Information computer system for water resource monitoring

The article discusses a new approach to monitoring water resources using an information computer system with data updating to ensure ecological safety of water bodies under anthropogenic impact. The structure of water resource monitoring system is presented. The collation of hydrochemical, hydrological, physical, microbiological and unwanted parasitic data and parameters of water bodies in this system is demonstrated. A detailed description of the conventional water quality assessment (entropy approach, use of specific combinatorial water pollution index SCWPI) and water quality evaluation by associative types of indexing included in the information computer system is given. The artificial intelligence-based methods are tested, namely, Gradient Boosting ((Xgboost), Random Forest, Logistic Regression, Nearest Neighbors (kNN)) and Neural Network. The best results are obtained using the method of Neural Networks. The parameters for the neural networks are optimized. Water quality impurities are revealed using the correlation matrix with Python and Matplotlib and Seaborn libraries.

Keywords: information system, monitoring, pollution, water quality assessment methods, neural network, data base, water bodies.
For citation:

Potapov V. P., Schastlivtsev E. L., Yukina N. I., Bykov A. A., Kharlampenkov I. E. Information computer system for water resource monitoring. MIAB. Mining Inf. Anal. Bull. 2021;(7):70-84. [In Russ]. DOI: 10.25018/0236_1493_2021_7_0_70.

Acknowledgements:
Issue number: 7
Year: 2021
Page number: 70-84
ISBN: 0236-1493
UDK: 622.5: 504.4.054: 004.9
DOI: 10.25018/0236_1493_2021_7_0_70
Article receipt date: 05.04.2021
Date of review receipt: 06.05.2021
Date of the editorial board′s decision on the article′s publishing: 10.06.2021
About authors:

V.P. Potapov1, Dr. Sci. (Eng.), Professor, Director of the Kemerovo Branch Federal Research Center for Information and Computing Technologies, e-mail: vadimptpv@gmail.com,
E.L. Schastlivtsev1, Dr. Sci. (Eng.), Head of Laboratory, e-mail: schastlivtsev@ict.sbras.ru,
N.I. Yukina1, Cand. Sci. (Eng.), Researcher, e-mail: leonakler@mail.ru,
A.A. Bykov1, Cand. Sci. (Phys. Mathem.), Senior Researcher, e-mail: bykov@icc.kemsc.ru,
I.E. Kharlampenkov1, Cand. Sci. (Eng.), Researcher, e-mail: harlampenkov@ict.sbras.ru,
1 Federal Research Center for Information and Computing Technologies, Kemerovo Branch, Kemerovo, Russia.

 

For contacts:

N.I. Yukina, e-mail: leonakler@mail.ru.

Bibliography:

1. Kravets E. A. Problems of processing and presenting monitoring data to obtain a detailed picture of water pollution. Materialy XIII mezhdunarodnogo simpoziuma problemy ekoinformatiki [XIII Ecoinformatics Symposium Proceedings], Moscow, 2018, pp. 205—208. [In Russ].

2. Mel'nikova T. N. Monitoring of the ecological state of water resources of the Republic of Adygea. Materialy mezhdunarodnoy nauchnoy konferentsii «Bisosfera i chelovek» [International Man and Biosphere Conference Proceedings], Maykop, 2019, pp. 210—213. [In Russ].

3. Bondarenko V. L., Leshchenko A. V. Fundamentals of methodological systemic integrated monitoring of the ecological state of objects of activity in the use of water resources. Materialy vserossiyskoy nauchno-prakticheskoy konferentsii «Melioratsiya i vodnoe khozyaystvo. Puti povysheniya effektivnosti i ekologicheskoy bezopasnosti melioratsiy zemel' yuga Rossii» [Melioration and Water Economy. Improvement of Efficiency and Environmental Safety of Land Reclamation in Southern Russia: All-Russian Conference Proceedings], Novocherkassk, OOO «Lik», 2017, pp. 120—128. [In Russ].

4. Chenskiy D. A., Grigor'ev K. A., Zolotarev N. S., Chenskiy A. G. Robotic catamaran for digital monitoring of water resources. Trudy IX mezhdunarodnoy nauchno-prakticheskoy konferentsii «Morskie issledovaniya i obrazovanie (Maresedu-2020)» [Marine Research and Education: IX International Conference Proceedings], Irkutsk, OOO «PoliPRESS», 2020, pp. 502—504. [In Russ].

5. Ibraev T., Li M. Monitoring i upravlenie vodnymi resursami v respublike Kazakhstan. Monografiya. Pod red. V. G. Sycheva, L. Myullera [Monitoring and management of water resources in the Republic of Kazakhstan. Monograph. Sychev V. G., Myuller L. (Eds.)], Moscow, 2018, pp. 104—109.

6. Fashchevskaya T. B., Motovilov Yu. G., Shaidiyanova N. B. Natural and anthropogenic variations of the concentrations of iron, copper, and zinc in water streams of the republic of bashkortostan. Water Resources. Road Town. 2018, vol. 45, no. 6, pp. 873—886. available at: https://doi.org/10.1134/S0097807818060064 (accessed 12.04.2021).

7. Ghebrehiwot A. A., Kozlov D. V. Spatial and statistical variability analyses of satellitebased climatic data in Mereb-Gash basin. Water Resources. 2021, vol. 48, no. 1, pp. 146–157. available at: https://doi.org/10.1134/S0097807821010152 (accessed 12.04.2021).

8. Gautam S. K., Tripathi J. K., Evangelos T., Singh S. K., Singh A. K. Environmental monitoring of water resources with the use of PoS index: a case study from Subarnarekha river basin, India. Environmental Earth Sciences. 2018, vol. 77, no. 3, pp. 70. available at: https://doi. org/10.1007/s12665-018-7245-5 (accessed 12.04.2021).

