Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH 24 -27, 2004AF_42 DAKAR WORKSHOP WELCOME TO THE SECOND AIACC

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN

MARCH 24 -27, 2004 AF_42 DAKAR WORKSHOP

WELCOME TO THE SECOND AIACC WORKSHOP, DAKAR

AF_42 RESEARCH TEAMBOTSWANA



Forecasting impact of climate change on runoff coefficients in Limpopo basin

using Artificial Neural Network

presenter:

Prof. B.P. Parida

University of Botswana, Gaborone



Area :

80 000 km2

~ 1/8 area of Botswana

4 Dams:

350 M Cum.

Farm Land : ~ Food Security







The Limpopo Basin

Multi-cell representation



Year Run off Coeff Year Run off Coeff Year Run off Coeff

1971 0.40 1981 0.35 1991 0.39

1972 0.29 1982 0.38 1992 0.35

1973 0.47 1983 0.35 1993 0.50

1974 0.46 1984 0.34 1994 0.43

1975 0.37 1985 0.48 1995 0.60

1976 0.56 1986 0.40 1996 0.55

1977 0.35 1987 0.52 1997 0.59

1978 0.21 1988 0.38 1998 0.43

1979 0.48 1989 0.36 1999 0.40

1980 0.37 1990 0.56 2000 0.45



Why runoff coefficient (roc) ?

Rainfall ~ Runoff complex

roc = (total runoff) / (total rainfall)

Assumed to marginalize the impact of

land use changes

decrease in rainfall ~ increase in roc

decrease in roc ~ decrease in flow



Source: Hydrological Sciences Journal



Cell body

Nucleus Axons

Dendrites Sunapses

Nucleus

Axon

Dendrides

Components of a neuron



Biological Neuron - specific type of cell - provides cognitive and other related activities.

- Neuron collects signals from dendrites

- Spikes of electrical activity sent out by a neuron through – long thin strands – axon which is split into thousands of branches

- At the end of each branch – synapse, which converts the activity from axon into electrical effects that excite activity. ( changing effectiveness of synapse, influence from preceding neuron is influenced)



Artificial Neuron – simulates the four basic components as well as functioning of the natural neuron.

- Each neuron receives output from many other neurons through input path.

- Each of the inputs to a neuron is multiplied by a weight.

- Products are then summed up and fed through a transfer function to generate an output.



Processing element

Weights Wn Inputs Xn

Output path

Transfer Sum

Xo

X1

X2

X3

Xn

Wo

W1

W2

W3

W4 . . .

The basic structural functioning of a neuron



Input array

Input neurons

First and hidden layer

Output layerneuron

Output array



Output of the best minimized performance function adopted for the study



Regression between target and modelled runoff coeffs

T = TARGETA = ACHIEVEMENT



0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8

Quantity optimized

Qua

ntity

val

ue

Conf. LevlMSEPCs used



Target and simulated runoff coefficients plotted for the entire study period



Input variables used: Annual Rainfall and Annual Evaporation

Target /Output variable: Water balance computed runoff coefficients.

Training Algorithms Used: Automated regularization with early stopping (as it outclassed others)

Transfer functions used: Log-sigmoid for hidden layer and purelin for the output layer.

The optimum number of neurons in the hidden layer: Fifteen

Final Choice of Architecture: Was arrived using PCA, was also found to be the best with two components used and at 0.001 significance level.

For Forecasting/Prediction: Model Predictive Control



Simulations up to year 2000 & Forecasts into the year 2016



0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Year

Ru

no

ff C

oef

fici

ents

By Extrpolation

Excel Toolbox

By ANN

….

A comparison between the forecasted runoff co-efficientobtained from ANN, EXCEL Tool Box & Extrapolation.



y = 0.0035x + 0.4908 y = 0.0004x + 0.4833 y = -0.0006x + 0.4468

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

By Extrapolation

Using ANN

Using Excel Tool Box

Comparison of Trend in Runoff Coefficints.



Period Avg. % increase

ROC per year

1971 - 1980 : 0.40 9

1981 - 1990 : 0.41 (2.5%) 4.7

1991 - 2000 : 0.47 (14.6%) 6.1

2001 - 2010 : 0.48 (2.13%) 4.7

2001 - 2016 : 0.50 (4.2%) 3.8



In conclusion:

It is evident that the by the next two decades runoff is likely to decrease

so a good water management strategy will be necessary as a possible adaptation measure.



Thank You for listening

Documents

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH 24 -27, 2004AF_42 DAKAR WORKSHOP WELCOME TO THE SECOND AIACC