and utility companies to tradeoff the risks when scheduling wind energy in the electricity market. In this paper, a day‐ahead wind power ramp forecasting algorithm is developed to provide probabilistic ramp forecasts for look‐ahead times up to 48 h using hourly wind speed forecasts from Environment Canada High Resolution Deterministic Prediction System (HRDPS). An image‐based similarity search strategy has been designed to build a direct link between the wind speed forecasts and the wind power ramp prediction, thus reducing the impact of the uncertainty from both the power production forecast model and the ramp identification process on the forecasting accuracy. A performance assessment and validation of the proposed ramp event forecasting method is conducted by using the forecast and operation data from six investigated wind farms across Canada. [ABSTRACT FROM AUTHOR]

In this paper we propose a machine learning-based approach to predict a multitude of insurance claim categories related to canine diseases. We introduce several machine learning approaches that are evaluated on a pet insurance dataset consisting of 785,565 dogs from the US and Canada whose insurance claims have been recorded over 17 years. 270,203 dogs with a long insurance tenure were used to train a model while the inference is applicable to all dogs in the dataset. Through this analysis we demonstrate that with this richness of data, supported by the right feature engineering, and machine learning approaches, 45 disease categories can be predicted with high accuracy. [ABSTRACT FROM AUTHOR]

This paper studies the forecasting ability of various Phillips curve specifications for 1 year ahead headline and underlying core inflation measures for three open currently inflation targeting (IT) countries: Sweden, Canada and New Zealand. The changes of the monetary policy regime in these countries are discussed for periods of fixed exchange rate regime, early years of IT and later periods in the light of inflation forecasting accuracy. The paper finds evidence that Phillips curve models can improve inflation forecasts against the random walk benchmark in the periods when the central bank is explicitly targeting the inflation. Yet, the results from earlier periods indicate heterogeneity in individual model performance across various econometric specifications and different sample periods. [ABSTRACT FROM AUTHOR]

The objective of the paper is to assess the resilience of the economy of Australia following the Covid‐19 pandemic that hit the global economy in Q4 2019, in years 2020, 2021 and 2022. Quarterly growth rates (annualised) of the Real GDP of Australia and Canada are forecasted between Q2 2022 and Q4 2050. Two sets of forecasts are generated: forecasts using historical data including the pandemic (from Q1 1961 to Q1 2022) and excluding the pandemic (from Q1 1961 to Q3 2019). The computation of the difference of their averages is an indicator of the resilience of the economies during the pandemic, the greater the difference the greater the resilience. Used as a benchmark, Canada's economy shows a slightly lower resilience to the Covid‐19 pandemic (+0.37%) than Australia's economy (+0.39%) based on Q2 2022–2050 forecasts. However, driven by stronger growth than Canada, the average estimate of the Q2 2022–Q4 2050 quarterly (annualised) growth rate forecasts of Australia is expected to be +2.09% with the Q1 1961–Q1 2022 historical data while it should be +1.61% for Canada. Supported by higher growth, Australia's Real GDP is expected to overtake Canada's in Q1 2040. [ABSTRACT FROM AUTHOR]

Improving the operational effectiveness of hydropower systems is becoming more relevant considering the shift to renewable energy sources and the rising social, financial, and environmental costs associated with the construction of new hydropower facilities. This challenge is interdisciplinary as it involves technical, managerial, and institutional aspects. This paper focuses on a technical aspect, more specifically on the relationship between the quality of short-term ensemble streamflow forecasts and the energy produced by a hydropower system. A secondary objective is to measure the contribution of both the meteorological and structural uncertainties on the energy output. To achieve this, a numerical experiment comprising multiple sets of hydrologic ensemble forecasts of different quality and a suite of reservoir optimization models is developed for a case study in Canada (the hydropower system of the Gatineau River basin). These ensemble forecasts are processed by the short-term reservoir operation model in rolling-horizon mode over a planning period of 6 years. Each day, the short-term optimization model seeks to maximize the energy output over the 14-day forecast lead time considering the expected future value of the system derived from a midterm optimization model. The relationship between hydropower generation and common statistical scores characterizing the ensemble forecasts indicates that although there is a link between the quality of the forecasts and the energy production, it is not a one-to-one causal relationship. Our results also show that the diversity of hydrological models is beneficial to the production of energy, indicating that the diversity of model structures compensates the deficiencies of individuals models and adds value to the forecast. [ABSTRACT FROM AUTHOR]

The article offers information about the position paper entitled "Share Mental Health Care in Canada", which proposes to enhance the working relationship between family physicians and psychiatrists. The working group of the Canadian Psychiatric Association and the College of Family Physicians of Canada has described the obligations and responsibilities of each of the two medical specialties in a proposed shared care model for the treatment of mental illness. The position paper highlights the current difficulties experienced between family physicians and psychiatrists, which poor communication and lack of contact between members of each of the 2 specialty groups.

