Package oms3.ngmf.util.cosu

Class Efficiencies

java.lang.Object

oms3.ngmf.util.cosu.Efficiencies

public class Efficiencies
extends Object

Author:

od

Field Summary

Fields

Modifier and Type	Field	Description
static int	ABSMAXIMIZATION
static int	ABSMINIMIZATION
static int	MAXIMIZATION
static int	MINIMIZATION

Method Summary

Modifier and Type	Method	Description
static double	absDiff(double[] obs, double[] sim)
static double	absDiffLog(double[] obs, double[] sim)
static double	absVolumeError(double[] obs, double[] sim)
static double	dsGrad(double[] obs, double[] sim)
static double	ioa(double[] obs, double[] sim, double pow)	Calculates the index of agreement (ioa) between a test data set and a verification data set after Willmot & Wicks (1980).
static double[]	linearReg(double[] xData, double[] yData)	Calcs coefficients of linear regression between x, y data
static double	nashSutcliffe(double[] obs, double[] sim, double pow)	Calculates the efficiency between a test data set and a verification data set after Nash & Sutcliffe (1970).
static double	nashSutcliffeLog(double[] obs, double[] sim, double pow)	Calculates the efficiency between the log values of a test data set and a verification data set after Nash & Sutcliffe (1970).
static double	pbias(double[] obs, double[] sim)
static double	pearsonsCorrelatrion(double[] obs, double[] sim)
static double	rmse(double[] obs, double[] sim)
static double	runoffCoefficientError(double[] obs, double[] sim, double[] precip)	Runoff coefficient error ROCE
static double	transformedRmse(double[] obs, double[] sim)	transformedRootMeanSquareError TRMSE

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

MAXIMIZATION

public static final int MAXIMIZATION

See Also:

Constant Field Values

MINIMIZATION

public static final int MINIMIZATION

See Also:

Constant Field Values

ABSMAXIMIZATION

public static final int ABSMAXIMIZATION

See Also:

Constant Field Values

ABSMINIMIZATION

public static final int ABSMINIMIZATION

See Also:

Constant Field Values

Method Detail

nashSutcliffe

public static double nashSutcliffe(double[] obs,
                                   double[] sim,
                                   double pow)

Calculates the efficiency between a test data set and a verification data set after Nash & Sutcliffe (1970). The efficiency is described as the proportion of the cumulated cubic deviation between both data sets and the cumulated cubic deviation between the verification data set and its mean value.

Parameters:

sim - the simulation data set

obs - the validation (observed) data set

pow - the power for the deviation terms

Returns:

the calculated efficiency or -9999 if an error occurs

nashSutcliffeLog

public static double nashSutcliffeLog(double[] obs,
                                      double[] sim,
                                      double pow)

Calculates the efficiency between the log values of a test data set and a verification data set after Nash & Sutcliffe (1970). The efficiency is described as the proportion of the cumulated cubic deviation between both data sets and the cumulated cubic deviation between the verification data set and its mean value. If either prediction or validation has a value of <= 0 then the pair is ommited from the calculation and a message is put to system out.

Parameters:

sim - the simulation data set

obs - the validation (observed) data set

pow - the power for the deviation terms

Returns:

the calculated log_efficiency or -9999 if an error occurs

ioa

public static double ioa(double[] obs,
                         double[] sim,
                         double pow)

Calculates the index of agreement (ioa) between a test data set and a verification data set after Willmot & Wicks (1980). The ioa is described as the proportion of the cumulated cubic deviation between both data sets and the squared sum of the absolute deviations between the verification data set and the test mean value and the test data set and its mean value.

Parameters:

sim - the test Data set

obs - the verification data set

pow - the power

Returns:

the calculated ioa or -9999 if an error occurs

linearReg

public static double[] linearReg(double[] xData,
                                 double[] yData)

Calcs coefficients of linear regression between x, y data

Parameters:

xData - the independent data array (x)

yData - the dependent data array (y)

Returns:

(intercept, gradient, r?)

dsGrad

public static double dsGrad(double[] obs,
                            double[] sim)

Parameters:

prediction -

validation -

Returns:

absVolumeError

public static double absVolumeError(double[] obs,
                                    double[] sim)

Parameters:

prediction -

validation -

Returns:

pbias

public static double pbias(double[] obs,
                           double[] sim)

Parameters:

prediction -

validation -

Returns:

rmse

public static double rmse(double[] obs,
                          double[] sim)

Parameters:

prediction -

validation -

Returns:

absDiffLog

public static double absDiffLog(double[] obs,
                                double[] sim)

Parameters:

validation -

prediction -

missVal -

Returns:

absDiff

public static double absDiff(double[] obs,
                             double[] sim)

Parameters:

validation -

prediction -

missVal -

Returns:

pearsonsCorrelatrion

public static double pearsonsCorrelatrion(double[] obs,
                                          double[] sim)

Parameters:

validation -

prediction -

missVal -

Returns:

transformedRmse

public static double transformedRmse(double[] obs,
                                     double[] sim)

transformedRootMeanSquareError TRMSE

Parameters:

obs -

sim -

Returns:

runoffCoefficientError

public static double runoffCoefficientError(double[] obs,
                                            double[] sim,
                                            double[] precip)

Runoff coefficient error ROCE

Parameters:

obs -

sim -

precip -

Returns: