Oil sands mining results in significant disturbances to natural ecosystems when soil and overburden materials are removed and stockpiled to provide access to mined materials. The mining process must be followed by land reclamation, whereby disturbed landscapes are recovered with the intent to replicate the performance of natural watersheds. Modeling hydrological processes in reclaimed landscapes is essential to assess the hydrological performance of the reclamation strategies as well as their evolution over time, and requires a reliable and continuous source of input data. In pursuit of simulating the various hydrological processes, such as soil moisture and actual evapotranspiration, a lumped generic system dynamics watershed (GSDW) model has been developed. The validity of the proposed model has been assessed in terms of its capacity to reproduce the hydrological behaviour of both reconstructed and natural watersheds.<p> Data availability is a major challenge that constrains not only the type of models used but also their predictive ability and accuracy. This study evaluates the utility of precipitation and temperature data from the North American Regional Reanalysis (NARR) versus conventional platform data (e.g., meteorological station) for the hydrological modeling. Results indicate NARR data is a suitable alternative to local weather station data for simulating soil moisture patterns and evapotranspiration fluxes despite the high complexity involved in simulating such processes. Initially, the calibrated GSDW model was used along with available historical meteorological records, from both Environment Canada and NARR, to estimate the maximum soil moisture deficit and annual evapotranspiration fluxes. A probabilistic framework was adopted, and frequency curves of the maximum annual moisture deficit values were consequently constructed and used to assess the probability that various reconstructed and natural watersheds would provide the desired moisture demands. The study shows a tendency for the reconstructed watersheds to provide less moisture for evapotranspiration than natural systems. The probabilistic framework could be implemented to integrate information gained from mature natural watersheds (e.g., the natural system canopy) and transfer the results to newly reconstructed systems.<p> Finally, this study provided some insight into the sensitivity of soil moisture patterns and evapotranspiration to possible changes in the projected precipitation and air temperature in the 21st century. Climate scenarios were generated using daily, statistically downscaled precipitation and air temperature outputs from global climate models (CGCM3), under A2 and B1 emission scenarios, to simulate the corresponding soil moisture and evapotranspiration using the GSDW model. Study results suggest a decrease in the maximum annual moisture deficit will occur due to the expected increase in annual precipitation and air temperature patterns, whereas actual evapotranspiration and runoff are more likely to increase.

Oil sands mining results in significant disturbances to natural ecosystems when soil and overburden materials are removed and stockpiled to provide access to mined materials. The mining process must be followed by land reclamation, whereby disturbed landscapes are recovered with the intent to replicate the performance of natural watersheds. Modeling hydrological processes in reclaimed landscapes is essential to assess the hydrological performance of the reclamation strategies as well as their evolution over time, and requires a reliable and continuous source of input data. In pursuit of simulating the various hydrological processes, such as soil moisture and actual evapotranspiration, a lumped generic system dynamics watershed (GSDW) model has been developed. The validity of the proposed model has been assessed in terms of its capacity to reproduce the hydrological behaviour of both reconstructed and natural watersheds.<p> Data availability is a major challenge that constrains not only the type of models used but also their predictive ability and accuracy. This study evaluates the utility of precipitation and temperature data from the North American Regional Reanalysis (NARR) versus conventional platform data (e.g., meteorological station) for the hydrological modeling. Results indicate NARR data is a suitable alternative to local weather station data for simulating soil moisture patterns and evapotranspiration fluxes despite the high complexity involved in simulating such processes. Initially, the calibrated GSDW model was used along with available historical meteorological records, from both Environment Canada and NARR, to estimate the maximum soil moisture deficit and annual evapotranspiration fluxes. A probabilistic framework was adopted, and frequency curves of the maximum annual moisture deficit values were consequently constructed and used to assess the probability that various reconstructed and natural watersheds would provide the desired moisture demands. The study shows a tendency for the reconstructed watersheds to provide less moisture for evapotranspiration than natural systems. The probabilistic framework could be implemented to integrate information gained from mature natural watersheds (e.g., the natural system canopy) and transfer the results to newly reconstructed systems.<p> Finally, this study provided some insight into the sensitivity of soil moisture patterns and evapotranspiration to possible changes in the projected precipitation and air temperature in the 21st century. Climate scenarios were generated using daily, statistically downscaled precipitation and air temperature outputs from global climate models (CGCM3), under A2 and B1 emission scenarios, to simulate the corresponding soil moisture and evapotranspiration using the GSDW model. Study results suggest a decrease in the maximum annual moisture deficit will occur due to the expected increase in annual precipitation and air temperature patterns, whereas actual evapotranspiration and runoff are more likely to increase.

<p dir="ltr">The volume of big data has surged, notably in unstructured textual data, comprising emails, social media, and more. Currently, unstructured data represents over 80% of global data, the growth is propelled by digitalization. Unstructured text data analysis is crucial for various applications like social media sentiment analysis, customer feedback interpretation, and medical records classification. The complexity is due to the variability in language use, context sensitivity, and the nuanced meanings that are expressed in natural language. Traditional machine learning approaches, while effective in handling structured data, frequently fall short when applied to unstructured text data due to the complexities. Extracting value from this data requires advanced analytics and machine learning. Recognizing the challenges, we developed innovative ensemble approaches that combine the strengths of multiple conventional machine learning classifiers through a probabilistic approach. Response to the challenges , we developed two novel models: the Consensus-Based Integration Model (CBIM) and the Unified Predictive Averaging Model (UPAM).The CBIM and UPAM ensemble models were applied to Twitter (40,000 data samples) and the National Electronic Injury Surveillance System (NEISS) datasets (323,344 data samples) addressing various challenges in unstructured text analysis. The NEISS dataset achieved an unprecedented accuracy of 99.50%, demonstrating the effectiveness of ensemble models in extracting relevant features and making accurate predictions. The Twitter dataset, utilized for sentiment analysis, demonstrated a significant boost in accuracy over conventional approaches, achieving a maximum of 65.83%. The results highlighted the limitations of conventional machine learning approaches when dealing with complex, unstructured text data and the potential of ensemble models. The models exhibited high accuracy across various datasets and tasks, showcasing their versatility and effectiveness in obtaining valuable insights from unstructured text data. The results obtained extend the boundaries of text analysis and improve the field of natural language processing.</p>