Scientific Journal of Astana IT University

FEATURE SELECTION METHODS FOR LSTM-BASED RIVER WATER LEVEL AND DISCHARGE FORECASTING

2025-02-12T12:33:42+05:00

Accurate forecasting of river discharge and water levels is essential for effective water resource management, flood mitigation, and public safety. This study compares correlation-based and PCA-based feature selection methods for LSTM forecasting models in the study area at Uba River basin, within Shemonaiha city in the East Kazakhstan region. The dataset spans from 1995 to 2021, with 1995 to 2019 used for training and validation and 2020 to 2021 for testing. Both feature selection methods reduced the original predictor set to 13 features while generally maintaining predictive accuracy. An ensemble of 10 LSTM models was trained using 60-day input sequences to forecast discharge and water levels over a 10-day horizon, reducing variance from random initialization and stabilizing predictions. Performance was evaluated using the Nash-Sutcliffe Efficiency. Results showed that correlation-based selection performed comparably to the full-feature baseline in 2020 test set, suggesting that removing highly correlated predictors did not decrease short-term forecasts capacity of the model. The model with PCA-based selected features, while slightly lagging at longer lead times in 2020, exhibited advantages in most lead times with 2021 forecasts. However, overall predictive performance declined in 2021 compared to 2020, indicating that the hydrological conditions deviate more from the historical training record, and suggesting the need for model updates with relevant historical training data. Both feature selection methods successfully reduced dimensionality, while preserving performance capacity, though neither was universally superior across all forecast lead times. These results emphasize the value of systematic feature selection in hydrological modeling and highlight the importance of model adaptability to evolving environmental conditions.

COMPARATIVE ANALYSIS OF VARIOUS FORECAST MODELS OF ELECTRICITY CONSUMPTION IN SMART BUILDINGS

2024-10-17T12:09:18+05:00

The rapidly growing field of smart building technology depends heavily on accurate electricity consumption forecasting. By anticipating energy demands, building managers can optimize resource allocation, minimize waste, and enhance overall efficiency. This study provides a comprehensive comparative analysis of various models used to forecast electricity consumption in smart buildings, highlighting their strengths, limitations, and suitability for different use cases. The investigation focuses on three major categories of forecasting models: statistical methods, machine learning techniques, and hybrid approaches. Statistical models, such as the Moving Average Method, leverage historical data patterns to predict future trends. These models enable analysts to utilize predictive analytics, simulating real-world environments and helping them make more informed decisions. The study offers a detailed comparison of several predictive models applied to Internet of Things (IoT) data, with a particular emphasis on energy consumption in smart buildings. Among the short-term forecasting models examined are gradient-enhanced regressors (XGBoost), random forest (RF), and long short-term memory networks (LSTM). The performance of these models was evaluated based on prediction errors to identify the most accurate one. Time series, machine learning, and hybrid models used to predict energy consumption are considered and analyzed. The focus is on the accuracy of forecasts and their applicability in real-world conditions, taking into account factors such as climate change and data obtained from Internet of Things (IoT) sensors. The analysis shows that hybrid models combining machine learning and time series provide the best prediction accuracy over different time horizons. It also highlights the importance of integrating user behavior data and using IoT technologies to improve model accuracy. The results can be applied to create energy-efficient control systems in smart buildings and optimize energy consumption.

DEVELOPMENT OF MACHINE LEARNING METHODS FOR MARKET TRENDS

2024-11-12T22:11:27+05:00

In the rapidly evolving real estate market, the application of machine learning (ML) is crucial for understanding and predicting price trends. This study evaluates and compares seven ML models, including multiple linear regression, random forest regression, support vector regression (SVR), decision tree regression, and XGBoost, to determine the most effective predictor of real estate prices in Astana, Kazakhstan. The study focuses on the Yesil district, a key area in the city, utilizing a dataset of over 9,000 records extracted from a broader collection of more than 30,000 real estate transactions across Kazakhstan. Through rigorous experimentation, model performance was assessed using statistical metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R-squared). The results indicate that the Random Forest Regressor and XGBRegressor models outperformed others, achieving the highest R-squared values (99.55% and 99.18%, respectively) and the lowest MAE and RMSE values. These findings highlight their robustness in predicting housing prices with high accuracy. The primary objective of this study was to develop a precise ML model capable of accurately forecasting real estate prices in Astana based on key market attributes. The superior predictive performance of the Random Forest and XGBRegressor models justifies their selection for deployment in real-world applications. Their high predictive accuracy suggests their potential utility for real estate professionals, policymakers, and investors seeking data-driven insights into market dynamics. This research expands of knowledge on the applications of ML in the real estate sector, reinforcing the importance of evidence-based decision-making within the industry.

CONSTRACTION OF DISTRIBUTION MODELS OF THE UNIVERSITY EDUCATIONAL WORK VOLUME

2024-09-12T11:13:32+05:00

With the advent of new time requirements for the quality of educational services, which is influenced by management in functioning business processes, existing research in the field of resource allocation in the management of complex processes, namely the calculation of the teaching load of university teaching staff, was studied. The purpose of the research in this article is to develop functional and mathematical distribution models of the university educational work volume, as well as an algorithm for optimizing the generation of educational flows and initialization of academic groups, taking into account the specifics of disciplines and classroom fund. The algorithm is based on the construction of all business processes implemented during the formation of educational streams and groups. The functional model described for the process of distributing the volume of educational work includes the definition of the main functions, their relationships, input and output data, as well as the criteria and restrictions that govern this process. The mathematical model is based on the representation of all types of educational work of departments of educational programs as a discrete set of resources that must be distributed between educational departments in accordance with the assumptions and restrictions accepted at the university. Data mining and operations research techniques were used to write the functional model. Empirical and quantitative methods were used to write a mathematical model. Thus, a new methodology has been developed for solving complex optimization problems that arise when modeling and optimizing the distribution of the volume of educational work of a university. It should be noted that comparative experiments under labor-intensive and time-limited conditions confirm the effectiveness of this technique in solving problems of distributing the amount of educational work among departments of educational programs, which in turn contributes to the implementation of high-quality software.