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Flood Risk Management Modelling in the River Ibar Catchment Area

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Natural Risk Management and Engineering

Part of the book series: Springer Tracts in Civil Engineering ((SPRTRCIENG))

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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.

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References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ilic, M., Jovic, S., Spalevic, P., & Vujicic, I. (2017). Water cycle estimation by neuro-fuzzy approach. Computers and Electronics in Agriculture, 135, 1–3.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Jang, J. S. R., Sun, C. T., Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence.

    Google Scholar 

  • Jovic, S., Nedeljkovic, B., Golubovic, Z., & Kostic, N. (2018a). Evolutionary algorithm for reference evapotranspiration analysis. Computers and Electronics in Agriculture, 150, 1–4.

    Article  Google Scholar 

  • Jovic, S., Vasic, P., & Jaksic, T. (2018b). Sensorless estimation of lake level by soft computing approach. Sensor Review, 38(1), 117–119.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Meng, J., & Xia, L. (2007). Support vector regression model for millimeter wave transitions. International Journal of Infrared and Millimeter Waves, 28(5), 413–421.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Morari, F., & Giardini, L. (2001). Estimating evapotranspiration in the Padova botanical garden. Irrigation Science, 20(3), 127–137.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • World Bank Document: Water Security for Central Kosovo NO. 71850. (2011). The Kosovo-Iber River Basin and Iber Lepenc Water System.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Correspondence to Jelena Đokić .

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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

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  • DOI: https://doi.org/10.1007/978-3-030-39391-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39390-8

  • Online ISBN: 978-3-030-39391-5

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