Skip to main content
SpringerLink
Log in

Transfer precipitation learning via patterns of dependency matrix-based machine learning approaches

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Accurate precipitation prediction is very significant for urban, environmental, and water resources management as well as mitigating the negative effects of drought and flood. However, precipitation prediction is a complex and challenging task which involves meteorological parameters that contain uncertainty. This study attempts to ease the complexity of the problem via proposing a correlation matrix approach. Covariance and correlation matrices are analytical tools that are widely used to identify the interrelationships and possible dependencies throughout the data. Correlation matrices have some advantages over covariance matrices. The main drawback of covariance matrices is their sensitivity to the measurement units of variables. The variables with relatively large variances will dominate the results of multivariate analysis when the covariance matrix is used. Accordingly, the covariance matrix fails to provide useful information when there exist large differences between variances of variables. On the other hand, besides their easy interpretable features, the results of different analyses obtained from correlation matrices can effectively be compared. Therefore, in this study, in order to improve the performances of the predictive models, interrelationships and possible dependencies among data obtained from eighteen precipitation observation stations located in the Upper Euphrates Basin of Turkey (1980–2010) is investigated using correlation matrix approach. Relatedly, dependencies between the stations are resolved by means of examining the correlation matrix and optimal model inputs (data of particular stations) are selected for each prediction scenario. The transfer precipitation learning was performed throughout the period from 1980 to 2010 for eighteen precipitation observation stations located in the Upper Euphrates. Three different data-driven models Fuzzy, K-nearest neighbors (KNN), and multilinear regression (MR) are developed based on the patterns of correlation matrix. Predictive powers of the models are compared by means of performance evaluation criteria, i.e., Nash–Sutcliffe efficiency, mean square error, mean absolute error, and coefficient of determination (R2). Results of this study show that all developed correlation matrix patterns-based Fuzzy, KNN, and MR models have high precipitation prediction performance. However, even though all model results are close to each other, Fuzzy model provided more accurate results with requiring data from a relatively low number of stations. Therefore, patterns of correlation matrix-based Fuzzy model is the most efficient and well-suited approach for precipitation prediction among all the developed models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Patel J, Parekh DF (2014) Forecasting rainfall using adaptive neuro-fuzzy inference system (ANFIS). Int J Appl or İnnov Eng Manag 3:510

    Google Scholar 

  2. Maeda N, Kobayashi S, Izum K et al (2001) Prediction of precipitation by a neural network method. J Nat Disast Sci 23:23–33

    Google Scholar 

  3. Khan WA (2022) Numerical simulation of Chun-Hui He’s iteration method with applications in engineering. Int J Numer Methods Heat Fluid Flow. https://doi.org/10.1108/HFF-04-2021-0245

    Article  Google Scholar 

  4. He CH (2016) An introduction to an ancient Chinese algorithm and its modification. Int J Numer Methods Heat Fluid Flow. https://doi.org/10.1108/HFF-09-2015-0377

    Article  Google Scholar 

  5. Claußnitzer A, Névir P (2009) Analysis of quantitative precipitation forecasts using the Dynamic State Index. Atmos Res. https://doi.org/10.1016/j.atmosres.2009.08.013

    Article  Google Scholar 

  6. Altunkaynak A, Kartal E (2019) Performance comparison of continuous wavelet-fuzzy and discrete wavelet-fuzzy models for water level predictions at northern and southern boundary of Bosphorus. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2019.06.002

    Article  Google Scholar 

  7. Adnan RM, Mostafa RR, Elbeltagi A et al (2022) Development of new machine learning model for streamflow prediction: case studies in Pakistan. Springer, Berlin

    Google Scholar 

  8. Mirabbasi R, Kisi O, Sanikhani H, Gajbhiye Meshram S (2019) Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3519-9

    Article  Google Scholar 

  9. Bagirov AM, Mahmood A, Barton A (2017) Prediction of monthly rainfall in Victoria, Australia: clusterwise linear regression approach. Atmos Res 188:20–29. https://doi.org/10.1016/j.atmosres.2017.01.003

