| Type: | Package |
| Title: | Compute SPI Index using the Methods Genetic Algorithm and Maximum Likelihood |
| Version: | 1.0.0 |
| Date: | 2016-06-09 |
| Maintainer: | Iván Ayala-Bizarro <ivan.ayala@unh.edu.pe> |
| NeedsCompilation: | no |
| Description: | Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx. |
| Depends: | GA |
| License: | GPL-2 |
| LazyData: | TRUE |
| Encoding: | UTF-8 |
| Repository: | CRAN |
| Packaged: | 2016-06-16 15:28:31 UTC; abia |
| Author: | Iván Ayala-Bizarro [aut, cre], Jessica Zúñiga-Mendoza [aut] |
| Date/Publication: | 2016-06-16 18:26:21 |
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
Pmon |
monthly precipitation series ordered according to time. It is a data frame with columns: year, month, station 1, station 2, etc. |
scale |
an integer value representing the time scale of analysis. The most common are 1, 3, 6, 9, 12, 48, etc. |
population |
an integer value that sets the number of population for the use of the technique of Genetic Algorithm. |
maxIter |
an integer value that sets the maximum number of iterations also called cycles within the concept of Genetic Algorithm. |
plotGA |
optional, value Boolean default false. Shows the performance versus the number of cycles in the Genetic Algorithm. |
plotCDF |
optional, value Boolean default false. Shows the cumulative distribution function of each station. The graphics are monthly. |
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.
| Year | Mon | st_1 | st_2 | st_3 | st_4 |
| 1981 | 1 | 120.25 | 125.25 | 90.55 | 150.25 |
| 1981 | 2 | 145.25 | 140.25 | 120.70 | 145.50 |
| 1981 | 3 | 120.80 | 150.28 | 90.50 | 130.40 |
| 1981 | 4 | 90.25 | 80.25 | 70.52 | 120.40 |
| 1981 | 5 | 50.25 | 58.25 | 60.50 | 80.50 |
| 1981 | 6 | 40.25 | 38.45 | 80.25 | 50.40 |
| 1981 | 7 | 20.25 | 30.69 | 50.40 | 40.40 |
| 1981 | 8 | 1.25 | 8.85 | 10.40 | 25.80 |
| 1981 | 9 | 25.25 | 14.25 | 5.80 | 20.80 |
| 1981 | 10 | 13.25 | 10.23 | 10.50 | 30.45 |
| 1981 | 11 | 50.25 | 40.25 | 30.50 | 80.50 |
| 1981 | 12 | 80.25 | 90.52 | 80.70 | 90.40 |
| 1982 | 1 | 145.80 | 110.25 | 105.40 | 120.25 |
| . | . | . | . | . | . |
| . | . | . | . | . | . |
| . | . | . | . | . | . |
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)
Iván Arturo Ayala Bizarro <ivan.ayala@unh.edu.pe>
Jessica Zúñiga 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)
Calculation of standardized precipitation index from alpha and beta parameter of the Gamma function.
Description
calculate the standardized precipitation index, from alpha and beta parameter of the Gamma function.
Usage
SPIFromParameters(Pmon, scale =3, Param_Alpha, Param_Beta)
Arguments
Pmon |
monthly precipitation series ordered according to time. It is a data frame with columns: year, month, station 1, station 2, etc. |
scale |
an integer value representing the time scale of analysis. The most common are 1, 3, 6, 9, 12, 48, etc. |
Param_Alpha |
data frame monthly data values corresponding to the alpha parameter to the function Gamma. |
Param_Beta |
data frame monthly data values corresponding to the alpha parameter to the function Gamma. |
Details
Analysis stations are in the columns of dataframe. the apha and beta parameters, are monthly and are in the rows of dataframe.
Input data
similar to Pm_Pisco.
| Mon | st_1 | st_1 | st_2 | st_3 |
| Jan | 9.584860915 | 9.227918987 | 10.35269003 | 8.433823824 |
| Feb | 13.76378505 | 15.02620223 | 12.1021093 | 10.85133914 |
| Mar | 26.09112343 | 17.41749632 | 21.10924889 | 23.53649421 |
| Apr | 17.34996675 | 17.4451073 | 13.00894394 | 16.66595319 |
| May | 9.943259493 | 9.46815537 | 9.164645239 | 9.455850664 |
| Jun | 5.103175852 | 5.041710686 | 4.080851346 | 5.790986084 |
| Jul | 2.85804336 | 3.042484994 | 2.797962575 | 2.645188236 |
| Aug | 3.033862506 | 3.183267843 | 3.435303986 | 2.631287947 |
| Sep | 3.815308513 | 2.627317533 | 3.550365645 | 3.66482456 |
| Oct | 7.430925356 | 3.956716609 | 7.023105167 | 7.540706878 |
| Nov | 6.303310502 | 5.339943557 | 6.358902249 | 5.556660824 |
| Dec | 5.84110559 | 5.899534971 | 6.581657735 | 4.889599504 |
Value
return values of standardized precipitation index in .txt formats.
Author(s)
Iván Arturo Ayala Bizarro <ivan.ayala@unh.edu.pe>
Jessica Zúñiga Mendoza <zumeje@gmail.com>
References
McKee, Thomas B. and Doesken, Nolan J. and Kleist, John. 1993. The relationship of Drought Frecuency 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
Examples
#### Load data
data(Pm_Pisco)
data(alphaGA_SPI3)
data(betaGA_SPI3)
#### Computing SPI with Genetic Algorithms
Pmon<-Pm_Pisco
Param_Alpha <- alphaGA_SPI3
Param_Beta <- betaGA_SPI3
SPIFromParameters(Pmon, scale =3, Param_Alpha, Param_Beta)
Generic methods for SPIGA objects.
Description
Generic methods for extracting information and plotting SPIGA objects.
Usage
calcSPI(Pt,alpha, beta,m, nd)
Arguments
Pt |
monthly precipitation series. |
alpha |
parameter to Gamma function. |
beta |
parameter to Gamma function. |
m |
Number of zeros in the column. |
nd |
Number total data. |
Author(s)
Iván Arturo Ayala Bizarro <ivan.ayala@unh.edu.pe>
Jessica Zúñiga 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
The data set for illustratrating the functions of the SPIGA package
Description
The set used, data are monthly rainfall (1981-2015) and the dimensionless parameters to calculate the SPI drought.
Usage
data(Pm_Pisco)
data(alphaGA_SPI3)
data(betaGA_SPI3)
Format
Pm_Pisco dataframe with:
- YEAR
monthly precipitation totals, in mm.
- MONTH
monthly precipitation totals, in mm.
- P1
monthly precipitation totals st-1, in mm.
- P2
monthly precipitation totals st-2, in mm.
- ...
monthly precipitation totals st-n, in mm.
alphaGA_SPI3 dataset: monthly alpha parameter.
- mon
month analysis
- st-1
monthly alpha parameter station 1
- st-2
monthly alpha parameter station 2
- st-n
monthly alpha parameter station n
betaGA_SPI3 dataset: monthly beta parameter.
- mon
month analysis
- st-1
monthly beta parameter station 1
- st-2
monthly beta parameter station 2
- st-n
monthly beta parameter station n
Author(s)
Iván Arturo Ayala Bizarro <ivan.ayala@unh.edu.pe>
Jessica Zúñiga Mendoza <zumeje@gmail.com>
Source
The Pm_Pisco data were obtained from the Peruvian Interpolation data of the SENAMHI's Climatological and Hidrological Observations, SENAMHI-PERU. http://peruclima.pe/.
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
Examples
data(Pm_Pisco)
names(Pm_Pisco)
summary(Pm_Pisco)
data(alphaGA_SPI3)
names(alphaGA_SPI3)
summary(alphaGA_SPI3)
data(betaGA_SPI3)
names(betaGA_SPI3)
summary(betaGA_SPI3)