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system operation. A hybrid method that is a combination of the similar day (SD) selection, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and deep neural networks is proposed and explored in this paper. For the SD selection, an extreme gradient boosting (XGB)-based weighted k-means method is chosen and applied to evaluate the similarity between the prediction and historical days. The CEEMDAN algorithm, which is an advanced method of empirical mode decomposition (EMD), is used to decompose original data, to acquire intrinsic mode functions (IMFs) and residuals, and to improve the noise reduction effect. Three popular deep neural networks that have been utilized for load predictions are gated recurrent units (GRUs), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The developed models were assessed on a real-life charging load dataset that was collected from 1000 EVs in nine provinces in Canada from 2017 to 2019. The obtained numerical results of six predictive combination models show that the proposed hybrid SD-CEEMDAN-BiLSTM model outperformed the single and other hybrid models with the smallest forecasting mean absolute percentage error (MAPE) of 2.63% Canada-wide. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to propose a method to predict shear-wave velocities in the process of AVO (amplitude variation with offset) inversion, to interpret seismic converted-wave data, and to match reservoir engineering information with seismic data in regions without shear-wave velocities. During CO2 flooding and sequestration, the shear-wave velocity varies with the pressure that is caused by fluid injection. Predictions of how the shear-wave velocity changes with pressure can be used to interpret 4D seismic monitoring data during different stages of CO2 injection. To verify the calculations, the authors used the characteristic parameters of the Marly dolomite and Vuggy limestone units of the Weyburn Oilfield reservoir in Canada. The Digby's model and Gassmann's equation were used to evaluate the dependence of the shear-wave velocity on the pressure in the carbonate rock formation. The authors have developed a new method for calculating the coordination number of the model and supplement the results with a shale correction, enabling the prediction to match the measured shear-wave velocities more closely. Our method is verified by dipole sonic logging data and petrophysical test data from the Weyburn Oilfield. When applied to petrophysical test data, the average error is below 5%. When the method is applied to well log data, the average error in the porous media is 3.014% in the Marly unit and 6.288% in the Vuggy unit. [ABSTRACT FROM AUTHOR]

"Short-term load forecasting (STLF)" is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forecasting model with wavelet transform for the STLF depending on the decomposition principle of load profiles. The model can effectively capture the portion of daily load profiles caused by seasonal variations. The results indicate that it is possible to improve STLF accuracy with the proposed method. The proposed approach is tested with the data taken from Ontario's electricity market in Canada. The results show that the proposed technique performs well in-terms of prediction when compared to existing traditional and cutting-edge methods. The performance of the model was validated with different datasets. Moreover, this approach can provide accurate load forecasting using ensemble models. Therefore, utilities and smart grid operators can use this approach as an additional decision-making tool to improve their real-time decisions. [ABSTRACT FROM AUTHOR]

Purpose: The EQ-5D-5L is a commonly used instrument for assessing the utility of different health states. Health state utility values are a key component of health technology evaluations. Such evaluations are used to support evidence-based decisions surrounding health resource allocations and therefore rely on the accuracy of the valuation set used. This paper takes an alternative approach to developing an EQ-5D-5L value set for Canada. The aim is to introduce a robust method that is likely to generate a value set with improved accuracy and that can be used to generate value sets for other populations without the need for modification. Methods: The common approach to developing a valuation set for preference-based instruments is to ask a population sample to value a subset of the health states using an established preference elicitation technique. The relationship between the elicited health states and the preferences is used to inform a model to predict the utility values for the unsampled health states described by the instrument. The true relationship is unknown and the functional forms chosen in the modelling process vary across valuation studies. We use nonparametric local constant regression to estimate an EQ-5D-5L value set for Canada and propose this method as an alternative for value set development because it does not require the specification of a functional form at the outset. Results: Compared to the existing valuation model for Canada, the nonparametric method improves in-sample fit, reducing the average squared prediction error by 94.46% and the mean absolute error by 79.37%. In four of five sets of out-of-sample studies, this new approach performs significantly better than 9 comparison models. Despite lacking any restriction on the functional form of the resulting valuations, the valuation set generated by this new approach is logically consistent. 100% of the pairs of health states in which one state is dominant have health state values which respect this ordering. The value set also appears to differ substantially from the comparators. Conclusions: Overall, the results suggest that nonparametric regression is a promising tool for the estimation of EQ-5D-5L valuation sets and may be a good option in a standardised methodology for value set development. [ABSTRACT FROM AUTHOR]

Problem: Do conservative econometric models that comply with the Golden Rule of Forecasting provide more accurate forecasts?, Methods: To test the effects of forecast accuracy, we applied three evidence-based guidelines to 19 published regression models used for forecasting 154 elections in Australia, Canada, Italy, Japan, Netherlands, Portugal, Spain, Turkey, U.K., and the U.S. The guidelines direct forecasters using causal models to be conservative to account for uncertainty by (I) modifying effect estimates to reflect uncertainty either by damping coefficients towards no effect or equalizing coefficients, (II) combining forecasts from diverse models, and (III) incorporating more knowledge by including more variables with known important effects., Findings: Modifying the econometric models to make them more conservative reduced forecast errors compared to forecasts from the original models: (I) Damping coefficients by 10% reduced error by 2% on average, although further damping generally harmed accuracy; modifying coefficients by equalizing coefficients consistently reduced errors with average error reductions between 2% and 8% depending on the level of equalizing. Averaging the original regression model forecast with an equal-weights model forecast reduced error by 7%. (II) Combining forecasts from two Australian models and from eight U.S. models reduced error by 14% and 36%, respectively. (III) Using more knowledge by including all six unique variables from the Australian models and all 24 unique variables from the U.S. models in equal-weight "knowledge models" reduced error by 10% and 43%, respectively., Originality: This paper provides the first test of applying guidelines for conservative forecasting to established election forecasting models., Usefulness: Election forecasters can substantially improve the accuracy of forecasts from econometric models by following simple guidelines for conservative forecasting. Decision-makers can make better decisions when they are provided with models that are more realistic and forecasts that are more accurate., Competing Interests: The authors have declared that no competing interests exist.

