Skip to main content
SpringerLink
Log in

Spatiotemporal precipitation modeling by artificial intelligence-based ensemble approach

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

This study aimed at time-space estimations of monthly precipitation via a two-stage modeling framework. In temporal modeling as the first stage, three different Artificial Intelligence (AI) models were applied to observed precipitation data from 7 gauges located at Northern Cyprus. In this way 2 different input scenarios proposed, by employing different input combinations. Afterwards, the outputs of single AI models were used to generate ensemble techniques to enhance the precision of modeling by the single AI models. For this purpose, 2 linear and 1 non-linear methods of ensembling were designed and afterwards, the results were evaluated. In the second stage, for estimation of the spatial distribution of precipitation over whole region, the results of temporal modeling were used as inputs for the Inverse Distance Weighting (IDW) spatial interpolator. The cross-validation was finally applied to evaluate the overall accuracy of the proposed hybrid spatiotemporal modeling approach. The obtained results in temporal modeling stage demonstrated that the non-linear ensemble technique provided more accurate results. Results of spatial modeling stage indicated that IDW scheme is a good choice for spatial estimation of the precipitation. The overall results show that the combination of temporal and spatial modeling tools could simulate the precipitation appropriately by serving unique features of both tools.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. II: Hydrologic applications. J Hydrol Eng 5(2):124–137

    Google Scholar 

  • Abbot J, Marohasy J (2012) Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Adv Atmos Sci 29(4):717–730

    Google Scholar 

  • Akrami SA, Nourani V, Hakim SJS (2014) Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam. Water Resour Manag 28(10):2999–3018

    Google Scholar 

  • Ashraf M, Loftis JC, Hubbard KG (1997) Application of geostatistics to evaluate partial weather station networks. Agric For Meteorol 84:255–271

    Google Scholar 

  • Bates JM, Granger CWJ (1969) The combination of forecasts. Oper Res Quart 20:451–468

    Google Scholar 

  • Bisht D, Joshi MC, Mehta A (2015) Prediction of monthly rainfall of Nainital region using artificial neural network and support vector machine. Int J Adv Res Innov Ideas Edu 1(3):2395–4396

    Google Scholar 

  • Caruso C, Quarta F (1998) Interpolation methods comparison. Comput Math Appl 35(12):109–126

    Google Scholar 

  • Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10(3):209–222

    Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  • Danandeh Mehr A, Nourani V, Karimi Khosrowshahi V, Ghorbani MA (2018) A hybrid support vector regression–firefly model for monthly rainfall forecasting. Int J Environ Sci Technol 16:335–346

    Google Scholar 

  • Devi SR, Arulmozhivarman P, Venkatesh C (2017) ANN based rainfall prediction—a tool for developing a landslide early warning system. In: Advancing culture of living with landslides—workshop on world landslide forum, pp 175–182

  • Granata F, Papirio S, Esposito G, Gargano R, de Marinis G (2017) Machine learning algorithms for the forecasting of wastewater quality indicators. Water 9(2):105

    Google Scholar 

  • Guhathakurta P (2008) Long lead monsoon rainfall prediction for meteorological sub-divisions of India using deterministic artificial neural network model. Meteorol Atmos Phys 101(2):93–108

    Google Scholar 

  • Haykin S (1994) Neural networks: a comprehensive foundation. McMillan, New York

    Google Scholar 

  • Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci 13:1413–1425

    Google Scholar 

  • Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York

    Google Scholar 

  • Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence. Prentice-Hall, New Jersey

    Google Scholar 

  • Kasiviswanathan KS, Cibin R, Sudheer KP, Chaubey I (2013) Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations. J Hydrol 499:275–288

    Google Scholar 

  • Khalili N, Khodashenas SR, Davary K, Mousavi B, Karimaldini F (2016) Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study. Arab J Geosci 9:624

    Google Scholar 

  • Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25(4):783–792

    Google Scholar 

  • Kong YF, Tong WW (2008) Spatial exploration and interpolation of the surface precipitation data. Geogr Res 27(5):1097–1108

    Google Scholar 

  • Kourentzes N, Barrow DK, Crone F (2014) Neural network ensemble operators for time series forecasting. Expert Syst Appl 41:4235–4244

    Google Scholar 

  • Lu K, Wang L (2011) A novel nonlinear combination model based on support vector machine for rainfall prediction. In: Fourth International Joint Conference on Computational Sciences and Optimization (CSO), Fourth International Joint Conference. IEEE. pp 1343–1347

  • Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055

    Google Scholar 

  • Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecast 1(2):111–153

    Google Scholar 

  • Mehdizadeh S, Behmanesh J, Khalili K (2018) New approaches for estimation of monthly rainfall based on GEP-ARCH and ANN-ARCH hybrid models. Water Resour Manag 32(2):527–545

    Google Scholar 

  • Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Gousheh MG (2011) Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 401(3–4):177–189

    Google Scholar 

  • Nourani V, Andalib G (2015) Daily and monthly suspended sediment load predictions using wavelet-based AI approaches. J. Mt. Sci. 12(1):85–100

    Google Scholar 

  • Nourani V, Ejlali RG, Alami MT (2010) Spatiotemporal groundwater level forecasting in coastal aquifers by hybrid artificial neural network-geostatisics model: a case study. Environ Eng Sci 28(3):217–228