    Article  Google Scholar 

  10. Khazaee Poul A, Shourian M, Ebrahimi H (2019) A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resour Manag 33:2907–2923. https://doi.org/10.1007/s11269-019-02273-0

    Article  Google Scholar 

  11. Kyada PM, Kumar P, Sojitra MA (2018) Rainfall forecasting using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Int J Agric Sci 10:6153–6159

    Google Scholar 

  12. Suparta W, Samah AA (2020) Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia. Geod Geodyn. https://doi.org/10.1016/j.geog.2020.08.001

    Article  Google Scholar 

  13. Tsakiri K, Marsellos A, Kapetanakis S (2018) Artificial neural network and multiple linear regression for flood prediction in Mohawk River. N Y Water (Switz). https://doi.org/10.3390/w10091158

    Article  Google Scholar 

  14. Tyralis H, Papacharalampous G, Langousis A (2021) Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms. Neural Comput Appl 33:3053–3068. https://doi.org/10.1007/s00521-020-05172-3

    Article  Google Scholar 

  15. Altunkaynak A, Jalilzadnezamabad A (2021) Extended lead time accurate forecasting of palmer drought severity index using hybrid wavelet-fuzzy and machine learning techniques. J Hydrol. https://doi.org/10.1016/j.jhydrol.2021.126619

    Article  Google Scholar 

  16. Rezaeianzadeh M, Tabari H, Arabi Yazdi A et al (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl. https://doi.org/10.1007/s00521-013-1443-6

    Article  Google Scholar 

  17. Hashim R, Roy C, Motamedi S et al (2016) Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology. Atmos Res. https://doi.org/10.1016/j.atmosres.2015.12.002

    Article  Google Scholar 

  18. Ahmadi A, Han D, Lafdani EK, Moridi A (2015) Input selection for long-lead precipitation prediction using large-scale climate variables: a case study. J Hydroinformatics. https://doi.org/10.2166/hydro.2014.138

    Article  Google Scholar 

  19. Kyada P, Kumar P (2015) Daily rainfall forecasting using adaptive neuro-fuzzy inference system (ANFIS) models. Int J Sci Nat 6:382–388

    Google Scholar 

  20. Bushara NO, Abraham A (2015) Using adaptive neuro-fuzzy inference system (ANFIS) to improve the long-term rainfall forecasting. J Netw Innov Comput 3:146–158

    Google Scholar 

  21. Sharma M, Mathew L, Chatterji S (2014) Weather forecasting using soft computing and statistical techniques. Int J Adv Res Electr Electron Instrum Eng 3(7):11285–11290

    Google Scholar 

  22. Niksaz P, Mohammad LA (2014) Rainfall events evaluation using adaptive neural-fuzzy inference system. Int J Inf Technol Comput Sci. https://doi.org/10.5815/ijitcs.2014.09.06

    Article  Google Scholar 

  23. Mekanik F, Imteaz MA, Talei A (2016) Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals. Clim Dyn. 46:3097–3111

    Article  Google Scholar 

  24. Asklany SA, Elhelow K, Youssef IK, Abd El-wahab M (2011) Rainfall events prediction using rule-based fuzzy inference system. Atmos Res. https://doi.org/10.1016/j.atmosres.2011.02.015

    Article  Google Scholar 

  25. Dubey AD (2015) Comparative analysis of ANFIS and SVR model performance for rainfall prediction. In: Part of the Advances in Intelligent Systems and Computing book series (AISC, vol 415)

  26. Liu K, Li Z, Yao C et al (2016) Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting. Int J Sediment Res. https://doi.org/10.1016/j.ijsrc.2016.02.002

    Article  Google Scholar 

  27. Altunkaynak A, Nigussie TA (2015) Prediction of daily rainfall by a hybrid wavelet-season-neuro technique. J Hydrol. https://doi.org/10.1016/j.jhydrol.2015.07.046

    Article  Google Scholar 

  28. Altunkaynak A (2014) Predicting Water level fluctuations in Lake Michigan-Huron using wavelet-expert system methods. Water Resour Manag. https://doi.org/10.1007/s11269-014-0616-0

    Article  Google Scholar 

  29. Remesan R, Shamim MA, Han D, Mathew J (2009) Runoff prediction using an integrated hybrid modelling scheme. J Hydrol. https://doi.org/10.1016/j.jhydrol.2009.03.034

    Article  Google Scholar 

  30. Jothiprakash V, Magar RB, Kalkutki S (2009) Rainfall-runoff models using adaptive neuro-fuzzy inference system (ANFIS) for an intermittent river. Int J Artif Intell 3(9 A):1–23

    Google Scholar 

  31. Zounemat-Kermani M, Teshnehlab M (2008) Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2007.07.011

    Article  Google Scholar 

  32. Lohani AK, Kumar R, Singh RD (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol. https://doi.org/10.1016/j.jhydrol.2012.03.031

    Article  Google Scholar 

  33. Mandal T, Jothiprakash V (2012) Short-term rainfall prediction using ANN and MT techniques. ISH J Hydraul Eng. https://doi.org/10.1080/09715010.2012.661629

    Article  Google Scholar 

  34. Seo Y, Kim S, Kisi O, Singh VP (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol. https://doi.org/10.1016/j.jhydrol.2014.11.050

    Article  Google Scholar 

  35. Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2005.04.015

    Article  Google Scholar 

  36. Deka P, Chandramouli V (2005) Fuzzy neural network model for hydrologic flow routing. J Hydrol Eng. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:4(302)

    Article  Google Scholar 

  37. Özger M (2009) Comparison of fuzzy inference systems for streamflow prediction. Hydrol Sci J. https://doi.org/10.1623/hysj.54.2.261

    Article  Google Scholar 

  38. Yaseen ZM, Ebtehaj I, Bonakdari H et al (2017) Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. J Hydrol. https://doi.org/10.1016/j.jhydrol.2017.09.007

    Article  Google Scholar 

  39. Wang K-H, Altunkaynak A (2012) Comparative case study of rainfall-runoff modeling between SWMM and fuzzy logic approach. J Hydrol Eng. https://doi.org/10.1061/(asce)he.1943-5584.0000419

    Article  Google Scholar 

  40. Shiri J, Kişi Ö, Makarynskyy O et al (2012) Forecasting daily stream flows using artificial intelligence approaches. ISH J Hydraul Eng. https://doi.org/10.1080/09715010.2012.721189

    Article  Google Scholar 

  41. Altunkaynak A (2010) A predictive model for well loss using fuzzy logic approach. Hydrol Process. https://doi.org/10.1002/hyp.7642

    Article  Google Scholar 

  42. Jalalkamali A, Sedghi H, Manshouri M (2011) Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran. J Hydroinform. https://doi.org/10.2166/hydro.2010.034

    Article  Google Scholar 

  43. El-Shafie A, Jaafer O, Akrami SA (2011) Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. Int J Phys Sci. https://doi.org/10.5897/IJPS11.515

    Article  Google Scholar 

  44. Alfarisy GAF, Mahmudy WF (2017) Rainfall forecasting in Banyuwangi using adaptive neuro fuzzy inference system. J Inf Technol Comput Sci. https://doi.org/10.25126/jitecs.20161212

    Article  Google Scholar 

  45. Jeong C, Shin JY, Kim T, Heo JH (2012) Monthly precipitation forecasting with a Neuro-Fuzzy Model. Water Resour Manag. https://doi.org/10.1007/s11269-012-0157-3

    Article  Google Scholar 

  46. Huang CL, Hsu NS, Wei CC, Lo CW (2015) Using artificial intelligence to retrieve the optimal parameters and structures of adaptive network-based fuzzy inference system for typhoon precipitation forecast modeling. Adv Meteorol. https://doi.org/10.1155/2015/472523

    Article  Google Scholar 

  47. Suhartono, Faulina R, Lusia DA, et al (2012) Ensemble method based on ANFIS-ARIMA for rainfall prediction. In: ICSSBE 2012-Proceedings, 2012 international conference on statistics in science, business and engineering: “Empowering decision making with statistical sciences”

  48. Jirakittayakorn A, Kormongkolkul T, Vateekul P et al (2017) Temporal kNN for short-Term ocean current prediction based on HF radar observations. In: Proceedings of the 2017 14th international joint conference on computer science and software engineering, JCSSE 2017