The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting. [ABSTRACT FROM AUTHOR]

Objectives: Weather is a key source of marine risk, but relationships between fishing activity, safety, and weather remain poorly understood. Critically, the fit between available marine forecast products, fish harvesters' needs, and harvester's decision-making processes has not been rigorously assessed. This paper addresses these gaps by documenting a) weather-related decision-making by harvesters, and its relationship to forecasts across multiple regions and fisheries on Canada's East coast (Newfoundland) and b) the dynamics of forecast production priorities. Methods: A multi-disciplinary, community-engaged research approach, conducted in partnership with the Newfoundland and Labrador Fish Harvesting Safety Association (NL-FHSA). Data consist of semi-structured interviews with fish harvesters and weather forecasters, focused on marine forecast production and use. Results: Results emphasize that there is a subjective "art" to both production and use of marine forecasts. Forecasters and harvesters share several common values regarding forecasts, but different emphases: forecasters favor some combination of accuracy, consistency, and utility, while harvesters are largely concerned with utility. Finally, harvesters' decision-making is based on nuanced and contextual interpretations of a few key hazards (winds and, to a lesser extent, waves). Conclusion: This community-engaged research has triggered experimentation with forecasts tailored to fisheries utility within Environment and Climate Change Canada (ECCC). It lays the groundwork for ongoing, mutually beneficial dialogue between forecasters and harvesters, engaging harvesters with the forecasting process while familiarizing forecasters with harvester's decision-making processes. Ongoing industry partnerships (NL-FHSA) continue to sustain momentum from this study towards further enhancing the utility of future marine forecasts for small-scale harvesters. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting ensures the controllability for the wind power system. In this paper, a structure with density-weighted active learning (DWAL)-based model selection strategy from the perspective of meteorological factors is proposed to improve the accuracy and stability for wind speed deterministic and probabilistic forecasting. To improve training efficiency and accelerate the sample selection process, DWAL is employed. The multi-objective flower pollination algorithm is used to combine best models selected from model space with optimal weights for higher accuracy and stability. Except deterministic forecasts, as large-scale wind power generation integrated into power grid, the wind direction should also be forecasted and the estimation of the wind speed and direction uncertainty is vital, offering various aspects of forecasts for risk management. Thus, both deterministic and probabilistic forecasting for the wind speed vector are included in this paper. Eight datasets from Ontario Province, Canada, are utilized to evaluate forecasting performance of the model selection and the proposed structure. Results demonstrated: (a) the proposed structure is suitable for wind speed vector forecasting; (b) the proposed structure obtains more precise and stable forecasting performance; (c) the proposed structure improves the accuracy of deterministic forecasting and provides probabilistic information for wind speed vector forecasting. • Propose a novel forecasting structure based on meteorological analysis. • The model selection is employed. • The multi-objective flower pollination algorithm is used. • Density-weighted active learning algorithm is applied. • Both deterministic forecasting and probabilistic forecasting are applied. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories. [ABSTRACT FROM AUTHOR]

Tide refers to a phenomenon that causes the change of water level in oceans. Tidal level forecasting plays an important role in many real-world applications especially those related to oceanic and coastal areas. For instance, accurate forecasting of tidal level can significantly increase the vessels’ safety as an excessive level of tidal makes serious problems in the movement of vessels. In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and nonparametric approach termed as the lower upper bound estimation (LUBE) model. Moreover, we develop a novel deep neuroevolution algorithm based on a two-stage modification of the gaining-sharing knowledge optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. This leads to a decline in the complexity raises in designing manually the deep learning architectures, as well as an enhancement in the performance of the PIs. We also utilize coverage width criterion to establish an excellent correlation appropriately between both the PI coverage probability and PI normalized average width. We indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada. [ABSTRACT FROM AUTHOR]

This paper presents an autoregressive fractionally integrated moving-average (ARFIMA) model of nominal exchange rates and compares its forecasting capability with the monetary structural models and the random walk model. Monthly observations are used for Canada, France, Germany, Italy, Japan and the United Kingdom for the period of April 1973 through December 1998. The estimation method is Sowell's (1992) exact maximum likelihood estimation. The forecasting accuracy of the long-memory model is formally compared to the random walk and the monetary models, using the recently developed Harvey, Leybourne and Newbold (1997) test statistics. The results show that the long-memory model is more efficient than the random walk model in steps-ahead forecasts beyond 1 month for most currencies and more efficient than the monetary models in multi-step-ahead forecasts. This new finding strongly suggests that the long-memory model of nominal exchange rates be studied as a viable alternative to the conventional models. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Canadian Earth System Model version 5 (CanESM5) developed at Environment and Climate Change Canada's Canadian Centre for Climate Modelling and Analysis (CCCma) is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). A 40-member ensemble of CanESM5 historical decadal forecasts (or hindcasts) is integrated for ten years from realistic initial states during 1961 to present using prescribed external forcing. The results are part of CCCma's contribution to the Decadal Climate Prediction Project (DCPP) component of CMIP6. This paper evaluates CanESM5 large ensemble decadal hindcasts against observational benchmarks and against historical climate simulations initialized from preindustrial control run states. The focus is on the evaluation of the potential predictability and actual skill of annual and multi-year averages of key oceanic and atmospheric fields at regional and global scales. The impact of initialization on prediction skill is quantified from the hindcasts decomposition into uninitialized and initialized components. The dependence of potential and actual skill on ensemble size is examined. CanESM5 decadal hindcasts skilfully predict upper-ocean states and surface climate with a significant impact from initialization that depend on climate variable, forecast range, and geographic location. Deficiencies in the skill of North Atlantic surface climate are identified and potential causes discussed. The inclusion of biogeochemical modules in CanESM5 enables the prediction of carbon cycle variables which are shown to be skilful on decadal time scales, with a strong long-lasting impact from initialization on skill in the ocean and a moderate short-lived impact on land. [ABSTRACT FROM AUTHOR]

Background: To develop and validate a prediction tool, or nomogram, for the risk of a decline in cognitive performance based on the interRAI Cognitive Performance Scale (CPS). Methods: Retrospective, population-based, cohort study using Canadian Resident Assessment Instrument for Home Care (RAI-HC) data, collected between 2010 and 2018. Eligible home care clients, aged 18+, with at least two assessments were selected randomly for model derivation (75%) and validation (25%). All clients had a CPS score of zero (intact) or one (borderline intact) on intake into the home care program, out of a possible score of six. All individuals had to remain as home care recipients for the six months observation window in order to be included in the analysis. The primary outcome was any degree of worsening (i.e., increase) on the CPS score within six months. Using the derivation cohort, we developed a multivariable logistic regression model to predict the risk of a deterioration in the CPS score. Model performance was assessed on the validation cohort using discrimination and calibration plots. Results: We identified 39,292 eligible home care clients, with a median age of 79.0 years, 62.3% were female, 38.8% were married and 38.6% lived alone. On average, 30.3% experienced a worsening on the CPS score within the six-month window (i.e., a change from 0 or 1 to 2, 3, 4, 5, or 6). The final model had good discrimination (c-statistic of 0.65), with excellent calibration. Conclusions: The model accurately predicted the risk of deterioration on the CPS score over six months among home care clients. This type of predictive model may provide useful information to support decisions for home care clinicians who use interRAI data internationally. [ABSTRACT FROM AUTHOR]

A database of difficult-to-forecast natural persistent deep slab avalanches was analyzed to determine thresholds for parameters that contribute to their release in western Canada. The database included avalanche observations and weather station data. The avalanches were grouped based on their primary cause-of-release, either precipitation loading, wind loading, solar warming or air temperature warming using a multivariate classification tree, which first split using a solar warming parameter. The precipitation group had a median 24 h snowfall of 15 cm and 3 d snowfall of 38 cm at weather stations, mostly at or below treeline. These amounts were likely closer between 20-30 and 50-80 cm at alpine start zones. The wind loading group experienced the most wind-transported snow potential. The solar warming group had predicted solar warming of 5.2°C, 10 cm into the snowpack, on the days of release. The air temperature warming group experienced the highest median maximum air temperature (5°C) on the days of release. These thresholds may be useful to forecast the likelihood of similar avalanches with experienced-based forecasting or with decision aids, although many false alarms are possible. A companion paper, Part II, relates weather model data to avalanche occurrences. [ABSTRACT FROM AUTHOR]

Three indicators (GDP, PCDIIP-rh and Total Population) are selected in this paper to study the effect of sample length on forecasting validity of FGM(1,1). It has passed the test, such as development coefficient, mean relative error within the sample, and ratio of mean square error. The above three sets of indicators are proved to be suitable for FGM(1,1) to make predictions. The results of the study indicate that the forecasting of 4–6 sample lengths is the most appropriate. The MAPE of 5 sample length is better than sample lengths 4 or 6. The conclusion of this study is verified by taking the oil production of India and Canada as examples. On this basis, the sample length 5 is selected to predict the average annual concentration of PM 2.5 from 2019 to 2021 in Xingtai. The forecasting results show that the PM 2.5 in Xingtai will decline in the next three years, but it will not reach the national level 2 concentration limit. [ABSTRACT FROM AUTHOR]

• Column height is the parameter with more impact on longitudinal displacements. • Deck skew is the parameter with lesser influence on the seismic response. • Minimum required support lengths by actual codes are found to be very conservative. • The period is a more accurate parameter to predict longitudinal displacements. • An equation is proposed to estimate the required support length from the period. Loss of bridge deck supports is one of the major failures observed during past earthquakes. Deck collapse due to loss of support has been observed mainly on simply supported bridge structures with insufficient support length at expansion bearings. To accommodate the large displacements induced by seismic movements and to avoid bridge collapse, modern seismic standards and regulations require a minimum support length at expansion deck bearings. This paper presents the state of knowledge on the empirical support length equation and the main geometric parameters influencing the required support length at deck expansion bearings. It also presents the results of the parametric study performed on typical existing single and multi-span simply supported bridges subjected to seismic loads typical of Eastern Canada. The results allowed to identify and describe the key geometric parameters influencing the nonlinear responses of existing bridges, evaluate their relative influence in terms of displacements and ductility associated with the loss of deck supports. It also compares the current empirical equation with predicted displacements and proposes an equation more suitable for predicting expected displacements (lower than code values) in an evaluation framework for judging the seismic vulnerability of existing such as the bridge models considered in this study. [ABSTRACT FROM AUTHOR]

This paper reports a component of a larger study, Informatics: enhancing the Clinical Experience? (ICE), which explored the impact on the therapeutic relationship of the implementation and use of Electronic Medical Records (EMR) in British Columbia, Canada. As anticipated, EMRs were found to negatively affect the relationship in many clinics. However, surprisingly paper-based clinics were as likely as EMR-based clinics to report problems with maintaining eye contact with their patients. This led to an interesting finding; that as a result of this difficulty few family care providers actually chart when their patients are with them, preferring to build rapport and chart at a later time. Consequently three recommendations are made: 1) Improve medical education in the area of charting (paper & EMR-based) with the patient present; 2) Explore the affect different technologies and skills have on the ability of providers to chart with the patient present and 3) Develop an understanding that unless the technology and training improve Canadian family medicine will never gain the asserted benefits of EMRs, and that other incentives are needed if Canada is to meet its target of delivering Electronic Health Records (EHR) to 100% of all Canadians by 2015.

Abstract: This article explores how the term ‘foresight’ originally came to be used in connection with science and technology by the author and SPRU colleagues in 1983. It analyses how the rationale for its use evolved over time, first providing a ‘catchy’ title for a study (‘Project Foresight’), and then a convenient shorthand for the focus of that study, before eventually coming to formally signify a new approach to looking systematically into the future of science and technology, an inclusive and wide-ranging process that differed appreciably from that of traditional ‘technology forecasting’. The paper reflects on the importance of concepts and terminology in the field of science policy research, providing examples of how an inappropriate term or phrase can damn the prospects of the research having an impact on policy, while a more politically astute use of terminology can greatly enhance the probability of making a significant impact. The paper also examines other early uses of the concept of ‘foresight’ in the United States and Canada at about the same time. In addition, it highlights the conceptual similarities between foresight and la prospective, a novel approach developed in France not just for looking into the future but also for shaping or even ‘constructing’ the future of our choice, an ambitious aspiration that it shares with foresight. This case-study on the origins and early evolution of ‘technology foresight’ illustrates the essential importance of terminology in differentiating key concepts in social sciences (where it sometimes gives rise to unfortunate priority disputes), and particularly in the case of policy research. [ABSTRACT FROM AUTHOR]

The 2015 Canadian Weight Bias Summit disseminated the newest research advances and brought together 40 experts, stakeholders, and policy makers in various disciplines in health, education, and public policy to identify future research directions in weight bias. In this paper we aim to share the results of the Summit as well as encourage international and interdisciplinary research collaborations in weight bias reduction. Consensus emerged on six research areas that warrant further investigation in weight bias: costs, causes, measurement, qualitative research and lived experience, interventions, and learning from other models of discrimination. These discussions highlighted three key lessons that were informed by the Summit, namely: language matters, the voices of people living with obesity should be incorporated, and interdisciplinary stakeholders should be included. [ABSTRACT FROM AUTHOR]

This paper evaluates the forecast performance of Canadian financial institutions and forecasting firms between 1984 and 2003. The sample period is roughly double that of an earlier study. We divide the sample in two and investigate whether a particular institution has improved its relative standing over the last ten years. Results for two Quebec institutions — the Banque Nationale (National Bank) and the Mouvement Desjardins — are now included in the second part of the sample, whereas a complete dataset was not available for these two institutions during the 1980s. We look as well at the relative performance of individual forecasters compared to the consensus forecast (defined as the mean of the forecasts available during the period in question). The paper then compares the accuracy of the Canadian consensus forecast with the forecast offered by the OECD each December for a limited number of key macroeconomic variables. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present the impacts of a fully interactive coupling between an atmospheric and a sea ice model over the Gulf of St. Lawrence, Canada. The impacts are assessed in terms of the atmospheric and sea ice forecasts produced by the coupled numerical system. The ocean-ice model has been developed at the Maurice Lamontagne Institute, where it runs operationally at a horizontal resolution of 5 km and is driven (one-way coupling) by atmospheric model forecasts provided by the Meteorological Service of Canada (MSC). In this paper the importance of two-way coupling is assessed by comparing the one-way coupled version with a two-way coupled version in which the atmospheric model interacts with the sea ice model during the simulation. The impacts are examined for a case in which the sea ice conditions are changing rapidly. Two atmospheric model configurations have been studied. The first one has a horizontal grid spacing of 24 km, which is the operational configuration used at the Canadian Meteorological Centre. The second one is a high-resolution configuration with a 4-km horizontal grid spacing. A 48-h forecast has been validated using satellite images for the ice and the clouds, and also using the air temperature and precipitation observations. It is shown that the two-way coupled system improves the atmospheric forecast and has a direct impact on the sea ice forecast. It is also found that forecasts are improved with a fine resolution that better resolves the physical events, fluxes, and forcing. The coupling technique is also briefly described and discussed. [ABSTRACT FROM AUTHOR]

