Scientific work

Description and purpose of the project

Functionality and technologies used

Project Scientific Team

Publications on the subject of the project

Project Description

Nowadays, specialized intelligent systems based on machine learning and deep learning are becoming increasingly popular for decision support in exceptional and emergency situations. Exceptional situations include floods. In this connection, there is a problem of advance forecasting of water level rise at stationary hydrological stations in order to minimize the risk of material damage and prevent the threat to human life.

To solve this problem, an intelligent decision support system for advance prediction of water level rise and modeling of flood zones based on a neural network analysis of retrospective data is proposed.

The following data for the last 25 years were taken for training the artificial neural networks: code of the stationary hydrological post, date, water level at the stationary hydrological post, atmospheric pressure, wind speed, snow cover thickness, precipitation and air temperature.

Objective of the project

Thus, the goal of the project is to calculate the water level for a certain number of days ahead. For mathematical calculations of predicted values, we developed our own machine learning library using the Java programming language. The main tool is a recurrent neural network developed from scratch, and for its training we used a modified method of back propagation of error, the main difference of which is the addition of a weight coefficient of matrix sensitivity, a function of gradient dependence on time and its own deviation metric. This makes it possible to achieve prediction accuracy in the range of 90-100%.

At the same time, the main difficulty in advance prediction is the sharp increase in water level over a short period of time (usually 1 day), caused by various reasons. Our neural network models take this into account and give more accurate results.

Functionality and technologies used

1. Neural network prediction of water level at the hydrological post. At the moment, the region "Republic of Bashkortostan" is available for forecasting, in the future other regions of the Russian Federation, as well as hydrological posts of other countries will be added. Available for all users.

2. Neural network modeling of possible area flooding based on the predicted values of water levels. Available to all users.

3. Automatic data retrieval from open sources of hydrological post coordinates to populate the database for training the artificial neural networks. Available for administrators only.

4. Reconstruction of missing data using machine learning techniques to improve the accuracy of forecast values. Available for administrators only.

5. Forecast data visualization and creation of statistical reports. Available for all users.

A consistent stack of technologies was used to implement on the Ubuntu operating system:

1. Backend: programming language «Java», «Apache Maven» + «Spring Boot», ORM (Object-Relational Mapping) + JPA (Java Persistence API) + Hibernate, database «MySQL».

2. Frontend: HTML, CSS, Bootstrap, OpenStreetMap, JavaScript.

Project Scientific Team

Palchevsky Evgeny Vladimirovich – Project Manager, Senior Lecturer, Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation.

Antonov Vyacheslav Viktorovich – scientific supervisor of the project, Doctor of Technical Sciences, Professor, Head of the Department of Automated Control Systems of the Ufa University of Science and Technology.

Rodionova Lyudmila Evgenievna – PhD, Associate Professor, Department of Automated Control Systems, Ufa University of Science and Technology.

Kromina Lyudmila Aleksandrovna – PhD, Associate Professor, Department of Automated Control Systems, Ufa University of Science and Technology.

Fakhrullina Almira Raisovna – PhD, Associate Professor, Department of Automated Control Systems, Ufa University of Science and Technology.

Breikin Tim – PhD and Senior Lecturer in Mathematics and Engineering at Sheffield Hallam University, UK.

Publications on the subject of the project

1. Palchevsky, E.V. A system based on an artificial neural network of the second generation for decision support in especially significant situations / E.V. Palchevsky, V.V. Antonov, R.R. Enikeev, T. Breikin // Journal of Hydrology. Vol. 616. – Elsevier, 2023. – pp. 128844. (Scopus: Q1, WoS: Q1)

Link to publication

2. Palchevsky E.V. Visualization of flood zones based on time series forecasting and GIS technologies / E.V. Palchevsky, V.V. Antonov, L.E. Rodionova, L.A. Kromina, A.R. Fakhrullina // Computer Optics. 2023. (Scopus: Q2, WoS: Q3)

The link to the publication will appear after the publication of the article.

3. Palchevsky, E.V. Threats complex distributed systems parrying based on their development prognostication / E.V. Palchevsky, O.I. Khristodulo, S.V. Pavlov // Advances in Social Science, Education and Humanities Research. Vol. 483. – Atlantis Press, 2020. – С. 191-194. (Web of Science)

Link to publication

4. Palchevsky, E.V. Threat prediction in complex distributed systems using artificial neural network technology / E.V. Palchevsky, O.I. Khristodulo, S.V. Pavlov // CEUR Workshop Proceedings. Vol. 2763. - CEUR Workshop, 2020. - C. 289-284. (Scopus)

Link to publication

5. Palchevsky, E.V. Intelligent data analysis for forecasting threats in complex distributed systems / E.V. Palchevsky, O.I. Khristodulo, S.V. Pavlov, A.M. Kalimgulov // CEUR Workshop Proceedings. Vol. 2744. - CEUR Workshop, 2020. - C. 285-296. (Scopus)

Link to publication

6. Palchevsky, E.V. Decision support system based on application of the second generation neural network / E.V. Palchevsky, V.V. Antonov // Programmnaya Ingeneria. Vol. 13. No. 6. - New technologies, 2022. - С.301-308. (RSCI)

Link to publication

7. Palchevsky, E.V. Forecasting based on an artificial neural network of the second generation for decision support in especially significant situations / E.V. Palchevsky, V.V. Antonov, R.R. Enikeev // Software & Systems. Volume 35. Issue 3. – Publisher: ZAO NII "Tsentrprogramsistem", Tver, 2022. – pp. 488-503. (RSCI)

Link to publication

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