  49. Nikoo MR, Kerachian R, Alizadeh MR (2018) A fuzzy KNN-based model for significant wave height prediction in large lakes. Oceanologia. https://doi.org/10.1016/j.oceano.2017.09.003

    Article  Google Scholar 

  50. Prairie JR, Rajagopalan B, Fulp TJ, Zagona EA (2006) Modified K-NN model for stochastic streamflow simulation. J Hydrol Eng. https://doi.org/10.1061/(asce)1084-0699(2006)11:4(371)

    Article  Google Scholar 

  51. Sharifazari S, Araghinejad S (2015) Development of a nonparametric model for multivariate hydrological monthly series simulation considering climate change impacts. Water Resour Manag. https://doi.org/10.1007/s11269-015-1119-3

    Article  Google Scholar 

  52. Ahani A, Shourian M, Rad PR (2018) Performance assessment of the linear, nonlinear and nonparametric data driven models in river flow forecasting. Water Resour Manag. https://doi.org/10.1007/s11269-017-1792-5

    Article  Google Scholar 

  53. Muluye GY (2012) Comparison of statistical methods for downscaling daily precipitation. J Hydroinformatics. https://doi.org/10.2166/hydro.2012.197

    Article  Google Scholar 

  54. Sumi SM, Zaman MF, Hirose H (2012) A rainfall forecasting method using machine learning models and its application to the Fukuoka city case. Int J Appl Math Comput Sci. https://doi.org/10.2478/v10006-012-0062-1

    Article  MathSciNet  MATH  Google Scholar 

  55. Mehrotra R, Sharma A (2006) Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2005.08.007

    Article  Google Scholar 

  56. Kusiak A, Wei X, Verma AP, Roz E (2013) Modeling and prediction of rainfall using radar reflectivity data: a data-mining approach. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2012.2210429

    Article  Google Scholar 

  57. Gupta D, Ghose U (2015) A comparative study of classification algorithms for forecasting rainfall. In: 2015 4th international conference on reliability, Infocom technologies and optimization: trends and future directions, ICRITO 2015

  58. Dar LA (2017) Rainfall-runoff modeling using multiple regression technique. Int J Res Appl Sci Eng Technol 5(VII:214–218

    Google Scholar 

  59. Yockey RD (2018) Multiple Linear Regression. In: SPSS® Demystified, Imprint: Routledge, 14 p. eBook ISBN: 9781315268545

  60. Sveinsson OGB, Lall U, Fortin V et al (2008) Forecasting spring reservoir inflows in Churchill Falls basin in Québec Canada. J Hydrol Eng. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:6(426)

    Article  Google Scholar 

  61. Li W, Huicheng Z (2010) Urban water demand forecasting based on HP filter and fuzzy neural network. J Hydroinformatics. https://doi.org/10.2166/hydro.2009.082

    Article  Google Scholar 

  62. Modaresi F, Araghinejad S, Ebrahimi K (2018) Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting. J Hydroinformatics. https://doi.org/10.2166/hydro.2018.098

    Article  Google Scholar 

  63. Chifurira R, Chikobvu D (2014) A weighted multiple regression model to predict rainfall patterns: principal component analysis approach. Mediterr J Soc Sci. https://doi.org/10.5901/mjss.2014.v5n7p34

    Article  Google Scholar 

  64. Um MJ, Yun H, Jeong CS, Heo JH (2011) Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea. J Hydrol. https://doi.org/10.1016/j.jhydrol.2011.09.016

    Article  Google Scholar 

  65. Ranhao S, Baiping Z, Jing T (2008) A multivariate regression model for predicting precipitation in the Daqing Mountains. Mt Res Dev. https://doi.org/10.1659/mrd.0944

    Article  Google Scholar 

  66. Navid M (2019) Multiple linear regressions for predicting rainfall for Bangladesh. Communications. https://doi.org/10.11648/j.com.20180601.11

    Article  Google Scholar 

  67. Naoum S, Tsanis IK (2004) A multiple linear regression GIS module using spatial variables to model orographic rainfall. J Hydroinformatics. https://doi.org/10.2166/hydro.2004.0004

