{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T20:00:20Z","timestamp":1782417620810,"version":"3.54.5"},"reference-count":7,"publisher":"STEF92 Technology","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,1]]},"abstract":"<jats:p>Accurate river flow forecasting is an extremely important issue for proper management and optimal use of water resources as well as for warnings of extreme hydrometeorological events. Rainfall-runoff simulation is essential for short and longterm forecasting of the river discharge. Determining the relationship between rainfall and runoff is one of the most important tasks faced by hydrologists. This relationship is a nonlinear and extremely complex process influenced by many factors such as watershed topology, vegetation cover, soil types, river bed characteristics, groundwater aquifers, precipitation distribution, snowmelt, rural and urban activities. \nArtificial Neural Networks (ANNs) are known as powerful and flexible models and are widely used in hydrology and forecasting. \nThis paper aims to demonstrate the research and operational application of ANN in hydrologic modeling to construct an effective operational forecasting system of stream flow and potential flood risks in the studied area. The studied area is the Struma river Basin. The availability of long historical records and a good physical understanding of the hydrologic process in the area are very important in selecting the input predictors and designing a more efficient network. Historical data from automatic stations for the period 2015 - 2022 is selected to create the networks. The six hourly precipitation, daily temperature and runoff data from eleven subwatersheds are collected and used in developing the ANN. Additional analyses of lags are performed using correlation analysis of runoff at hydrometric stations at the outlet of the watersheds and correlation analysis of runoff and accumulated precipitation data in watersheds. The statistical estimates are Nash Sutcliffe model 0.8 - 0.9, MSE - 0.04 - 0.149, MAE 0.13 -0.176 and R 0.8-0.98. Operational forecasting is based on data from the global weather model ECMWF.<\/jats:p>","DOI":"10.5593\/sgem2023\/3.1\/s12.13","type":"proceedings-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T06:07:41Z","timestamp":1697090861000},"page":"107-114","source":"Crossref","is-referenced-by-count":1,"title":["NEURAL NETWORK-BASED MODELS FOR STRUMA RIVER FLOW FORECASTING"],"prefix":"10.5593","volume":"23","author":[{"given":"Snezhanka","family":"Balabanova","sequence":"first","affiliation":[{"name":"National Institute of Meteorology and Hydrology, BAS","place":["Bulgaria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vesela","family":"Stoyanova","sequence":"additional","affiliation":[{"name":"National Institute of Meteorology and Hydrology, BAS","place":["Bulgaria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeriya","family":"Yordanova","sequence":"additional","affiliation":[{"name":"National Institute of Meteorology and Hydrology, BAS","place":["Bulgaria"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3602","reference":[{"key":"ref=1","doi-asserted-by":"crossref","unstructured":"[1]. S. Stoyanova, G. Koshinchanov (2019) Sensitivity analyses of conceptual and semidistributed hydrological models applied over a pilot basin, International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM 19(3.1), pp. 513-520","DOI":"10.5593\/sgem2019\/3.1\/S12.066"},{"key":"ref=2","unstructured":"[2]. Georgy Koshinchanov (2012), Calibration of water levels and discharges using the HD module of MIKE11 platform (on the example of Maritsa river), Annuaire de Universite de Sofia \ufffdSt. Kliment Ohridski\ufffd, Faculte de Geologie et Geographie, Livre 2 \ufffd Geographie, Tome 103, pp 419-432, ISSN 0324-2579"},{"key":"ref=3","unstructured":"[3]. Silviya Stoyanova \ufffdHydrological modeling for Water Balance Components Assessment\ufffd \ufffd\ufffd: XXIX Conference of The Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management, Conference proceedings, Extended abstracts, September 6\ufffd8, 2021 Brno, Czech Republic, Czech Hydrometeorological Institute, ISBN 978-80-7653-020-1"},{"key":"ref=4","doi-asserted-by":"crossref","unstructured":"[4]. Bojilova. E. 2020. Applicability of Rainfall-Runoff Models to the Condtions of River Runoff in Bulgaria. - 20th International Multidisciplinary Scientific GeoConference 18-24 August, Proceedings SGEM 2020, Book 3.1, 27-34, ISBN: 978- 619-7603-08-8, ISSN: 1314-2704, DOI: 10.5593\/sgem2020\/3.1\/s12.004 (Scopus)","DOI":"10.5593\/sgem2020\/3.1\/s12.004"},{"key":"ref=5","unstructured":"[5]. Artificial Neural Networks in Hydrology \ufffd R.S. Govindaju and A. Ramachandra Rao, School of Civil Engineering Purdue University USA, ISBN: 978-90-481-5421-0"},{"key":"ref=6","doi-asserted-by":"crossref","unstructured":"[6]. Yaseen, Z.M.; El-Shafie, A.; Jaafar, O.; Afan, H.A.; Sayl, K.N. Artificial intelligence based models for stream-flow forecasting: 2000\ufffd2015. J. Hydrol. 2015, 530, 829\ufffd844.","DOI":"10.1016\/j.jhydrol.2015.10.038"},{"key":"ref=7","unstructured":"[7]. Directive 2007\/60\/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks."}],"event":{"name":"23rd SGEM International Multidisciplinary Scientific GeoConference 2023","theme":"Earth and Planetary Sciences","location":"Albena, Bulgaria","acronym":"SGEM2023","number":"23","sponsor":["SGEM WORLD SCIENCE (SWS) Scholarly Society, Austria"],"start":{"date-parts":[[2023,7,3]]},"end":{"date-parts":[[2023,7,9]]}},"container-title":["SGEM International Multidisciplinary Scientific GeoConference\ufffd EXPO Proceedings","23rd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2023, Water Resources. Forest, Marine and Ocean Ecosystems, Vol 23, Issue 3.1"],"original-title":[],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T19:27:10Z","timestamp":1782415630000},"score":1,"resource":{"primary":{"URL":"https:\/\/epslibrary.at\/items\/7e92bb8e-fc9c-4cf3-886f-38cce2ae00e0\/neural-network-based-models-for-struma-river-flow-forecasting"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,1]]},"references-count":7,"URL":"https:\/\/doi.org\/10.5593\/sgem2023\/3.1\/s12.13","relation":{},"ISSN":["1314-2704"],"issn-type":[{"value":"1314-2704","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10,1]]}}}