Improvement of methods of hydrological forecasting using geoinformation technologies
Abstract This article discusses the possibility of improving hydrological forecasting methods based on a neural network. The hydrological series, its importance and forecasting features are considered. For hydrological forecasting using the MapInfoProfessional geoinformation system, an electronic ma...
Published in: | Journal of Physics: Conference Series |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
IOP Publishing
2021
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1088/1742-6596/2131/3/032069 https://iopscience.iop.org/article/10.1088/1742-6596/2131/3/032069 https://iopscience.iop.org/article/10.1088/1742-6596/2131/3/032069/pdf |
id |
crioppubl:10.1088/1742-6596/2131/3/032069 |
---|---|
record_format |
openpolar |
spelling |
crioppubl:10.1088/1742-6596/2131/3/032069 2024-06-02T08:12:26+00:00 Improvement of methods of hydrological forecasting using geoinformation technologies Zueva, A Shamova, V Pilipenko, T 2021 http://dx.doi.org/10.1088/1742-6596/2131/3/032069 https://iopscience.iop.org/article/10.1088/1742-6596/2131/3/032069 https://iopscience.iop.org/article/10.1088/1742-6596/2131/3/032069/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining Journal of Physics: Conference Series volume 2131, issue 3, page 032069 ISSN 1742-6588 1742-6596 journal-article 2021 crioppubl https://doi.org/10.1088/1742-6596/2131/3/032069 2024-05-07T14:06:36Z Abstract This article discusses the possibility of improving hydrological forecasting methods based on a neural network. The hydrological series, its importance and forecasting features are considered. For hydrological forecasting using the MapInfoProfessional geoinformation system, an electronic map has been developed containing information about the rivers of Russia, as well as gauging stations on the Ob River. The electronic map is the basis for creating a module for short-term hydrological forecasting based on an artificial neural network. The features of a neural network, methods of its training and implementation are considered. The developed artificial neural network is a layer of neurons with a linear activation function and a delay line at the input. To predict the levels of hydrological series, real water levels at gauging stations of the Ob River in the Novosibirsk region will be used. The developed module and its capabilities have been tested. The study was carried out on the basis of models of hydrological series, as well as on the basis of levels of real hydrological series. Based on the study, dependence of the root-mean-square error on the number of previous values of series was revealed. The study also shows that it is possible to use a neural network for the current one-step forecasting of levels of hydrological series in conditions of insufficient information about the runoff region and its characteristics. Article in Journal/Newspaper ob river IOP Publishing Journal of Physics: Conference Series 2131 3 032069 |
institution |
Open Polar |
collection |
IOP Publishing |
op_collection_id |
crioppubl |
language |
unknown |
description |
Abstract This article discusses the possibility of improving hydrological forecasting methods based on a neural network. The hydrological series, its importance and forecasting features are considered. For hydrological forecasting using the MapInfoProfessional geoinformation system, an electronic map has been developed containing information about the rivers of Russia, as well as gauging stations on the Ob River. The electronic map is the basis for creating a module for short-term hydrological forecasting based on an artificial neural network. The features of a neural network, methods of its training and implementation are considered. The developed artificial neural network is a layer of neurons with a linear activation function and a delay line at the input. To predict the levels of hydrological series, real water levels at gauging stations of the Ob River in the Novosibirsk region will be used. The developed module and its capabilities have been tested. The study was carried out on the basis of models of hydrological series, as well as on the basis of levels of real hydrological series. Based on the study, dependence of the root-mean-square error on the number of previous values of series was revealed. The study also shows that it is possible to use a neural network for the current one-step forecasting of levels of hydrological series in conditions of insufficient information about the runoff region and its characteristics. |
format |
Article in Journal/Newspaper |
author |
Zueva, A Shamova, V Pilipenko, T |
spellingShingle |
Zueva, A Shamova, V Pilipenko, T Improvement of methods of hydrological forecasting using geoinformation technologies |
author_facet |
Zueva, A Shamova, V Pilipenko, T |
author_sort |
Zueva, A |
title |
Improvement of methods of hydrological forecasting using geoinformation technologies |
title_short |
Improvement of methods of hydrological forecasting using geoinformation technologies |
title_full |
Improvement of methods of hydrological forecasting using geoinformation technologies |
title_fullStr |
Improvement of methods of hydrological forecasting using geoinformation technologies |
title_full_unstemmed |
Improvement of methods of hydrological forecasting using geoinformation technologies |
title_sort |
improvement of methods of hydrological forecasting using geoinformation technologies |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
http://dx.doi.org/10.1088/1742-6596/2131/3/032069 https://iopscience.iop.org/article/10.1088/1742-6596/2131/3/032069 https://iopscience.iop.org/article/10.1088/1742-6596/2131/3/032069/pdf |
genre |
ob river |
genre_facet |
ob river |
op_source |
Journal of Physics: Conference Series volume 2131, issue 3, page 032069 ISSN 1742-6588 1742-6596 |
op_rights |
http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1742-6596/2131/3/032069 |
container_title |
Journal of Physics: Conference Series |
container_volume |
2131 |
container_issue |
3 |
container_start_page |
032069 |
_version_ |
1800758851292626944 |