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Prediction of Rainfall as One of the Main Variables in Several Natural Disasters

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Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques

Part of the book series: Advances in Natural and Technological Hazards Research ((NTHR,volume 48))

Abstract

Rainfall is one of the main variables in several natural disasters such as, floods, drought, groundwater depletion and landslides. Therefore, development of robust models for rainfall forecasting is essential in environmental studies. The chief goal of this research is to use Group Method of Data Handling (GMDH) besides signal processing approaches to forecast rainfall in monthly time steps. To that end, three different signal processing approaches i.e. Ensemble empirical mode decomposition (EEMD), wavelet transform (WT) and wavelet packet transform (WPT) were used combined with GMDH model. Four stations were used to apply aforementioned modeling techniques. Results of this research showed that all three abovementioned signal processing approaches can enhance the ability of the GMDH model. The ability of EEMD-GMDH and wavelet packet-GMDH were relatively close to each other. However, wavelet packet-GMDH outperformed EEMD-GMDH model to some extent. The other important note was the effect of exogenous data on the ability of all models. It was shown that forecasting rainfall without using exogenous data does not produce acceptable results.

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References

  • Adamowski J (2007) Development of a short-term river flood forecasting method based on wavelet analysis. Polish Academy of Sciences Publication, Warsaw, p 172

    Google Scholar 

  • Adamowski J, Chan FH (2011) A wavelet neural network conjunction model for rainfall forecasting. J Hydrol 407:28–40

    Article  Google Scholar 

  • Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390(1–2):85–91

    Article  Google Scholar 

  • Aghajani A, Kazemzadeh R, Ebrahimi A (2016) A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm. Energ Convers Manageme 121:232–240

    Article  Google Scholar 

  • Alves MdC, Pozzab EA, Costac JCB, Carvalhod LG, Alvese LS (2011) Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust. Environ Modell Softw 26(9):1089–1096

    Article  Google Scholar 

  • Alvisi S, Franchini M (2011) Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environ Modell Softw 26(4):523–537. http://www.sciencedirect.com/science/journal/13648152/26/4

    Article  Google Scholar 

  • Amanifard N, Nariman-Zadeh N, Farahani MH, Khalkhali A (2008) Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks. J Energy Convers Manage 49(10):2588–2594

    Article  Google Scholar 

  • Breaker LC, Ruzmaikin A (2011) The 154-year record of sea level at San Francisco: extracting the long-term trend, recent changes, and other tidbits. Clim Dyn 36(3–4):545–559

    Article  Google Scholar 

  • Cannas B, Fanni A, Sias G, Tronei S, Zedda MK (2006) River flow forecasting using neural networks and wavelet analysis. In: Proceedings of the European Geosciences Union. 234–243

    Google Scholar 

  • Christopoulou EB, Skodras AN, Georgakilas AA (2002) The ‘‘Trous’’ wavelet transform versus classical methods for the improvement of solar images. In: Proceedings of 14th international conference on digital signal processing, vol. 2, pp. 885–888

    Google Scholar 

  • Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Rainfall forecasting using artificial neural networks. J of Hydrol 309:229–240

    Article  Google Scholar 

  • Daubechies L (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inform Theory 36:961–1005

    Article  Google Scholar 

  • Fallah-Mehdipour E, Bozorg Haddad O, Mariño MA (2013) Prediction and simulation of monthly rainfalls by genetic programming. J Hydro Environ Res 7(4):253–260

    Article  Google Scholar 

  • Farlow SJ (1984) Self -organizing method in modeling. Marcel Dekker Inc, GMDH Type Algorithm

    Google Scholar 

  • Feng Z, Liang M, Zhang Y, Hou S (2012) Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation. Renew Energy 47:112–126

    Article  Google Scholar 

  • Garcia C, Zikos G, Tziritas G (2000) Wavelet packet analysis for face recognition. Image Vision Comput 18:289–297

    Article  Google Scholar 

  • Guo W, Tse PW (2013) A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals. J Sound Vib 332:423–441

    Article  Google Scholar 

  • Han J, Zheng P, Wang H (2014) Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform. Earthquake Eng Vib 13:101–111

    Article  Google Scholar 

  • He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386

    Article  Google Scholar 

  • Ivakhnenko AG (1966) Group method of data handling—a rival of the method of stochastic approximation. Sov Autom Cont 13:43–71

    Google Scholar 

  • Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern Syst 1(4):364–378

    Article  Google Scholar 

  • Kalantary F, Ardalan H, Nariman-Zadeh N (2009) An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol 104:144–155

    Article  Google Scholar 

  • Kim Y, Shin HS, Plummer JD (2014) A wavelet-based autoregressive fuzzy model for forecasting algal blooms. Environ Modell Softw 62:1–10

    Article  Google Scholar 

  • Kisi O (2009) Neural networks and wavelet conjunction model for intermittent stream flow forecasting. J Hydrol Eng 14:773–782

    Article  Google Scholar 

  • Li X, Maier HR, Zecchin AC (2015) Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models. Environ Modell Softw 65:15–29

    Article  Google Scholar 

  • Liu H, Chen C, Hq Tian, Li Y (2012) A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew Energ 48:545–556

    Article  Google Scholar 

  • Mackay JD, Jackson CR, Wang L (2014) A lumped conceptual model to simulate rainfall time-series. Environ Modell Softw 61:229–245

    Article  Google Scholar 

  • Manjula M, Sarma AVRS (2012) Comparison of Empirical Mode Decomposition and Wavelet Based Classification of Power Quality Events. Energy Procedia 14:1156–1162

    Article  Google Scholar 

  • Mariyappa N, Sengottuvel S, Parasakthi C (2014) Baseline drift removal and denoising of MCG data using EEMD: role of noise amplitude and the thresholding effect. Med Eng Phys 36(10):1266–1276

    Article  Google Scholar 

  • Mehra RK (1977) Group method of data handling (GMDH): Review and experience. IEEE conf dec cont 16:29–34

    Google Scholar 

  • Mehrara M, Moeini A, Ahrari M, Erfanifard A (2009) Investigating the efficiency in oil futures market based on GMDH approach. Expert Syst Appl 36(4):7479–7483

    Article  Google Scholar 

  • Millie DF, Weckman GR, Young WA, Ivey JE, Carrick HJ, Fahnenstiel GL (2012) Modeling microalgal abundance with artificial neural networks: Demonstration of a heuristic ‘Grey-Box’ to deconvolve and quantify environmental influences. Environ Modell Softw 27–39

    Article  Google Scholar 

  • Moosavi V, Niazi Y (2015) Development of Hybrid Wavelet Packet-Statistical Models (WP-SM) for landslide susceptibility mapping. Landslides 13(1):97–114. https://doi.org/10.1007/s10346-014-0547-0

    Article  Google Scholar 

  • Moosavi V, Talebi A, Mokhtari MH, Fallah Shamsi SR, Niazi Y (2015) A Wavelet-Artificial Intelligence Fusion Approach (WAIFA) for Blending Landsat and MODIS surface temperature. Remote Sens Environ 169:243–254

    Article  Google Scholar 

  • Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A Wavelet-ANFIS hybrid model for rainfall forecasting for different prediction periods. Water Resour Manag 27:1301–1321

    Article  Google Scholar 

  • Moosavi V, Vafakhah M, Shirmohammadi B, Ranjbar B (2014) Optimization of Wavelet-ANFIS and Wavelet-ANN Hybrid Models by Taguchi Method for Rainfall forecasting. Arab J Sci Eng 39:1785–1796

    Article  Google Scholar 

  • Najafzadeh M (2015) Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions. Ocean Eng 99:85–94

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Hessami Kermani MR (2013) GMDH based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106

    Article  Google Scholar 

  • Najafzadeh M, Lim SY (2015) Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci Inform 8(1):187–196

    Article  Google Scholar 

  • Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22:466–472

    Article  Google Scholar 

  • Nourani V, Kisi Z, Mehdi K (2011) Two hybrid artificial Intelligence approaches for modeling rainfall-runoff process. J Hydrol 402:41–59

