Calculation of Standardized Precipitation Index, using the Genetic Algorithm Method (SPIGA) and Maximum Likelihood (SPIML)

Description

Calculate the standardized precipitation index (SPI) for monitoring drought using the technique of Genetic Algorithm (SPIGA) and Maximum Likelihood (SPIML) of a series of monthly rainfall for different time scales.

Usage

SPIGA(Pmon, scale = 3, population = 500, maxIter = 50, plotGA = FALSE, plotCDF = FALSE)

SPIML(Pmon, scale =3)

Arguments

Details

The SPIGA and SPIML, are functions to calculate the SPI using Artificial Intelligence techniques - Genetic Algorithms (GA) and numerical method - Maximum Likelihood (ML) and both provide quantitative results for monitoring DROUGHT. The GA optimize the parameters alpha and beta of the probability function Gamma given by McKee.

The population parameter must be an integer and balanced value, large values can generate higher time run, ie, high computational effort and small values can influence the accuracy of the results. By plotGA option and its corresponding graph, you can see the number of cycles to obtain a proper balance of the accuracy of the results and the computational effort.

Input data

similar to Pm_Pisco.

Value

Functions SPIGA and SPIML return values saved in .txt formats (Tabular) and .pdf (graphics). They are located in the working folder of R [getwd()].

Note

Dependencies: the SPIGA function, depend on the library GA.

Author(s)

Iv<c3><83><c2><a1>n Arturo Ayala Bizarro <ivan.ayala@unh.edu.pe>

Jessica Z<c3><83><c2><ba><c3><83><c2><b1>iga Mendoza <zumeje@gmail.com>

References

McKee, Thomas B. and Doesken, Nolan J. and Kleist, John. 1993. The relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology

A. Belauneh and J. Adamowski. Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression. Applied Computational Intelligence and Soft Computing, http://dx.doi.org/10.1155/2012/794061

See Also

SPIFromParameters to calculate the standardized precipitation index, from alpha and beta parameter of the Gamma function.

Examples

#### Load data
data(Pm_Pisco)
Pmon<-Pm_Pisco      # dataframe Precipitation
summary(Pm_Pisco)   # view summary
Pmon<-Pm_Pisco[,]

#### Computing SPI with Genetic Algorithms
pob     <-50        # Define population number
iMax    <-10        # Define Max iteration

# Total stations calculation. It may take some time.
#SPIGA(Pmon, scale=3, population=pob, maxIter = iMax, plotGA=TRUE, plotCDF=TRUE)

# station 1 computing
Pmon1<-data.frame(Pmon[,1:2], Pmon$Pm_St1)
SPIGA(Pmon1, scale=3, population=pob, maxIter = iMax)

# station 2 computing
Pmon2<-data.frame(Pmon[,1:2], Pmon$Pm_St2)
SPIGA(Pmon2, scale=3, population=pob, maxIter = iMax)

#### Computing SPI with Maximun Likelihood
SPIML(Pmon, scale=3)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SPIGA)
Loading required package: GA
Loading required package: foreach
Loading required package: iterators
Package 'GA' version 3.0.2
Type 'citation("GA")' for citing this R package in publications.
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SPIGA/SPIGA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Drought Index
> ### Title: Calculation of Standardized Precipitation Index, using the
> ###   Genetic Algorithm Method (SPIGA) and Maximum Likelihood (SPIML)
> ### Aliases: SPIGA SPIML
> 
> ### ** Examples
> 
> #### Load data
> data(Pm_Pisco)
> Pmon<-Pm_Pisco      # dataframe Precipitation
> summary(Pm_Pisco)   # view summary
      year          month            Pm_St1             Pm_St2        
 Min.   :1981   Min.   : 1.000   Min.   :  0.5866   Min.   :  0.5362  
 1st Qu.:1989   1st Qu.: 3.250   1st Qu.: 16.5649   1st Qu.: 14.0321  
 Median :1998   Median : 6.000   Median : 46.8745   Median : 47.4559  
 Mean   :1998   Mean   : 6.476   Mean   : 62.0483   Mean   : 59.3716  
 3rd Qu.:2007   3rd Qu.: 9.000   3rd Qu.: 98.8216   3rd Qu.: 96.8824  
 Max.   :2015   Max.   :12.000   Max.   :236.0222   Max.   :239.9725  
     Pm_St3             Pm_St4        
 Min.   :  0.4474   Min.   :  0.5104  
 1st Qu.: 13.4251   1st Qu.: 18.5945  
 Median : 43.8687   Median : 48.0113  
 Mean   : 57.3330   Mean   : 65.4650  
 3rd Qu.: 90.9596   3rd Qu.:100.2296  
 Max.   :236.6156   Max.   :244.5666  
> Pmon<-Pm_Pisco[,]
> 
> #### Computing SPI with Genetic Algorithms
> pob     <-50        # Define population number
> iMax    <-10        # Define Max iteration
> 
> # Total stations calculation. It may take some time.
> #SPIGA(Pmon, scale=3, population=pob, maxIter = iMax, plotGA=TRUE, plotCDF=TRUE)
> 
> # station 1 computing
> Pmon1<-data.frame(Pmon[,1:2], Pmon$Pm_St1)
> SPIGA(Pmon1, scale=3, population=pob, maxIter = iMax)
********** Calculation Station Pmon.Pm_St1 

SPIGA: Successful Analysis
See the results:  /home/ddbj/DataUpdator-rgm3/target 
> 
> # station 2 computing
> Pmon2<-data.frame(Pmon[,1:2], Pmon$Pm_St2)
> SPIGA(Pmon2, scale=3, population=pob, maxIter = iMax)
********** Calculation Station Pmon.Pm_St2 

SPIGA: Successful Analysis
See the results:  /home/ddbj/DataUpdator-rgm3/target 
> 
> #### Computing SPI with Maximun Likelihood
> SPIML(Pmon, scale=3)
********** Calculation Station Pm_St1 
********** Calculation Station Pm_St2 
********** Calculation Station Pm_St3 
********** Calculation Station Pm_St4 

SPIML: Successful Analysis
See the results:  /home/ddbj/DataUpdator-rgm3/target 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>