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Background: Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control. Methods: To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc). Results: A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (–0.81 and –0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (–0.03), Israel (–0.12), Italy (–0.02), and France (–0.03); all p < 0.05. Conclusions: The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the main crops in Canada. Near real-time forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of near real-time crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. In addition, three simple unsupervised domain adaptation (DA) methods were adopted for improving the generalization ability of yield prediction. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that "DA then MLR at the optimum stage" performed better than "MLR directly at the early stages" for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. [ABSTRACT FROM AUTHOR]

Autoregressive (AR) models are useful in time series analysis. Inferences under such models are distorted in the presence of measurement error, a common feature in applications. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We consider two measurement error models to describe different data contamination scenarios. We propose an estimating equation approach to estimate the AR model parameters with measurement error effects accounted for. We further discuss forecasting using the proposed method. Our work is inspired by COVID-19 data, which are error-contaminated due to multiple reasons including those related to asymptomatic cases and varying incubation periods. We implement the proposed method by conducting sensitivity analyses and forecasting the fatality rate of COVID-19 over time for the four most populated provinces in Canada. The results suggest that incorporating or not incorporating measurement error effects may yield rather different results for parameter estimation and forecasting. [ABSTRACT FROM AUTHOR]

"The main themes of the paper are developed in three parts. First, the setting for the study is established by reviewing the foundational theory of multiregional population projections, and by detailing how interregional migration is projected within this approach. Second, we will identify the requirements for making multiregional population projections in Canada and undertake to explain why the conventional approach to projecting interregional migration produces less than satisfactory results. Third, an alternative method for projecting interregional migration is proposed.", (excerpt)

"Canadian families are pluralistic and varied, with a mix of traditional, modern, and post-modern characteristics. This paper looks at families into the immediate future.... Consideration is given to ethnicity, cohabitation, fertility, childrearing, sexuality, family policy, adolescence, and general family life. The state will continue to influence family life. Non-familial associations will affect family values and activity.", (excerpt)

"This paper analyses the prospects for age distribution in Canada through 2036, based on today's age structure and expected trends in fertility and international migration. Simulating these two demographic components at extreme levels shows that tomorrow's aging is already built into the current age distribution, which is heavily marked by the post-war baby boom and the following persistent baby bust, and that neither fertility nor immigration can be seen as effective adjustment mechanisms for the changing age composition of Canada. Population projections using the component method are applied along with an age-specific growth-rate analysis.", (excerpt)

An inaccurate forecast of inflation is costlier to economic agents when the inflation rate is high and volatile. In this situation, the use of more sophisticated and information-oriented forecasting models become economically efficient. We test this hypothesis by analysing the forecasting accuracy of vector auto-regressive (VAR), auto-regressive integrated moving average (ARIMA) and static expectation models. We use Canadian data and divide the post-sample forecasting period into four sub-periods, based on high/low and volatile/stable inflation. Prediction errors are compared for both short-term and long-term forecasts. Finally, the paper proposes a portfolio approach for obtaining a more accurate forecast of inflation. [ABSTRACT FROM AUTHOR]

This paper examines the broad movements of Canadian period and cohort fertility over the last hundred years or so, and compares them with corresponding trends in the United States and other industrialized countries. The main movement in Canada was a decline in fertility extending from the nineteenth century to the present time, interrupted in the 1940s and 1950s by a ‘baby boom’. The long decline in cohort fertility is largely explained by the decrease in the proportions of families of six or more children. This decrease continued during the baby boom, but in these years was more than offset, though not for Catholics, by the effects of increases in the proportions of families with three, four, and five children. [ABSTRACT FROM PUBLISHER]

This paper presents a Canadian inflationary expectations measure that is based on quite realistic assumptions about the amount of information available to those forming expectations and uses this estimate to study the determinants of wage changes in the 1967-81 period. The main conclusions are: (1) wage settlements are quite responsive to variations in labour market conditions, a 1 per cent change in the unemployment rate leading to approximately a 1 per cent change in the rate of change of wages; (2) the 1971 revisions to the Unemployment Insurance Act raised the 'natural' unemployment rate by approximately 1.4 points; and (3) the Anti-Inflation Program of 1975-8 had a significant effect on wage settlements, the estimated average reduction being approximately 2 per cent. The study also presents the first statistical tests of two competing hypotheses regarding the manner in which 'catch-up' forces affect wage settlements. [ABSTRACT FROM AUTHOR]

This paper presents a comparison between similar types of American and Canadian forecasts of annual gross national product for an identical period (1960-69) using the same error measures. It was concluded that on mean, mean absolute, and mean square error bases, American forecasts were significantly more accurate than were the Canadian ones in predicting relative changes in their respective annual gross national products during the period 1960-69. Forecasters in both the United States and Canada were likely to have made the same types of errors on average in forecasting any given year. The general conclusion from the analysis of the errors seemed to suggest that a bias toward underestimating change may be less evident in the American gross national product forecasts than in those made in Canada. The negative mean errors were smaller, the percentage of underestimates was smaller, the covariance components of the mean square errors were larger, and the regression and test statistics were less suggestive. One of the more important points is that when "adjustments" of the predictions for differences in the basic nature of the economies are made, the conclusions as to differences in the "accuracy" of aggregate forecasts made in various countries may change a great deal. That is, conclusions as to differences in forecasting ability based solely on the comparison of "absolute accuracy" could be seriously misleading if the economies are not of approximately similar natures. The method of "adjustment" used here assumes that information as to the nature of the economies is contained in the past history of the variables to be forecast, so that a more worthwhile comparison is obtained when the respective judgmental sets are first "adjusted" by extrapolative predictions from these past actuals. Comparisons of the "relative" accuracy of forecasts showed that the American predictions of annual gross national product percentage changes had larger relative mean square errors than did the Canadian sets. That is, the American sets were only about as accurate as, and probably less accurate then, relative to extrapolative predictions. the forecasts made in Canada. The predictive efficiency of the Canadian gross national product forecasts relative to that of the extrapolative predictions was generally higher than that of the American predictions; the amount of autonomous information and available extrapolative information included was also somewhat higher. However, the extrapolative information actually used in forecasting was quite low in both countries, probably because of the essentially trendless nature of percentage changes in gross national product. [ABSTRACT FROM AUTHOR]

The author reflects on the developments surrounding the August 1997 issue of the journal "The Canadian Journal of Psychiatry." In the leading paper, author Michael Ruiner familiarizes the reader with his latest preoccupation. He defines genetic research to include a thorough understanding of the multiple interplays between genetic structures and the outside world.

Accurate estimation of fuel consumption and emissions is crucial for assessing the impact of materials and stringent emission control techniques on climate change, particularly in the transportation industry, which accounts for a significant portion of global greenhouse gases and hazardous pollutants emissions. To address these concerns, the government of Canada has collected a large sensor-based dataset containing detailed information on 7384 light-duty vehicles from 2017 to 2021, with the goal of reducing CO2 emissions by 40–45% by 2030. To this end, various researchers worldwide have developed vehicle emissions and consumption models to comply with these targets and achieve the Canadian government's ambitious objectives. In this work, we propose the development of boosting and other regression models to predict carbon dioxide emissions for light-duty vehicle designs, with the aim of creating ensemble learning models that leverage vehicle specifications to forecast emissions. Our proposed boosting model is capable of accurately predicting CO2 emissions, even with only one car attribute as input. Moreover, our regression models, in conjunction with the boosting algorithm, can effectively make predictions from various vehicle inputs. Our proposed technique, categorical boosting (Catboost), provides critical insights into transportation-generated air pollution, offering valuable recommendations for both vehicle users and manufacturers. Importantly, Catboost performs data processing in less time and with less memory than other algorithms proposed in the literature. Future research efforts should focus on developing higher performance models and expanding datasets to further improve the accuracy of predictions. [ABSTRACT FROM AUTHOR]

Summary: Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non‐destructive estimates of leaf traits, but it remains unclear whether general trait‐spectra models can yield accurate estimates across functional groups and ecosystems.We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least‐squares regression (PLSR) to build empirical models for estimating traits from spectra.Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55–0.85; %RMSE = 12.7–19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy.We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world. [ABSTRACT FROM AUTHOR]

Avalanche warning services increasingly employ large-scale snow stratigraphy simulations to improve their insight into the current state of the snowpack. These simulations contain information about thin, persistent critical avalanche layers that are buried within the snowpack and are fundamental drivers of avalanche hazard. However, the data volume, data complexity, and unknown validity have so far limited the value of the simulations for operational decisions. We attribute this at least partially to a lack of research that validates the simulations for their capability to represent the existence and instability of known critical layers at the regional scale. To address this knowledge gap, we present methods that enable meaningful comparisons between regional assessments of avalanche forecasters and snowpack simulations that are distributed across entire forecast regions. We applied these methods to operational data sets of ten winter seasons and three public forecast regions in western Canada and thereby quantified the performance of the Canadian weather and snowpack model chain to represent persistent critical avalanche layers. We found that the overall probability of detecting a known critical layer in the simulations can be as high as 75 % when accepting a low probability of 40 % that any simulated layer is actually of operational concern in reality. Furthermore, we explored patterns that characterize which layers were represented well and which were not. Faceted layers, for example, were captured well but also caused most false alarms, whereas surface hoar layers tended to be less prevalent but in return were mostly of operational concern when modeled. Overall, our results suggest that the simulations provide a valuable starting point for targeted field observations as well as a rich complementary information source that can help alert forecasters about the existence of specific critical layers or provide an independent perspective on their instability. However, we do not believe that the existing model chain is sufficiently reliable to generate assessments purely based on simulations. We conclude by presenting our vision of a real-time operational validation suite that can help forecasters develop a better understanding of the simulations' strengths and weaknesses by continuously comparing assessments and simulations in a user-friendly manner. [ABSTRACT FROM AUTHOR]

