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Browsing Information Systems and Technology by Author "Erechtchoukova, Marina G."
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Item Open Access Advancement of Data-Driven Short-Term Flood Predictions on an Urbanized Watershed Using Preprocessing Techniques(2018-11-21) Zistler, Marina; Erechtchoukova, Marina G.Supervised classification can be applied for short-term predictions of hydrological events in cases where the label of the event rather than its magnitude is crucial, as in the case of early flood warning systems. To be effective, these warning systems must be able to forecast floods accurately and to provide estimates early enough. Following the approach of transforming hydrological sensor data into a phase space using time-delay embedding, an attempt was made to improve the performance of the models and to increase the lead-time of reliable predictions. For this, the available set of attributes supplied by stream and rain gauges was extended by derivatives. In addition, imbalanced data techniques were applied at the data preprocessing step. The computational experiments were conducted on various data sets, lead-times, and years with different hydrological characteristics. The results show that especially derivatives of water level data improve model performance, increasingly when added for only one or two hours before the prediction time. In addition to that, the imbalanced data techniques allowed for overall improved prediction of floods at the cost of slight increase of misclassification of low-flow events.Item Open Access Comparative Analysis of Language Models on Augmented Low-Resource Datasets for Application in Question & Answering Systems(2024-11-07) Ranjbargol, Seyedehsamaneh; Erechtchoukova, Marina G.This thesis aims to advance natural language processing (NLP) in question-answering (QA) systems for low-resource domains. The research presents a comparative analysis of several pre-trained language models, highlighting their performance enhancements when fine-tuned with augmented data to address several critical questions, such as the effectiveness of synthetic data and the efficiency of data augmentation techniques for improving QA systems in specialized contexts. The study focuses on developing a hybrid QA framework that can be integrated with a cloud-based information system. This approach refines the functionality and applicability of QA systems, boosting their performance in low-resource settings by using targeted fine-tuning and advanced transformer models. The successful application of this method demonstrates the significant potential for specialized, AI-driven QA systems to adapt and thrive in specific environments.Item Open Access Comparative Analysis of Transformer-Based Language Models for Text Analysis in the Domain of Sustainable Development(2023-08-04) Safwat, Nabil; Erechtchoukova, Marina G.With advancements of Artificial Intelligence, Natural Language Processing (NLP) has gained a lot of attention because of its potential to facilitate complex human-machine interactions, enhance language-based applications, and automate processing of unstructured texts. The study investigates the transfer learning approach on Transformer-based Language models, abstractive text summarization approach, and their application to the domain of Sustainable Development with the goal to determine SDGs representation in scientific publications using the text summarization technique. To achieve this, the traditional transfer learning framework was expanded so that: (1) the relevance of textual documents to specified text can be evaluated, (2) neural language models, namely BART and T5, were selected, and (3) 8 text similarity measures were investigated to identify the most informative ones. Both the BART and T5 models were fine-tuned on an acquired domain-specific corpus of scientific publications extracted from Scopus Elsevier database. The relevance of recently published works to an SDG was determined by calculating semantic similarity scores between each model generated summary to the SDG’s description. The proposed framework made it possible to identify goals that dominated the developed corpus and those that require further attention of the research community.Item Open Access Exploratory Analysis of Water Quality in a Small Urbanized Watershed Using Deep Learning(2023-12-08) Ofosu, Alfred; Erechtchoukova, Marina G.Water is a life-sustaining resource for living organisms inside and outside water bodies. Natural waters serve as municipal and industrial water supplies, sources for agricultural irrigation, homes for aquatic ecosystems, recreation, and other essential uses. The quality of water determines its use. Therefore, it must be monitored, managed, and reported to help stakeholders in decision-making that can protect watershed ecosystems and improve measures to mitigate factors adversely affecting water bodies. Water quality is represented by a set of parameters that describe specific characteristics or properties of water. These parameters are determined by measuring water's physical and chemical characteristics and concentration levels of various substances in a water column with subsequent sample analysis in laboratories. This results in low frequencies of observations for water quality parameters compared to hydrometric and meteorological data. Frequencies of observation adopted by many water quality monitoring systems vary between 4 and 12 samples per year, suggesting applying modelling techniques to support decision-making. The study aims to develop a data-driven computational tool for water quality modelling in a small, highly urbanized watershed of the Don River, Ontario, Canada. The study focuses on major ions, namely, cations: calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and potassium (K+), and anions such as bicarbonate (HCO3-), carbonate (CO32-), chloride (Cl-), and sulphate (SO42-). These parameters are not affected significantly by the aquatic ecosystem. The hydrological and meteorological processes mainly determine their dynamics. The study uses data from different monitoring systems belonging to the Toronto and Region Conservation Authority (TRCA) and Environment and Climate Change Canada (ECCC). It consists of water quality parameters and hydrometric and meteorological characteristics observed in the watershed over 57 years. Concentrations of selected water quality parameters are modelled using deep neural networks. The data pre-processing framework for cleansing and integrating data observed at different frequencies from different locations is developed. The framework is applied for the comparative analysis of neural networks of various configurations. Two sets of computational experiments were conducted. In the first set of experiments, integrated data from all monitoring stations in the watershed was fed into the deep learning algorithms to train a neural network to predict the concentration of major ions for the upcoming month (t+1). The second set of experiments uses upstream environmental parameters to train the model and predict the major ion concentrations in the lower subwatershed. The study investigates the performance of developed models in accurately predicting ion concentrations and provides insights into the relationship between environmental factors and water quality in the investigated watershed. The findings have practical applications for water resource management and pollution prevention efforts.Item Open Access Exploring Topic Modeling in The Domain of Integrated Water Resource Management(2021-11-15) Kohli, Akshay Kumar; Erechtchoukova, Marina G.To successfully achieve the United Nations Sustainable Development goals, policy and decision making should include Integrated Environmental Assessment (IEA). Water resources and there utilization play an important role in achieving these goals at all levels from global to local. Sustainability of a water resource is of paramount importance for achieving United Nations long-term development goals. Sustainability of a resource is governed by the interplay of inner natural processes, biological, economical and social systems, making management of a water resource a complex multidisciplinary problem which can be solved only by combining various approaches. The thesis explored application of text mining techniques, namely, topic modelling, to scientific publications in the sustainable water resource management domain with the goal to identify major research questions, practical problems and methodological approaches used to address these problems. Comparative analysis of approaches to building corpora and model performance evaluations were conducted.