Precipitation pattern modeling using cross-station perception: regional investigation

  • Sadeq Oleiwi Sulaiman
  • Jalal Shiri
  • Hamed Shiralizadeh
  • Ozgur Kisi
  • Zaher Mundher YaseenEmail author
Original Article


Establishing robust models for predicting precipitation processes can yield a significant aspect for many applications in water resource engineering and environmental prospective. In particular, understanding precipitation phenomena is crucial for managing the effects of flooding in watersheds. In this research, a regional precipitation pattern modeling was undertaken using three intelligent predictive models incorporating artificial neural network (ANN), support vector machine (SVM) and random forest (RF) methods. The modeling was carried out using monthly time scale precipitation information in a semi-arid environment located in Iraq. Twenty weather stations covering the entire region were used to construct the predictive models. At the initial stage, the region was divided into three climatic districts based on documented research. Initially, modeling was carried out for each district using historical information from regionally distributed meteorological stations for calibration. Subsequently, cross-station modeling was undertaken for each district using precipitation data from other districts. The study demonstrated that cross-station modeling was an effective means of predicting the spatial distribution of precipitation in watersheds with limited meteorological data.


Precipitation forecasting Intelligence models Cross-station modeling Semi-arid environment 



Authors would like to acknowledge their gratitude and appreciate for the Meteorological Organization of Seismology (IMOS), Ministry of Agriculture and Water Resources of Kurdistan, for providing the precipitation data set of the studied case study and their admirable cooperation. Also, we thank all reviewers and editors for their insightful comments that improved the clarity of the final paper.

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest to declare for publishing this article.


  1. 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 28:2999–3018. CrossRefGoogle Scholar
  2. Al-Ansari NA (2013) Management of water resources in Iraq: perspectives and prognoses. Engineering 05:667–684. CrossRefGoogle Scholar
  3. Al-Ansari N, Ali AA, Knutsson S (2014) Present conditions and future challenges of water resources problems in Iraq. J Water Resour Prot 6:1066–1098. CrossRefGoogle Scholar
  4. Ali SH, Daood NA, Ibrahim LK (2015) Derivation of seasonal variation maps of the average precipitation in Iraq by using remote sensing data and Geographical Information System. Arab J Geosci 8:4741–4753. CrossRefGoogle Scholar
  5. Arnell NW (1999) Climate change and global water resources. Global Environ Change 9:S31–S49CrossRefGoogle Scholar
  6. Breiman L (2001) Random forests. Mach Learn 45:5–32. CrossRefGoogle Scholar
  7. Chowdary VM, Ramakrishnan D, Srivastava YK et al (2009) Integrated water resource development plan for sustainable management of Mayurakshi Watershed, India using remote sensing and GIS. Water Resour Manag 23:1581–1602. doiCrossRefGoogle Scholar
  8. Dahamsheh A, Aksoy H (2013) Markov chain-incorporated artificial neural network models for forecasting monthly precipitation in arid regions. Arab J Sci Eng. CrossRefGoogle Scholar
  9. Deo RC, Kisi O, Singh VP (2016) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res. CrossRefGoogle Scholar
  10. Dore MHI (2005) Climate change and changes in global precipitation patterns: what do we know? Environ Int 31:1167–1181CrossRefGoogle Scholar
  11. Fahimi F, Yaseen ZM, El-shafie A (2016) Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol. CrossRefGoogle Scholar
  12. Frumau KFA, Bruijnzeel LAS, Tobón C (2011) Precipitation measurement and derivation of precipitation inclination in a windy mountainous area in northern Costa Rica. Hydrol Process 25:499–509. CrossRefGoogle Scholar
  13. Gupta A, Kamble T, Machiwal D (2017) Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India. Environ Earth Sci. CrossRefGoogle Scholar
  14. Haddad K, Rahman A (2014) Derivation of short-duration design rainfalls using daily rainfall statistics. Nat Hazards 74:1391–1401. CrossRefGoogle Scholar
  15. Hamidi O, Poorolajal J, Sadeghifar M (2014) A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theor Appl Climatol. CrossRefGoogle Scholar
  16. Haykin S (1999) Neural networks: a comprehensive foundation, Prentic-Hall, Upper Saddle River, New Jersey, p 842Google Scholar
  17. 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. CrossRefGoogle Scholar
  18. Jalalkamali A, Moradi M, Moradi N (2015) Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index. Int J Environ Sci Technol 12:1201–1210. CrossRefGoogle Scholar
  19. Jeong C, Shin JY, Kim T, Heo JH (2012) Monthly precipitation forecasting with a neuro-fuzzy model. Water Resour Manag 26:4467–4483. CrossRefGoogle Scholar
  20. Joshi S, Kumar K, Joshi V, Pande B (2014) Rainfall variability and indices of extreme rainfall-analysis and perception study for two stations over Central Himalaya, India. Nat Hazards 72:361–374CrossRefGoogle Scholar
  21. Joshi N, Gupta D, Suryavanshi S et al (2016) Analysis of trends and dominant periodicities in drought variables in India: a wavelet transform based approach. Atmos Res 182:200–220. CrossRefGoogle Scholar
  22. Keyantash J, Dracup JA (2002) The quantification of drought: An evaluation of drought indices. Bull Am Meteorol Soc 83:1167–1180CrossRefGoogle Scholar
  23. Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25:783–792. CrossRefGoogle Scholar
  24. Kumar M, Raghuwanshi N, Singh R et al (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224–233. CrossRefGoogle Scholar
  25. Li B, Yang G, Wan R et al (2016) Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the {Poyang} {Lake} in {China}. Hydrol Res. CrossRefGoogle Scholar
  26. Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2:18–22. CrossRefGoogle Scholar
  27. Ließ M, Glaser B, Huwe B (2012) Uncertainty in the spatial prediction of soil texture. Comparison of regression tree and Random Forest models. Geoderma 170:70–79. CrossRefGoogle Scholar
  28. Lin GF, Jhong BC (2015) A real-time forecasting model for the spatial distribution of typhoon rainfall. J Hydrol 521:302–313. CrossRefGoogle Scholar
  29. Mishra AK, Singh VP (2011) Drought modeling—a review. J Hydrol 403:157–175CrossRefGoogle Scholar
  30. Nastos PT, Paliatsos AG, Koukouletsos KV et al (2014) Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece. Atmos Res 144:141–150. CrossRefGoogle Scholar
  31. Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. J Hydrol 514:358–377. CrossRefGoogle Scholar
  32. Oladipo EO (1995) Some statistical characteristics of drought area variations in the savanna region of Nigeria. Theor Appl Climatol 50:147–155. CrossRefGoogle Scholar
  33. Olmstead SM (2010) The economics of managing scarce water resources. Rev Environ Econ Policy 4:179–198. CrossRefGoogle Scholar
  34. Osborn TJ, Wallace CJ, Harris IC, Melvin TM (2016) Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation. Clim Change 134:353–369. CrossRefGoogle Scholar
  35. Osman Y, Al-Ansari N, Abdellatif M et al (2014) Expected future precipitation in central Iraq using LARS-WG Stochastic weather generator. Engineering 06:948–959. CrossRefGoogle Scholar
  36. Osman Y, Abdellatif M, Al-Ansari N et al (2017) Climate change and future precipitation in arid environment of middle east: case study of Iraq. J Environ Hydrol 25:1–18Google Scholar
  37. Partal T, Kişi Ö (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212. CrossRefGoogle Scholar
  38. Polishchuk PG, Muratov EN, Artemenko AG et al (2009) Application of random forest approach to QSAR prediction of aquatic toxicity. J Chem Inf Model 49:2481–2488. CrossRefGoogle Scholar
  39. Rodriguez-Galiano VF, Ghimire B, Rogan J et al (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104. CrossRefGoogle Scholar
  40. Ruiz-Gazeb A, Villa N (2007) Storms prediction: Logistic regression vs random forest for unbalanced data. Case Stud Business Ind Gov Stat 1:91–101Google Scholar
  41. Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4:24–38. CrossRefGoogle Scholar
  42. Sayl KN, Muhammad NS, Yaseen ZM, El-shafie A (2016) Estimation the physical variables of rainwater harvesting system using integrated GIS-based remote sensing approach. Water Resour Manag 30:3299–3313. CrossRefGoogle Scholar
  43. Segal MR (2004) Machine learning benchmarks and random forest regression. Biostatistics 1–14Google Scholar
  44. Shiri J, Nazemi AH, Sadraddini AA et al (2014) Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Comput Electron Agric 108:230–241. CrossRefGoogle Scholar
  45. Srivastava G, Panda SN, Mondal P, Liu J (2010) Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique. J Hydrol 395:190–198. CrossRefGoogle Scholar
  46. Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 14:323–348. CrossRefGoogle Scholar
  47. Sulaiman J, Darwis H, Hirose H (2013) Forecasting monthly maximum 5-day precipitation using artificial neural networks with initial lags. In: Proceedings—6th International Symposium on Computational Intelligence and Design, ISCID 2013. pp 3–7Google Scholar
  48. Van Loon AF, Laaha G (2015) Hydrological drought severity explained by climate and catchment characteristics. J Hydrol 526:3–14. CrossRefGoogle Scholar
  49. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  50. Vapnik VN (2000) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  51. Yaseen ZM, Deo RC, Hilal A et al (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Dams and Water Resources Department, College of EngineeringUniversity of AnbarRamadiIraq
  2. 2.Water Engineering Department, Faculty of AgricultureUniversity of TabrizTabrizIran
  3. 3.Faculty of Natural Sciences and EngineeringIlia State UniversityTbilisiGeorgia
  4. 4.Sustainable Developments in Civil Engineering Research Group, Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam

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