    Article  Google Scholar 

  68. Olver PJ, Shakiban C (2018) Undergraduate texts in mathematics applied linear algebra, 2nd edn. Springer International Publishing, Cham, 702 p. ISBN 978-3-319-91040-6

    MATH  Google Scholar 

  69. Jolliffe IT, Stephenson DB (2008) Proper scores for probability forecasts can never be equitable. Mon Weather Rev. https://doi.org/10.1175/2007MWR2194.1

    Article  Google Scholar 

  70. Jolliffe IT, Basilevsky A (1997) Statistical factor analysis and related methods: theory and applications. Biometrics. https://doi.org/10.2307/2533129

    Article  Google Scholar 

  71. Zadeh LA (1965) Fuzzy sets-information and control-1965. Inf Control. https://doi.org/10.1080/00098650209599249

    Article  Google Scholar 

  72. Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc Inst Electr Eng. https://doi.org/10.1049/piee.1974.0328

    Article  Google Scholar 

  73. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/TSMC.1985.6313399

    Article  MATH  Google Scholar 

  74. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  75. Fadaei-Kermani E, Barani GA, Ghaeini-Hessaroeyeh M (2017) Drought monitoring and prediction using K-nearest neighbor algorithm. J AI Data Min 5(2):319–325

    Google Scholar 

  76. Lee TR, Wood WT, Phrampus BJ (2019) A machine learning (kNN) approach to predicting global seafloor total organic carbon. Global Biogeochem Cycles. https://doi.org/10.1029/2018GB005992

    Article  Google Scholar 

  77. Muliono R, Lubis JH, Khairina N (2020) Analysis K-Nearest Neighbor algorithm for improving prediction student graduation time. SinkrOn. https://doi.org/10.33395/sinkron.v4i2.10480

    Article  Google Scholar 

  78. Cover TM, Hart PE (1967) Nearest Neighbor pattern classification. IEEE Trans Inf Theory. https://doi.org/10.1109/TIT.1967.1053964

    Article  MATH  Google Scholar 

  79. Yamaç SS (2021) Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.106968

    Article  Google Scholar 

  80. Altunkaynak A, Kartal E (2021) Transfer sea level learning in the Bosphorus Strait by wavelet based machine learning methods. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2021.109116

    Article  Google Scholar 

  81. Fix E, Hodges J (1951) Nonparametric discrimination: consistency properties. Int Stat Rev 57:238

    Article  MATH  Google Scholar 

  82. Hellman ME (1970) The Nearest Neighbor classification rule with a reject option. IEEE Trans Syst Sci Cybern. https://doi.org/10.1109/TSSC.1970.300339

    Article  MATH  Google Scholar 

  83. Riani M, Atkinson AC (2000) Robust diagnostic data analysis: transformations in regression. Technometrics. https://doi.org/10.1080/00401706.2000.10485711

    Article  MathSciNet  MATH  Google Scholar 

  84. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  85. Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE. https://doi.org/10.13031/201323153

    Article  Google Scholar 

  86. Altunkaynak A (2019) Predicting water level fluctuations in Lake Van using hybrid Season-neuro approach. J Hydrol Eng. https://doi.org/10.1061/(asce)he.1943-5584.0001804

    Article  Google Scholar 

Download references

Acknowledgements

We sincerely thank the Meteorological Service to provide us precipitation data.

Author information

Authors and Affiliations

  1. Hydraulics and Water Resource Engineering Division, Faculty of Civil Engineering, Istanbul Technical University, 34469, Istanbul, Turkey

    Abdüsselam Altunkaynak & Kübra Küllahcı

Authors
  1. Abdüsselam Altunkaynak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kübra Küllahcı
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdüsselam Altunkaynak.

Ethics declarations

Conflict of interest

No funding was received for conducting this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altunkaynak, A., Küllahcı, K. Transfer precipitation learning via patterns of dependency matrix-based machine learning approaches. Neural Comput & Applic 34, 22177–22196 (2022). https://doi.org/10.1007/s00521-022-07674-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07674-8

Keywords

Search

Navigation