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.
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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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