Peer-reviewed articles 17,970 +


Title: POSSIBILITIES FOR DAY-STEP FLOOD FORECASTING IN SMALLER CATCHMENTS USING MACHINE LEARNING METHODS
POSSIBILITIES FOR DAY-STEP FLOOD FORECASTING IN SMALLER CATCHMENTS USING MACHINE LEARNING METHODS

PlumX Article Metrics by Elsevier
Tomas Kozel; Ruzena Pavelkova
10.5593/sgem2024v/3.2/s11.11
10.5593/sgem2024v/3.2
1314-2704
978-619-7603-76-7
English
24
3.2
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Hydrology, Water Resources, and Climate Resilience
Today, it is possible to work with a wide range of freely available data, often on a daily basis. These data can be used to create an early warning system for estimating the approximate hazard. It can also be used to develop models of long-term catchment behaviour. Most applications are generally carried out on catchments larger than 200 km2. For this reason, areas between 20 and 350 km2 were selected to test the hypothesis using models based on machine learning methods. How good results can be achieved when using daily data to predict increased flows caused by previous precipitation (summer) or a combination of snowmelt and precipitation (winter). The one day step was chosen for availability data (free data) and for testing area size limit for this step. The results showed that floods caused by a combination of rain and snowmelt were significantly better than those caused by rain alone. Two methods were compared. The neural networks ANN and fuzzy model. For both methods were founded the best architecture in training period. The results of the experiment showed that the limit of applicability of the data is above (around) 130 km2 in the case of pure rainfall. In the case of floods caused by a combination of rain and snow, the daily step can be used even for catchments of about 20 km2 with a one-day time shift.
[1] Apaydin, H., Sattari, M.T., Falsafian, K., Prasad, R., 2021 Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions. J. Hydrol, 600, 126506.
[2] Bezdec, J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York,
[3] Bras, R.L., 19990. Hydrology: An Introduction to Hydrologic Science. Addison Wesley, New York, NY. ISBN: 978-0201059229.
[4] Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrol Sci J 43(1):47–66
[5] Hopfield, J. J., 1982. Neural networks and physical systems with emergent collective computational abilities". Proceedings of the National Academy of Sciences. 79 (8): 2554–2558.
[6] Jiang G.,Wang W., 2017. Error estimation based on variance analysis of k-fold cross-validation, Pattern Recognition, Volume 69, Pages 94-106, ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2017.03.025.
[7] Leskovar, K., Tezak, D., Mesec, J., Biondic, R., 2021. Influence of Meteorological Parameters on Explosive Charge and Stemming Length Predictions in Clay Soil during Blasting Using Artificial Neural Networks. Appl. Sci. 2021, 11, 7317.
[8] Kozel, T., Stary, M., 2022. Adaptive Stochastic Management of the Storage Function for a Large, Open Reservoir Using Learned Fuzzy Models. Journal of Hydrology and Hydromechanics 70, no. 2 213–21. https://doi.org/10.2478/johh-2022-0010.
[9] Rezaeianzadeh, M., Tabari, H., Yazdi, A.A., Isik, S., Kalin, L. Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput. Appl. 2014, 25, 25–37. https://doi.org/10.1007/s00521-013-1443-6
[10] Seo Y, Kim S, Kisi O, Singh VP (2014) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol 520:224–243
[11] Tagaki, H., Sugeno, M., 1985. Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics, SMC-15, 1, 116–132.
Acknowledgements: The article was supported by grant FAST-S-24.
conference
https://doi.org/10.5593/sgem2024v/3.2/s11.11
Download PDF
Proceedings of 24th International Multidisciplinary Scientific GeoConference SGEM 2024
24th International Multidisciplinary Scientific GeoConference SGEM 2024, 27 - 30 November, 2024
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
85-92
27 - 30 November, 2024
website
10057
Daily data, floods, snow, machine learning methods, small catchments

25th SGEM International Conference on Earth & Planetary Sciences


International GeoConference SGEM2025
27 June - 6 July, 2025 / Albena, Bulgaria

Read More		   

SGEM Vienna GREEN "Green Science for Green Life"


Extended Scientific Sessions SGEM Vienna GREEN
3 -6 December, 2025 / Vienna, Austria

Read More
   

A scientific platform for Art-Inspired Scientists!


The Magical World Where Science meets Art
Vienna, Austria

Read More