Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks.

ABSTRACT: The success of many projects linked to the management and planning of water resources depends mainly on the quality of the climatic and hydrological data that is provided. Nevertheless, the missing data are frequently found in hydroclimatic variables due to measuring instrument failures, observation recording errors, meteorological extremes, and the challenges associated with accessing measurement areas. Hence, it is necessary to apply an appropriate fill of missing data before any analysis. This paper is intended to present the filling of missing data of monthly rainfall of 45 gauge stations located in southwestern Colombia. The series analyzed covers 34 years of observations between 1983 and 2016, available from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM).

SUBMITTER: Canchala-Nastar T 

PROVIDER: S-EPMC6811873 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature