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Article
Author(s)
Iván Ayala Bizarro1, Miguel Zubiaur Alejos2 and Jessica Zúñiga Mendoza3
Full-Text PDF XML 874 Views
DOI:10.17265/2162-5263/2018.07.002
Affiliation(s)
1. Department of Civil Engineering, National University of Huancavelica, Huancavelica 09001, Peru
2. Department of Civil Engineering, National University of Engineering, Lima 15333, Peru
3. AYZU Engineering Company, Huancavelica 09001, Peru
ABSTRACT
The aim of the study
is to determine the performance of the regional agricultural drought prediction
by the model of ANN (Artificial Neural Networks)
type NARX, using the SPI (Standardized Precipitation Index),
SPEI (Precipitation
Index Standardized Evapotranspiration), VCI (Vegetation Condition Index)
and GCI (Global
Climate Indexes). There have been determined 10 homogeneous
regions through RAF (regional frequency analysis)
and L-moments, defining the most arid region and the index representing their
respective time scale (SPEI 6 months) which responds to the growth and
development of vegetation in the basin correlation Pearson equal to 0.58.
Monthly rainfall and temperatures correspond to PISCO data prepared by
SENAMHI-Peru, with space resolution of 0.05 degrees. For prediction, they have determined two groups, the first to
build the model with 80% of the registration and validation of the model and
the hypothesis with the remaining 20%. The results have been satisfactory
prediction accepting the null hypothesis.
KEYWORDS
Drought, ANN, SPI-SPEI-VCI, RAF, L-Moments.
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