International Journal of Innovative Research and Scientific Studies

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Integrating satellite data and machine learning algorithms into a flood prediction system
(2025-07-19)
Gulzat Z. Ziyatbekova
;
Dauren Darkenbayev
;
Shyraigul Shekerbayeva
;
Aisulu Zhaksymbet
The objective of this study is to develop an intelligent flood forecasting system based on the integration of satellite and ground-based hydrometeorological data using machine learning algorithms in the Orange visual analytical environment. The aim of the article is to improve the accuracy and efficiency of forecasting extreme hydrological events, as well as to simplify the process of building forecasting models through the use of an interface that does not require programming. The methodological basis of the study is the formation of a synthetic multivariate dataset combining satellite vegetation indices NDVI and LST temperature with meteorological parameters such as precipitation, temperature, humidity, and water level. Random forest, gradient boosting (XGBoost), and multilayer perceptron (MLP) algorithms were used to build and validate the models. All stages from data loading and pre-processing to visualization and interpretation of results are implemented in the Orange environment using cross-validation and feature significance assessment. The results obtained demonstrated high forecast accuracy (up to 94%), especially when using ensemble and deep models. The significance of satellite data is confirmed by analyzing the contribution of features to the final classification. The developed forecasting model can be adapted to various geographical conditions and integrated into existing monitoring and early warning platforms. The proposed approach has high applied value and demonstrates the potential for using modern big data analysis technologies and artificial intelligence methods in the tasks of reducing the risk of natural floods.
Цитирования (OpenAlex) 1Цитирования (OpenCitations) 1
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Machine learning-based predictive maintenance system for urban heating networks for real-time failure detection and analysis
(2025-09-19)
Dauren Darkenbayev
;
Uzak K. Zhapbasbayev
;
Gulnar Tultayevna Balakayeva
This study presents a comprehensive framework for predictive maintenance of urban heat supply networks utilizing advanced machine learning algorithms. The primary objective is to enable early detection of potential system failures, thereby improving operational reliability and minimizing unplanned downtimes. A synthetically generated dataset of 10,000 records was employed, simulating real – world operational parameters such as temperature, pressure, flow rate, and vibration, sampled at 5–minute intervals to replicate actual monitoring conditions. Data preprocessing involved outlier removal using the interquartile range (IQR) method, normalization through Min-Max scaling, and imputation of missing values, ensuring data quality and consistency. Feature importance was further analyzed using SHAP values to enhance interpretability and identify critical predictors influencing system behavior. Five machine learning models – Logistic Regression, Support Vector Machine (SVM), Random Forest, Artificial Neural Networks (ANN), and Gradient Boosting (LightGBM) – were implemented and evaluated using 10 – fold cross – validation. The Gradient Boosting model demonstrated superior performance, achieving an accuracy of 99.9%, F1-score of 0.999, ROC-AUC of 1.0, and LogLoss of 0.004. Logistic Regression and Random Forest also performed well (AUC = 1.0, F1 = 0.999), whereas SVM and ANN exhibited limited predictive capabilities (AUC ≈ 0.50, F1 = 0.038 and 0.632, respectively). These results underscore the robustness of Gradient Boosting in modeling complex nonlinear relationships and its applicability for real-time anomaly detection in heating systems. The proposed framework holds significant practical potential for integration into existing monitoring infrastructures, facilitating proactive maintenance planning, optimizing resource allocation, and reducing operational costs. Future research will focus on validating the approach with real – world datasets and exploring hybrid machine learning architectures to enhance model generalizability and resilience.
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Flood forecasting using machine learning methods in a visual programming environment
(2025-07-17)
Gulzat Z. Ziyatbekova
;
Dauren Darkenbayev
Floods are among the most destructive natural phenomena, significantly impacting the environment, economy, infrastructure, and public safety. Effective forecasting of such emergencies is crucial, especially in the context of global climate change and the increasing population density in coastal and low-lying areas. This work aims to develop and analyze flood forecasting models using machine learning algorithms within the Orange visual software environment. The study employed five algorithms: Random Forest, Decision Tree, Gradient Boosting, AdaBoost, and K-Nearest Neighbors. A comparative analysis of these models was conducted using key classification metrics, including Accuracy, Precision, Recall, and AUC. Special emphasis was placed on visualizing the results and assessing the usability of models within the Orange environment. The findings can be valuable for educational purposes, such as teaching students to work with real environmental data, as well as for practical applications in early warning systems and flood monitoring.

International Journal of Innovative Research and Scientific Studies

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