    Google Scholar 

  • Nourani V, RezapourKhanghah T, Hosseini Baghanam A (2014) Implication of feature extraction methods to improve performance of hybrid Wavelet-ANN rainfall–runoff model. In: Issac B, Israr N (eds) Case studies in intelligent computing. Taylor and Francis Group, New York, pp 457–498

    Google Scholar 

  • Nourani V, Uzelaltinbulat S, Sadikoglu F, Behfar N (2019) Artificial intelligence based ensemble modeling for multi-station prediction of precipitation. Atmosphere 10(2):80

    Google Scholar 

  • Partal T, Cigizoglu HK (2008) Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J Hydrol 358(3–4):317–331

    Google Scholar 

  • Price C, Michaelides S, Pashiardis S, Alperta P (1999) Long term changes in diurnal temperature range in Cyprus. Atmos Res 51(2):85–98

    Google Scholar 

  • Rizzo DM, Dougherty DE (1994) Characterization of aquifer properties using artificial neural networks: neural Kriging. Water Resour Res 30:483–497

    Google Scholar 

  • Sahoo M, Das T, Kumari K, Dhar A (2017) Space–time forecasting of groundwater level using a hybrid soft computing model. Hydrol Sci J 62(4):561–574

    Google Scholar 

  • Shahidi M, Abedini MJ (2018) Optimal selection of number and location of rain gauge stations for areal estimation of annual rainfall using a procedure based on inverse distance weighting estimator. Paddy Water Environ 16(3):617–629

    Google Scholar 

  • Sharghi E, Nourani V, Behfar N (2018) Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. J Hydroinf 20(5):1071–1084

    Google Scholar 

  • Sharghi E, Nourani V, Najafi H, Soleimani S (2019) Wavelet-exponential smoothing: a new hybrid method for suspended sediment load modeling. Environ Process 6(1):191–218

    Google Scholar 

  • Sharifi SS, Delirhasannia R, Nourani V, Sadraddini AA, Ghorbani A (2013) Using artificial neural networks (ANNs) and adaptive Neuro-Fuzzy Inference System (ANFIS) for modeling and sensitivity analysis of effective rainfall. In: Mladenov V (eds) Recent advances in continuum mechanics, hydrology and ecology, pp 133–139

  • Singh VK, Kumar P, Singh BP, Malik A (2016) A comparative study of adaptive neuro fuzzy inference system (ANFIS) and multiple linear regression (MLR) for rainfall-runoff modeling. Int J Sci Nat 7(4):714–723

    Google Scholar 

  • Sojitra MA, Purohit RC, Pandya PA (2015) Comparative study of daily rainfall forecasting models using ANFIS. Curr World Environ 10(2):529–536

    Google Scholar 

  • Theodossiou N, Latinopoulos P (2006) Evaluation and optimisation of groundwater observation networks using the Kriging methodology. Environ Model Softw 21(7):991–1000

    Google Scholar 

  • Uzelaltinbulat S, Sadikoglu F, Nourani V (2019) Comparative analysis of artificial intelligence based methods for prediction of precipitation. case study: North Cyprus. In: Aliev R, Kacprzyk J, Pedrycz W, Jamshidi M, Sadikoglu F (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing—ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing. Springer, Cham, vol 896, pp 51–64

    Google Scholar 

  • Yamashkin S, Radovanovic M, Yamashkin A, Vukovic D (2018) Using ensemble systems to study natural processes. J Hydroinf 20(4):753–765

    Google Scholar 

  • Yang HH, Vuuren SV, Sharma S, Hermansky H (2000) Relevance of time frequency features for phonetic and speaker-channel classification. Speech Commun 31:35–50

    Google Scholar 

  • Yaseen ZM, Ghareb MI, Ebtehaj I, Bonakdari H, Siddique R, Heddam S, Yusif AA, Deo R (2018) Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA. Water Resour Manag 32(1):105–122

    Google Scholar 

  • Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175

    Google Scholar 

  • Zhang GP, Berardi VL (2001) Time series forecasting with neural network ensembles: an application for exchange rate prediction. J Oper Res Soc 52:652–664

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, 29 Bahman Ave., Tabriz, 51666, Iran

    Vahid Nourani

  2. Faculty of Civil and Environmental Engineering, Near East University, Near East Boulevard, 99138, Nicosia, North Cyprus, via Mersin 10, Turkey

    Vahid Nourani

  3. Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, 29 Bahman Ave., Tabriz, Iran

    Vahid Nourani & Nazanin Behfar

  4. Department of Computer Engineering, Faculty of Engineering, Near East University, POBOX: 99138, Nicosia, TRNC, Mersin 10, Turkey

    Selin Uzelaltinbulat

  5. Department of Electrical & Electronic Engineering, Faculty of Engineering, Near East University, POBOX: 99138, Nicosia, TRNC, Mersin 10, Turkey

    Fahreddin Sadikoglu

Authors
  1. Vahid Nourani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nazanin Behfar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Selin Uzelaltinbulat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fahreddin Sadikoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vahid Nourani.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nourani, V., Behfar, N., Uzelaltinbulat, S. et al. Spatiotemporal precipitation modeling by artificial intelligence-based ensemble approach. Environ Earth Sci 79, 6 (2020). https://doi.org/10.1007/s12665-019-8755-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-019-8755-5

Keywords

Search

Navigation