• This paper is focused on predicting price signals for energy arbitrage of storage systems. • A methodology is presented based on feature selection tools and classification techniques. • A Li-Ion battery in the Ontario competitive electricity market is considered as a case study. • Results indicate that the proposed methodology outperforms alternative point forecasting models. When participating in a competitive electricity market, energy storage systems could deliver various services and stack multiple revenue streams. One potential venue for gaining profit is energy arbitrage, i.e., to harvest the price differential that might exist in some markets between the peak and off-peak hours. To take advantage of price arbitrage, however, it is necessary to have an insight into the price fluctuations of upcoming hours. In this paper, we propose a method for generating predictive electricity price signals to help storage operators make arbitrage decisions. The proposed method delivers signals that are integrated into an optimization platform to schedule the arbitrage operation of a storage system. We examine a Lithium-ion battery as the choice of storage system and the Ontario's competitive electricity market in Canada is considered as the case study. We compare the performance of the proposed approach against the cases that alternative point forecasts of the electricity price are used for self-scheduling of the storage system. The results show that the obtained arbitrage profit of the storage system can be increased significantly using the proposed approach. [ABSTRACT FROM AUTHOR]

Sustainable building practices are rooted in the need for reliable information on the long-term performance of building materials; specifically, the expected service-life of building materials, components, and assemblies. This need is ever more evident given the anticipated effects of climate change on the built environment and the many governmental initiatives world-wide focused on ensuring that structures are not only resilient at their inception but also, can maintain their resilience over the long-term. The Government of Canada has funded an initiative now being completed at the National Research Council of Canada's (NRC) Construction Research Centre on "Climate Resilience of Buildings and Core Public infrastructure". The outcomes from this work will help permit integrating climate resilience of buildings into guides and codes for practitioners of building and infrastructure design. In this paper, the impacts of climate change on buildings are discussed and a review of studies on the durability of building envelope materials and elements is provided in consideration of the expected effects of climate change on the longevity and resilience of such products over time. Projected changes in key climate variables affecting the durability of building materials is presented such that specifications for the selection of products given climate change effects can be offered. Implications in regard to the maintainability of buildings when considering the potential effects of climate change on the durability of buildings and its components is also discussed. [ABSTRACT FROM AUTHOR]

Ian B. MacNeill, an outstanding statistician of our times, passed away on January 16, 2019. He left the legacy of fundamental contributions to change‐point and forecasting methodology, environmetrics, and health statistics. He was an integral part of building The International Environmetrics Society and its journal Environmetrics. He was a thoughtful visionary scientist and a caring mentor for students and faculty. We were fortunate to record the conversation with Ian when all three of us were participating in the 2016 annual meeting of the Statistical Society of Canada. At the meeting, a special session was organized to honor Ian MacNeill in celebration of his 85th birth year. This conversation took place between Ian MacNeill, Krishna Jandhyala, and Elena Naumova at the Four Points by Sheraton, Niagara Suites on June 1, 2016. [ABSTRACT FROM AUTHOR]

This study investigates the capability of sequence-to-sequence machine learning (ML) architectures in an effort to develop streamflow forecasting tools for Canadian watersheds. Such tools are useful to inform local and region-specific water management and flood forecasting related activities. Two powerful deep-learning variants of the Recurrent Neural Network were investigated, namely the standard and attention-based encoder-decoder long short-term memory (LSTM) models. Both models were forced with past hydro-meteorological states and daily meteorological data with a look-back time window of several days. These models were tested for 10 different watersheds from the Ottawa River watershed, located within the Great Lakes Saint-Lawrence region of Canada, an economic powerhouse of the country. The results of training and testing phases suggest that both models are able to simulate overall hydrograph patterns well when compared to observational records. Between the two models, the attention model significantly outperforms the standard model in all watersheds, suggesting the importance and usefulness of the attention mechanism in ML architectures, not well explored for hydrological applications. The mean performance accuracy of the attention model on unseen data, when assessed in terms of mean Nash–Sutcliffe Efficiency and Kling-Gupta Efficiency is, respectively, found to be 0.985 and 0.954 for these watersheds. Streamflow forecasts with lead times of up to 5 days with the attention model demonstrate overall skillful performance with well above the benchmark accuracy of 70%. The results of the study suggest that the encoder–decoder LSTM, with attention mechanism, is a powerful modelling choice for developing streamflow forecasting systems for Canadian watersheds. [ABSTRACT FROM AUTHOR]