Abstract
In this paper, there are presented the risk assessment modeling of all rivers in the River Ibar Catchment that have been flooding or have a potential for flooding of agriculture land, houses, roads, bridges, and other objects. For each river, those flooded or potentially flooded surfaces are presented by category of risk (high risk, medium risk, and low risk) as well as the causes of the flooding and recommendations for short term and long term activity protection against floods. All inputs for the flood risk assessment (water cycle analysis, lake-level prediction, evapotranspiration, climatic conditions) were simulated by using different modeling techniques. By analyzing the locations and vicinity of the human activities, it sets the river priority for intervention. This is enabled by the information presented through the Geographical Information System Elements (GIS) of the Water Framework Directive. Although the information presented by GIS depends on the availability of the spatial and field data, it is a valuable tool in risk assessment in determining the cumulative sensitivity of the specific region to the floods.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Blum, U., & Gerig, T. M. (2006). Interrelationships between p-coumaric acid, evapotranspiration, soil water content, and leaf expansion. Journal of Chemical Ecology, 32(8), 1817–1834.
Buchtele, J., & Tesar, M. (2009). The time variability of evapotranspiration and soil water storage in long series of rainfall-runoff process. Biologia, 64(3), 575–579.
Cai, C. Z., Zhu, X. J., Wen, Y. F., Pei, J. F., Wang, G. L., & Zhuang, W. P. (2010). Predicting the superconducting transition temperature T c of BiPbSrCaCuOF superconductors by using support vector regression. Journal of Superconductivity and Novel Magnetism, 23(5), 737–740.
de la Paix Mupenzi, J., Li, L., Ge, J., Ngamije, J., Achal, V., Habiyaremye, G., et al. (2012). Water losses in arid and semi-arid zone: Evaporation, evapotranspiration and seepage. Journal of Mountain Science, 9(2), 256–261.
Djokic, J., Minic, D., Kamberovic, Z., & Petkovic, D. (2012). Impact analysis of airborn pollution due to magnesium slag deposit and climatic changes condition. Ecological Chemistry and Engineering, 19(3), 439–444.
Dong, Q., Zhan, C., Wang, H., Wang, F., & Zhu, M. (2016). A review on evapotranspiration data assimilation based on hydrological models. Journal of Geographical Sciences, 26(2), 230–242.
Gao, G., Xu, C. Y., Chen, D., & Singh, V. P. (2012). Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China. Stochastic Environmental Research and Risk Assessment, 26(5), 655–669.
Gerla, P. J. (1992). The relationship of water-table changes to the capillary fringe, evapotranspiration, and precipitation in intermittent wetlands. Wetlands, 12(2), 91–98.
Gocic, M., Shamshirband, S., Razak, Z., Petković, D., Ch, S., & Trajkovic, S. (2016). Long-term precipitation analysis and estimation of precipitation concentration index using three support vector machine methods. Advances in Meteorology, (Article ID 7912357), 11. https://doi.org/10.1155/2016/7912357.
Gong, Y., Zhang, Y., Lan, S., & Wang, H. (2016). A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near lake Okeechobee, Florida. Water Resources Management, 30(1), 375–391.
Ilic, M., Jovic, S., Spalevic, P., & Vujicic, I. (2017). Water cycle estimation by neuro-fuzzy approach. Computers and Electronics in Agriculture, 135, 1–3.
Itier, B., Flura, D., Belabbes, K., Kosuth, P., Rana, G., & Figueiredo, L. (1992). Relations between relative evapotranspiration and predawn leaf water potential in soybean grown in several locations. Irrigation Science, 13(3), 109–114.
Jang, J. S. R., Sun, C. T., Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence.
Jovic, S., Nedeljkovic, B., Golubovic, Z., & Kostic, N. (2018a). Evolutionary algorithm for reference evapotranspiration analysis. Computers and Electronics in Agriculture, 150, 1–4.
Jovic, S., Vasic, P., & Jaksic, T. (2018b). Sensorless estimation of lake level by soft computing approach. Sensor Review, 38(1), 117–119.
Kakahaji, H., Banadaki, H. D., Kakahaji, A., & Kakahaji, A. (2013). Prediction of Urmia lake water-level fluctuations by using analytical, linear statistic and intelligent methods. Water Resources Management, 27(13), 4469–4492.
Kalaba, D. V., Ivanović, I., Čikara, D., & Milentijević, G. (2014). The Initial analysis of the River Ibar temperature downstream of the lake Gazivode. Thermal Science, 18(1), 73–80.
