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Precipitation concentration index management by adaptive neuro-fuzzy methodology

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Abstract

This paper reconsiders the precipitation concentration index (PCI) in Serbia using precipitation measurements such as the mean winter precipitation amount, annual total precipitation, mean summer precipitation amount, mean spring precipitation amount, mean autumn precipitation amount and the mean of precipitation for the vegetation period (April–September). Potentials for further improvement of PCI prediction lie in the improvement of current prediction strategies. One of the options is the introduction of model predictive control. To manage the PCI, it is good to select factors or parameters that are the most important for PCI estimation and prediction, i.e. to conduct variable selection procedure. In the present study, a regression based on the adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential PCI inputs based on the precipitation measurements. The effectiveness of the proposed strategy is verified according to the simulation results. The results show that the mean autumn precipitation amount is the most influential for PCI prediction and estimation and could be used for the simplification of predictive methods to avoid multiple input variables.

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References

  • Aldair AA, Wang WJ (2011) Design an intelligent controller for full vehicle nonlinear active suspension systems. Int J Smart Sens Intell Syst 4(2):224–243

    Google Scholar 

  • Alijani B, O’Brien J, Yarnal B (2008) Spatial analysis of precipitation intensity and concentration in Iran. Theor Appl Climatol 94(1):107–124

    Article  Google Scholar 

  • Anderson FO, Åberg M, Jacobsson SP (2000) Algorithmic approaches for studies of variable influence, contribution and selection in neural networks. Chemom Intell Lab Syst 51(1):61–72

    Article  Google Scholar 

  • Apaydin H, Erpul G, Bayramin I, Gabriels D (2006) Evaluation of indices for characterizing the distribution and concentration of precipitation: a case for the region of Southeastern Anatolia Project, Turkey. J Hydrol 328:726–732

    Article  Google Scholar 

  • Babovic V, Keijzer M (2000) Rainfall runoff modeling based on genetic programming. Nord Hydrol 33:331–346

    Google Scholar 

  • Bacanli UG, Firat M, Dikbas F (2009) Adaptive neuro-fuzzy inference system for drought forecasting. Stoch Env Res Risk A 23:1143–1154

    Article  Google Scholar 

  • Bae D-H, Jeong DM, Kim G (2007) Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrol Sci J 52:99–113

    Article  Google Scholar 

  • Castellano G, Fanelli AM (2000) Variable selection using neural-network models. Neurocomputing 31:1–13

    Article  Google Scholar 

  • Chang F-J, Chang Y-T (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10

    Article  Google Scholar 

  • Chang F-J, Chiang Y-M, Tsai M-C, Hsu K-L, Sorooshian S (2014) Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information. J Hydrol 508:374–384

    Article  Google Scholar 

  • Cibas T, Soulie FF, Gallinari P, Raudys S (1996) Variable selection with neural networks. Neurocomputing 12:223–248

    Article  Google Scholar 

  • Coscarelli R, Caloiero T (2012) Analysis of daily and monthly rainfall concentration in Southern Italy. J Hydrol 416–417:145–156

    Article  Google Scholar 

  • Crone SF, Kourentzes N (2010) Feature selection for time series prediction—a combined filter and wrapper approach for neural networks. Neurocomputing 73(10):1923–1936

    Article  Google Scholar 

  • Dastranj MR, Ebroahimi E, Changizi N, Sameni E (2011) Control DC motorspeed with adaptive neuro-fuzzy control (ANFIS). Aust J Basic Appl Sci 5(10):1499–1504

    Google Scholar 

  • De Luis M, Reventos J, Gonzalez-Hidalgo JC, Sanchez IR, Cortina J (2000) Spatial analysis of rainfall trends in the region of Valencia (East Spain). Int J Climatol 20:1451–1469

    Article  Google Scholar 

  • De Luis M, Gonzalez-Hidalgo CJ, Brunetti M, Longares LA (2011) Precipitation concentration changes in Spain 1946–2005. Nat Hazards Earth Syst Sci 11:1259–1265

    Article  Google Scholar 

  • Dieterle F, Busche S, Gauglitz G (2003) Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements. Anal Chim Acta 490(1–2):71–83

    Article  Google Scholar 

  • Eldessouki M, Hassan M (2015) Adaptive neuro-fuzzy system for quantitative evaluation of woven fabrics’ pilling resistance. Expert Syst Appl 42(4):2098–2113

    Article  Google Scholar 

  • El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan High Dam. Water Resour Manag 21:533–556

    Article  Google Scholar 

  • Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—part 1: concepts and methodology. Hydrol Earth Syst Sci 14:1931–1941

    Article  Google Scholar 

  • Firat M, Güngör M (2010) Monthly total sediment forecasting using adaptive neuro fuzzy inference system. Stoch Env Res Risk A 24(2):259–270