9. Karpunichev A. V. Otsenka kachestva i opredelenie zagryaznennosti vody na primere poverkhnostnykh vodoemov. Sbornik materialov mezhdunarodnykh nauchno-prakticheskikh konferentsiy [Assessment of the quality and determination of water pollution on the example of surface water bodies. Collection of materials of international scientific and practical conferences], Moscow, 2018, pp. 536—538.

10. Guanhua Zhang, Wenfeng Ding, Huiying Liu, Liang Yi, Xu Lei, Ouyang Zhang Quantifying climatic and anthropogenic influences on water discharge and sediment load in Xiangxi river basin of the three gorges reservoir area. Water Resources. 2021, vol. 48, pp. 204–218. available at: https://doi.org/10.1134/S0097807821020184 (accessed 12.04.2021).

11. Runxiang Cao, Li F., Zhao Y. Dynamic regulation of reservoir drought limit water level. Water Resources. 2021, vol. 48, pp. 194–203. available at: https://doi.org/10.1134/ S0097807821020147 (accessed 12.04.2021).

12. Babayan G., Reshetnyak O., Zakrutkin V. A comparative assessment of river water quality in mountain regions of Russia and Armenia. Water Resources. 2021, vol. 48, pp. 102–110. available at: https://doi.org/10.1134/S0097807821010115 (accessed 12.04.2021).

13. Pavlova A. S., Sandimirov S. S., Kudryavtseva L. P. The distribution of chemical elements between ecosystem components in Belaya Bay, Lake Imandra, Murmansk Oblast. Water Resources. 2021, vol. 48, pp. 73–81. available at: https://doi.org/10.1134/ S0097807821010231 (accessed 12.04.2021).

14. Bin Pan, Mei Han, Yunlong Li, Min Wang, Huan Du An analysis on the trend of sustainable utilization of water resources in Dongying City, China. Water Resources. 2021, vol. 48, pp. 158–166. available at: https://doi.org/10.1134/S009780782101022X (accessed 12.04.2021).

15. Logov А. B., Oparin V. N., Potapov V. P., Schastlivtsev E. L., Yukina N. I. Entropy analysis of process wastewater composition in mineral mining. Journal of Mining Science. 2015, vol. 51, no. 1, pp. 186–196.

16. Metod kompleksnoy otsenki stepeni zagryaznennosti poverkhnostnykh vod po gidrokhimicheskim pokazatelyam. Metodicheskie ukazaniya RD 52.24.643-2002 ot 03.12.2002g. [Method for a comprehensive assessment of the degree of pollution of surface waters by hydrochemical indicators. Methodical instructions RD 52.24.643-2002 from 03.12.2002]. [In Russ].

17. Potapov V. P., Mazikin V. P., Schastlivtsev E. L., Vashlaeva N. Yu. Geoekologiya ugledobyvayushchikh rayonov Kuzbassa [Geoecology of coal-mining regions of Kuzbass], Novosibirsk, Nauka, 2005. 660 p.

18. Kovalev V. A., Potapov V. P., Schastlivtsev E. L., Shokin Yu. I. Modelirovanie geoekologicheskikh sistem ugledobyvayushchikh rayonov [Modeling geoecological systems of coal mining areas], Novosibirsk, Izd-vo SO RAN, 2015, 298 p.

19. Schastlivtsev E. L., Yukina N. I., Kharlampenkov I. E. Information and analytical system of geoecological monitoring of water resources in a coal mining region. Vestnik Kuzbasskogo gosudarstvennogo tekhnicheskogo universiteta. Kemerovo. 2016, no. 2 (114), pp. 157—164. [In Russ].

20. Dontsov A. A., Sutorikhin I. A. Specialized geographic information system for monitoring inland water resources. Tezisy dokladov vserossiyskoy nauchnoy konferentsii «Monitoring sostoyaniya i zagryazneniya okruzhayushchey sredy. osnovnye rezul'taty i puti razvitiya» [Environment and Pollution Monitoring. Major Results and Development Routes: All-Russian Conference Head-Notes], Moscow, 2017, pp. 372—373. [In Russ].

21. Nemčić-Jurec J., Singh S. K., Jazbec A., Gautam S. K., Kovac I. Hydrochemical investigations of groundwater quality for drinking and irrigational purposes: two case studies of Koprivnica-Krizˇevci County (Croatia) and district Allahabad (India). Sustainable Water Resources Management. 2017, vol. 5, no. 4. available at: https://doi.org/10.1007/s40899-017-0200-x (accessed 12.04.2021).

22. Barzegar R., Asghari Moghaddam A., Tziritis E., Sajjad Fakhri M., Soltani S. Identification of hydrogeochemical processes and pollution sources of groundwater resources in the Marand plain, northwest of Iran. Environmental Earth Sciences. 2017, vol. 76, no. 7, pp. 296. available at: https://doi.org/10.1007/s12665-017-6612-y (accessed 12.04.2021).

23. Karapetyan T. A. Using artificial neural networks to assess and predict river sediments. Molodoy uchenyy. 2019, no. 17 (255), pp. 29—32. [In Russ]. available at: https://moluch.ru/ archive/255/58486/ (accessed 12.04.2021).

24. Potapov V. P., Schastlivtsev E. L., Yukina N. I., Kharlampenkov I. E. Deep neural networks for water quality assessment. MIAB. Mining Inf. Anal. Bull. 2019. Special edition 37, pp. 569–577. [In Russ]. DOI: 10.25018/0236-1493-2019-11-37-569-577.

Our partners

Подписка на рассылку

Раз в месяц Вы будете получать информацию о новом номере журнала, новых книгах издательства, а также о конференциях, форумах и других профессиональных мероприятиях.