    Article  Google Scholar 

  • Pal S, Devara PCS (2012) A wavelet-based spectral analysis of long-term time series of optical properties of aerosols obtained by lidar and radiometer measurements over an urban station in Western India. J Atmos Sol-Rerr Phy Solar-Terrestrial Phys 84–85:75–87

    Article  Google Scholar 

  • Quiroz R, Yarlequé Ch, Posadas A, Mares V, Immerzeel WW (2011) Improving daily rainfall estimation from NDVI using a wavelet transform. Environ Modell Softw 26(2):201–209

    Article  Google Scholar 

  • Ravisankar P, Ravi V (2010) Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP. Knowl-Based Sys 23:823–831

    Article  Google Scholar 

  • Sànchez-Marrè M, Cortés U, Comas J (2004) Environmental sciences and artificial intelligence. Environ Modell Softw 19(9):761–762

    Article  Google Scholar 

  • Si J, Feng Q, Wen X, Xi H, Yu T, Li W, Zhao C (2015) Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system. J of Hydrol 527:679–687

    Article  Google Scholar 

  • Sreekanth P, Geethanjali DN, Sreedevi PD, Ahmed S, Kumar NR, Jayanthi PDK (2009) Forecasting rainfall using artificial neural networks. Current Sci 96(7):933–939

    Google Scholar 

  • Talei A, Chua LHC, Wong TSW (2010) Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. J of Hydrol 391(3–4):248–262

    Article  Google Scholar 

  • Volterra V (1959) Theory of functionals and of integrals and integro-differential equations. Madrid (Spanish), translated version reprinted New York: Dover Publications

    Google Scholar 

  • Wieland R, Mirschel W, Zbell B, Krin Groth K, Pechenick A, Fukuda K (2010) A new library to combine artificial neural networks and support vector machines with statistics and a database engine for application in environmental modeling. Environ Modell Softw 25(4):412–420

    Article  Google Scholar 

  • Witczak M, Korbicz J, Mrugalski M, Patton R (2006) A GMDH neural networkbased approach to robust fault diagnosis: application to the DAMADICS benchmark problem. Control Eng Pract 14(6):671–683

    Article  Google Scholar 

  • Wu Zh, Huang Zh (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1:1–41

    Article  Google Scholar 

  • Yoon H, Hyun Y, Ha K, Lee KK, Kim GB (2016) A method to improve the stability and accuracy of ANN- and SVM based time series models for long-term rainfall predictions. Comput Geosc 90:144–155

    Article  Google Scholar 

  • Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting rainfalls in a coastal aquifer. J of Hydrol 396:128–138

    Article  Google Scholar 

  • Young WA, Millie DF, Weckman GR, Anderson JS, Klarer DM, Fahnenstiel GL (2011) Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network. Environ Modell Softw 26(10):1199–1210

    Article  Google Scholar 

  • Yu Y, Li W, Sheng D, Chen J (2015) A novel sensor fault diagnosis method based on modified ensemble empirical mode decomposition and probabilistic neural network. Measurement 68:328–336

    Article  Google Scholar 

  • Zhan X (2005) Parallel Fortran-MPI software for numerical inversion of the Laplace transform and its application to oscillatory water levels in groundwater environments. Environ Modell Softw 20(3):279–284

    Article  Google Scholar 

  • Zhao JP, Huang DJ (2001) Mirror extending and circular spline function for empirical mode decomposition method. J Zhejiang Univ Sci 2(3):247–252

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Watershed Management Engineering, Faculty of Natural Resources, Yazd University, Yazd, Iran

    Vahid Moosavi

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  1. Vahid Moosavi
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Correspondence to Vahid Moosavi .

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Editors and Affiliations

  1. Natural Resources and Environmental Engineering, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  2. IRPI, National Research Council, Perugia, Italy

    Mauro Rossi

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Moosavi, V. (2019). Prediction of Rainfall as One of the Main Variables in Several Natural Disasters. In: Pourghasemi, H., Rossi, M. (eds) Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Advances in Natural and Technological Hazards Research, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-73383-8_8

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