Kisi, O., & Yildirim, G. (2005). Discussion of “Forecasting of reference evapotranspiration by artificial neural networks” by Slavisa Trajkovic, Branimir Todorovic, and Miomir Stankovic. Journal of Irrigation and Drainage Engineering, 131(4), 390. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:4(390).
Liu, Y., Zhuang, Q., Pan, Z., Miralles, D., Tchebakova, N., Kicklighter, D., et al. (2014). Response of evapotranspiration and water availability to the changing climate in Northern Eurasia. Climate Change, 126(3–4), 413–427.
Meng, J., & Xia, L. (2007). Support vector regression model for millimeter wave transitions. International Journal of Infrared and Millimeter Waves, 28(5), 413–421.
Milentijević, G., Spalević, Ž., Bjelajac, Ž., Djokić, J., & Nedeljković, B. (2013). Impact analysis of mining company ‘Trepča’ to the Contamination of the river Ibar Water, National Vs. European law regulations. Metalurgia International, 18, 283–288.
Morari, F., & Giardini, L. (2001). Estimating evapotranspiration in the Padova botanical garden. Irrigation Science, 20(3), 127–137.
Qin, D., Lu, C., Liu, J., Wang, H., Wang, J., Li, H., et al. (2014). Theoretical framework of dualistic nature–social water cycle. Chinese Science Bulletin, 59(8), 810–820.
Rana, G., Katerji, N., Mastrorilli, M., & El Moujabber, M. (1997). A model for predicting actual evapotranspiration under soil water stress in a Mediterranean region. Theoretical and Applied Climatology, 56(1–2), 45–55.
Sanikhani, H., Kisi, O., Kiafar, H., & Ghavidel, S. Z. Z. (2015). Comparison of different data-driven approaches for modeling lake level fluctuations: the case of Manyas and Tuz lakes (Turkey). Water Resources Management, 29(5), 1557–1574.
Shafaei, M., & Kisi, O. (2016). Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resources Management, 30(1), 79–97.
Stanojevic, P., Djokic, J., Zivkovic, B., & Rajovic, J. (2018). GIS application in floods risk assessment in Leposavic. In Proceedings of 9th GRACM International Congress on Computational Mechanics, Chania, June 4–6, 2017 (pp. 195–201).
Tongal, H., & Berndtsson, R. (2014). Phase-space reconstruction and self-exciting threshold modeling approach to forecast lake water levels. Stochastic Environmental Research and Risk Assessment, 28(4), 955–971.
Trajkovic, S., & Kolakovic, S. (2010). Comparison of simplified pan-based equations for estimating reference evapotranspiration. Journal of Irrigation and Drainage Engineering, 136(2), 137–140.
Vaheddoost, B., Aksoy, H., & Abghari, H. (2016). Prediction of water level using monthly lagged data in lake Urmia, Iran. Water Resources Management, 30(13), 4951–4967.
Verplancke, T., Vanlooy, S., Benoit, D., Vansteelandt, S., Depuydt, P., Deturck, F., et al. (2008). Prediction of hospital mortality by support vector machine versus logistic regression in patients with a haematological malignancy admitted to the ICU. Critical Care, 12(2), 1.
Weng, X. Y., Xu, H. X., Yang, Y., & Peng, H. H. (2008). Water-water cycle involved in dissipation of excess photon energy in phosphorus deficient rice leaves. Biologia Plantarum, 52(2), 307–313.
World Bank Document: Water Security for Central Kosovo NO. 71850. (2011). The Kosovo-Iber River Basin and Iber Lepenc Water System.
Xu, J., Lv, Y., Ai, L., Yang, S., He, Y., & Dalson, T. (2016). Validation of dual-crop coefficient method for calculation of rice evapotranspiration under drying—Wetting cycle condition. Paddy and Water Environment, 1–13.
Xu, M., Ye, B., Zhao, Q., Zhang, S., & Wang, J. (2013). Estimation of water balance in the source region of the Yellow River based on GRACE satellite data. Journal of Arid Land, 5(3), 384–395.
Zhao, L., Xia, J., Xu, C. Y., Wang, Z., Sobkowiak, L., & Long, C. (2013). Evapotranspiration estimation methods in hydrological models. Journal of Geographical Sciences, 23(2), 359–369.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Jović, S., Đokić, J. (2020). Flood Risk Management Modelling in the River Ibar Catchment Area. In: Gocić, M., Aronica, G., Stavroulakis, G., Trajković, S. (eds) Natural Risk Management and Engineering. Springer Tracts in Civil Engineering . Springer, Cham. https://doi.org/10.1007/978-3-030-39391-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-39391-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39390-8
Online ISBN: 978-3-030-39391-5
eBook Packages: EngineeringEngineering (R0)