Technology of Construction of Expert Information Web System of Identification and Verification of Priority Ecological Problems in Water Bodies of the River Basin
DOI:
https://doi.org/10.31649/1997-9266-2021-154-1-77-87Keywords:
information web system, water protection technologies, expert assessments, fuzzy sets, environmental problems, water body, RBMP, machine learning, text data classification, natural language processing, NLP, BERTAbstract
The article considers the collection, verification and generalization of a large number of expert assessments of the current state of waters, existing environmental problems and influential factors that increase the risk of failure to achieve environmental goals of each volume of water during the development of river basin management plans (RBMPs) or stabilization of good ecological status of water in the water bodies of this basin. The task is complicated by the large number of such volumes of water, as it is extremely difficult to gather reliable information about the objects located in each of them. Creation of a web system with a map of water volumes and the involvement of a large number of experts from among locals who are not indifferent to the problems of their environment will help to solve this problem. However, then there is the problem of verifying the assessments of these experts, taking into account their different qualifications, experience, different views of the objectives of the RBMP, and the problem of how to compare them to identify the most vulnerable regions by different criteria. It is proposed that experts will be required not only expert assessments on the basis of single directories of possible answers, but also - links to text web resources that confirm their assessments to solve this problem. And then it will be analyzed if these sources really confirm each assessment of the appropriate type of problem for a given region. The authors consider various approaches for comparing expert assessments both based on the basis of fuzzy sets and with the help of machine learning and natural language processing (NLP) technologies. Analogues of the system developed by the authors are considered.
A method has been developed to identify and verify priority of environmental problems in water bodies of the river basin based on fuzzy expert estimates, taking into account the probabilities that the text materials cited by the expert do correspond to this problem. These probabilities are determined to use models of NLP technologies. The stages of functioning of the expert information web system for the implementation of the proposed technology are described, which will simultaneously collect the most reliable and detailed information about the objects of water bodies and accelerate its processing and ranking.
An example of the implementation of an information web system for identifying priority environmental problems in the water bodies of the Southern Bug River basin is given. Examples of calculating the reliability of expert estimates using the author’s program in Python based on NLP-model BERT and logistic regression which were applied to real text information are given.
References
Кабінет Міністрів України, Постанова № 336 від 18.05.2017 року «Про затвердження Порядку розроблення плану управління річковим басейном». [Електронний ресурс]. Режим доступу: https://www.davr.gov.ua/postanova-kabinetu-ministriv-ukraini-vid-18-travnya-2017-roku--336-pro-zatverdzhennya-poryadku-rozroblennya-planu-upravlinnya-richkovim-basejnom .
The EU Water Framework Directive – integrated river basin management for Europe Directive 2000/60/ЕС establishing a framework for the Community action of water policy (Water Framework Directive).
Ю. І. Мітюшкін, Б. І. Мокін, і О. П. Ротштейн, Soft Computing: ідентифікація закономірностей нечіткими базами знань, моногр. Вінниця, Україна: УНІВЕРСУМ-Вінниця, 2002, 145 с. [Електронний ресурс]. Режим доступу: http://mokin.com.ua/files/articles/60/88/SoftComputing.pdf .
В. М. Дубовой, Р. Н. Квєтний, О. І. Михальов, і А. В. Усов, Моделювання та оптимізація систем, підруч. Вінниця, Україна: ПП «ТД«Едельвейс», 2017, 804 с.
Haining Ding, Xiaojian Hu, and Xiaoan Tang «Multiple-attribute group decision making for interval-valued intuitionistic fuzzy sets based on expert reliability and the evidential reasoning rule,» Neural Computing and Applications, vol. 32, pp. 5213-5234, 2020. [Electronic resource]. Available: https://link.springer.com/article/10.1007%2Fs00521-019-04016-z .
Jean-Valère Cossu, Emmanuel Ferreira, Killian Janod, Julien Gaillard, and Marc El-Bèze «NLP-Based Classifiers to Generalize Expert Assessments in E-Reputation,» in International Conference of the Cross-Language Evaluation Forum for European Languages, vol. 9283, pp. 340-351, 2015. [Electronic resource]. Available: https://link.springer.com/chapter/10.1007/978-3-319-24027-5_37 .
Matthew Matero, et al., «Suicide Risk Assessment with Multi-level Dual-Context Language and BERT,» Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pp. 39-44, 2019. [Electronic resource]. Available: https://www.aclweb.org/anthology/W19-3005.pdf .
Er. Samadhan, U. Birajdar, and V. A. Losarwar, «Mining opinion targets and opinion words from reviews using natural language processing (NLP) techniques,» Journal of Critical Reviews, vol. 8, pp. 1029-1036, 2020. [Electronic resource]. Available: http://www.jcreview.com/?mno=95186 .
В. Б. Мокін, «Ідентифікація параметрів малих річок на основі теорії нечітких множин по експертних оцінках та по їх геоінформаційній моделі,» Вісник ЖДТУ, c. 133-142, 2004.
В. Б. Мокін, «Новий підхід до ідентифікації параметрів малих річок за нечіткими експертними оцінками,» Вісник Вінницького політехнічного інституту, № 4, c. 34-41, 2005.
Scikit-learn 0.24.1. Machine Learning in Python. User Guide. [Electronic resource]. Available: https://scikit-learn.org/stable/user_guide.html .
V. Mokin, D. Pasichniuk, O. Radetskyi, and M. Horash, Kaggle Dataset NLP: Reports & News Classification. ENG & UKR Automatic Environmental Reports & News Classification, 2020. [Electronic resource]. Available: https://www.kaggle.com/vbmokin/nlp-reports-news-classification .
Афанасьєв С., та ін., План управління річковим басейном Південного Бугу: аналіз стану та першочергові заходи. Київ, Україна: ТОВ «НВП «Інтерсервіс», 2014, 188 с.
V. Mokin, A. Luchko, and O. Davidyuk, Kaggle. NLP for EN: BERT Predict_proba in Water Report, [Electronic resource]. Available: https://www.kaggle.com/vbmokin/nlp-for-en-bert-predict-proba-in-water-report?scriptVersionId=53779986 .
Downloads
-
PDF (Українська)
Downloads: 198
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