    Article  Google Scholar 

  • Gocic M, Trajkovic S (2014a) Spatiotemporal characteristics of drought in Serbia. J Hydrol 510:110–123

    Article  Google Scholar 

  • Gocic M, Trajkovic S (2014b) Spatio-temporal patterns of precipitation in Serbia. Theor Appl Climatol 117(3–4):419–431

    Article  Google Scholar 

  • He Y, Tian P, Mu X, Gao P, Zhao G, Wang F, Li P (2016) Changes in daily and monthly rainfall in the Middle Yellow River, China. Theor Appl Climatol. doi:10.1007/s00704-016-1763-4

    Google Scholar 

  • Huang J, Sun SL, Zhang JC (2013) Detection of trends in precipitation during 1960–2008 in Jiangxi province, southeast China. Theor Appl Climatol 14:237–251

    Article  Google Scholar 

  • Huang J, Liu F, Xue Y, Sun S (2015) The spatial and temporal analysis of precipitation concentration and dry spell in Qinghai, northwest China. Stoch Env Res Risk A 29(5):1403–1411

    Article  Google Scholar 

  • Islam T, Srivastava PK, Rico-Ramirez MA, Dai Q, Han D, Gupta M (2014) An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR. Atmos Res 145–146:57–68

    Article  Google Scholar 

  • Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans On Syst Man Cybern 23:665–685

    Article  Google Scholar 

  • Jang J-SR, Sun C-T, Mizutani E (1997) Neurofuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • John GH, Kohavi R, Pfleger K (1994) Irrelevant features and the subset selection problem. In: Machine Learning: Proceedings of the Eleventh International Conference, pp 121–129

  • Karimi-Googhari SH, Lee TS (2011) Applicability of adaptive neuro-fuzzy inference systems in daily reservoir inflow forecasting. Int J Soft Comput 6:75–84

    Article  Google Scholar 

  • Keskin ME, Taylan D, Terzi O (2006) Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrol Sci J 51:588–598

    Article  Google Scholar 

  • Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S (2009) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 40(6):438–444

    Article  Google Scholar 

  • Kodogiannis VS, Alshejari A (2014) An adaptive neuro-fuzzy identification model for the detection of meat spoilage. Appl Soft Comput 23:483–497

    Article  Google Scholar 

  • Koza JR (1992) Genetic programming: on the programming of computers by natural selection. MIT Press, Cambridge

    Google Scholar 

  • La Rocca M, Perna C (2005) Variable selection in neural network regression models with dependent data: a subsampling approach. Comput Stat Data Anal 48(2):415–429

    Article  Google Scholar 

  • Li X, Jiang F, Li L, Wang G (2011) Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China. Int J Climatol 31(11):1679–1693

    Google Scholar 

  • Lohani AK, Kumar R, Singh RD (2012) Hydrological time series modelling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442–443:23–35

    Article  Google Scholar 

  • Longobardi A, Buttafuoco G, Caloiero T, Coscarelli R (2016) Spatial and temporal distribution of precipitation in a Mediterranean area (southern Italy). Environ Earth Sci 75:189. doi:10.1007/s12665-015-5045-8

    Article  Google Scholar 

  • Martins DS, Raziei T, Paulo AA, Pereira LS (2012) Spatial and temporal variability of precipitation and drought in Portugal. Nat Hazards Earth Syst Sci 12:1493–1501

    Article  Google Scholar 

  • Martin-Vide J (2004) Spatial distribution of a daily precipitation concentration index in peninsular Spain. Int J Climatol 24(8):959–971

    Article  Google Scholar 

  • Michiels P, Gabriels D, Hartmann Ghent R (1992) Using the seasonal and temporal precipitation concentration index for characterizing the monthly rainfall distribution in Spain. Catena 19:43–58

    Article  Google Scholar 

  • Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66

    Article  Google Scholar 

  • Oliver JE (1980) Monthly precipitation distribution: a comparative index. Prof Geogr 32(3):300–309

    Article  Google Scholar 

  • Petković D, Ćojbašić Ž (2012) Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability. Neural Comput & Applic 21(8):2065–2070

    Article  Google Scholar 

  • Petković D, Ćojbašić Ž, Lukić S (2013) Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system. Expert Syst Appl 40(11):4490–4495

    Article  Google Scholar 

  • Raziei T, Bordi I, Pereira LS (2008) A precipitation-based regionalization for Western Iran and regional drought variability. Hydrol Earth Syst Sci 12:1309–1321

    Article  Google Scholar 

  • Scholz G, Quinton JN, Strauss P (2008) Soil erosion from sugar beet in Central Europe in response to climate change induced seasonal precipitation variations. Catena 72:91–105

