Optimal functional forms for estimation of missing precipitation data

Ramesh S.V. Teegavarapu, Mohammad Tufail, Lindell Ormsbee

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


A fixed functional set genetic algorithm method (FFSGAM) is proposed and is investigated in the current study to obtain optimal functional forms for estimating missing precipitation data. The FFSGAM provides functional forms with optimal combination of parameters of surrogate and actual measures of strength of correlation among observations for estimating missing data. The method uses genetic algorithms and a nonlinear optimization formulation to obtain optimal functional forms and coefficients, respectively. Historical daily precipitation data available from 15 rain gaging stations from the state of Kentucky, USA, are used to test the functional forms and derive conclusions about the efficacy of the proposed method for estimating missing precipitation data. The tests of FFSGAM at two rainfall gaging stations in Kentucky, using multiple error and performance indices, indicate that better estimates of precipitation can be obtained compared to those from a traditional inverse distance weighting technique. Also, results from the use of the method confirm its robustness when only six rain gaging stations out of 14 were used for estimating missing data.

Original languageEnglish
Pages (from-to)106-115
Number of pages10
JournalJournal of Hydrology
Issue number1-2
StatePublished - Jul 30 2009


  • Distance weighting methods
  • Fixed function set genetic algorithm method
  • Genetic algorithms
  • Missing precipitation data
  • Optimal functional forms
  • Spatial interpolation

ASJC Scopus subject areas

  • Water Science and Technology


Dive into the research topics of 'Optimal functional forms for estimation of missing precipitation data'. Together they form a unique fingerprint.

Cite this