    Article  Google Scholar 

  • Shi W, Yu X, Liao W, Wang Y, Jia B (2013) Spatial and temporal variability of daily precipitation concentration in the Lancang River basin, China. J Hydrol 495:197–207

    Article  Google Scholar 

  • Shi P, Qiao X, Chen X, Zhou M, Qu S, Ma X, Zhang Z (2014) Spatial distribution and temporal trends in daily and monthly precipitation concentration indices in the upper reaches of the Huai River, China. Stoch Env Res Risk A 28(2):201–212

    Article  Google Scholar 

  • Shojaei MJ, Bahrami E, Barati P, Riahi S (2014) Adaptive neuro-fuzzy approach for reservoir oil bubble point pressure estimation. J Nat Gas Sci Eng 20:214–220

    Article  Google Scholar 

  • Subbaraj P, Kannapiran B (2014) Fault detection and diagnosis of pneumatic valve using adaptive neuro-fuzzy inference system approach. Appl Soft Comput 19:362–371

    Article  Google Scholar 

  • Talei A, Chua LHC, Wong T (2010) Evaluation of rainfall and discharge inputs used by adaptive network-based fuzzy inference systems (ANFIS) in rainfall–runoff modeling. J Hydrol 391:248–262

    Article  Google Scholar 

  • Task Committee ASCE (2000a) Artificial neural networks in hydrology—1: preliminary concepts. J Hydrol Eng 5(2):115–123

    Article  Google Scholar 

  • Task Committee ASCE (2000b) Artificial neural networks in hydrology—2: hydrologic applications. J Hydrol Eng 5(2):124–137

    Article  Google Scholar 

  • Tsai M-J, Abrahart RJ, Mount NJ, Chang F-J (2014) Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan. Hydrol Process 28(3):1055–1070

    Article  Google Scholar 

  • Wahida Banu RSD, Shakila Banu A, Manoj D (2011) Identification and control of nonlinear systems using soft computing techniques. Int J Mod Optimization 1(1):24–28

    Google Scholar 

  • Wang X, Han M (2015) Improved extreme learning machine for multivariate time series online sequential prediction. Eng Appl Artif Intell 40:28–36

    Article  Google Scholar 

  • Yang X-S (2009) Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications. Lect Notes Comput Sci 5792:169–178

    Article  Google Scholar 

  • Yang L, Entchev E (2014) Performance prediction of a hybrid microgeneration system using adaptive neuro-fuzzy inference system (ANFIS) technique. Appl Energy 134:197–203

    Article  Google Scholar 

  • Yeşilırmak E, Atatanır L (2016) Spatiotemporal variability of precipitation concentration in western Turkey. Nat Hazards 81(1):687–704

    Article  Google Scholar 

  • Zhang R, Wang W (2011) Facilitating the applications of support vector machine by using a new kernel. J Nat Gas Sci Eng 38(11):14225–14230

    Google Scholar 

  • Zhang Q, Xu CY, Gemmer M, Chen YD, Liu C (2009) Changing properties of precipitation concentration in the Pearl River basin, China. Stoch Env Res Risk A 23(3):377–385

    Article  Google Scholar 

  • Zhang Q, Sun P, Singh VP, Chen X (2012) Spatial–temporal precipitation changes (1956–2000) and their implications for agriculture in China. Glob Planet Chang 82–83:86–95

    Article  Google Scholar 

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Acknowledgements

The authors express their sincere thanks for the funding support to the Ministry of Education, Science and Technological Development, Republic of Serbia (grant no. TR37003) and the ICT COST Action IC1408 Computationally intensive methods for the robust analysis of non-standard data (CRoNoS).

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Authors and Affiliations

  1. Pedagogical Faculty in Vranje, University of Niš, Partizanska 14, 17500, Vranje, Serbia

    Dalibor Petković

  2. Faculty of Civil Engineering and Architecture, University of Nis, Aleksandra Medvedeva 14, 18000, Nis, Serbia

    Milan Gocic & Slavisa Trajkovic

  3. Faculty of Mechanical Engineering, University of Nis, Aleksandra Medvedeva 14, 18000, Nis, Serbia

    Miloš Milovančević

  4. Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

    Dragoljub Šević

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  1. Dalibor Petković
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  2. Milan Gocic
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  3. Slavisa Trajkovic
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  4. Miloš Milovančević
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  5. Dragoljub Šević
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Correspondence to Dalibor Petković.

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Petković, D., Gocic, M., Trajkovic, S. et al. Precipitation concentration index management by adaptive neuro-fuzzy methodology. Climatic Change 141, 655–669 (2017). https://doi.org/10.1007/s10584-017-1907-2

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