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WELCOME TO THE SECOND AIACC WORKSHOP, DAKAR. AF_42 RESEARCH TEAM BOTSWANA. 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 km 2

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WELCOME TO THE SECOND AIACC WORKSHOP, DAKAR


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Presentation Transcript
slide1

WELCOME TO THE SECOND AIACC

WORKSHOP, DAKAR

AF_42 RESEARCH TEAM

BOTSWANA

AF_42 DAKAR WORKSHOP

slide2

Forecasting impact of climate change on runoff coefficients in Limpopo basin using Artificial Neural Network

presenter:

Prof. B.P. Parida

University of Botswana, Gaborone

AF_42 DAKAR WORKSHOP

slide3
Area :

80 000 km2

~ 1/8 area of Botswana

4 Dams:

350 M Cum.

Farm Land : ~ Food Security

AF_42 DAKAR WORKSHOP

slide6

Multi-cell representation

The Limpopo Basin

AF_42 DAKAR WORKSHOP

slide8
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

AF_42 DAKAR WORKSHOP

slide11
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)

AF_42 DAKAR WORKSHOP

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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.

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Output of the best minimized performance function

adopted for the study

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T = TARGET

A = ACHIEVEMENT

Regression between target and modelled runoff coeffs

AF_42 DAKAR WORKSHOP

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Target and simulated runoff coefficients plotted for the entire study period

AF_42 DAKAR WORKSHOP

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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

AF_42 DAKAR WORKSHOP

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A comparison between the forecasted runoff co-efficient

obtained from ANN, EXCEL Tool Box & Extrapolation.

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slide23
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

AF_42 DAKAR WORKSHOP

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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.

AF_42 DAKAR WORKSHOP

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Thank You for listening

AF_42 DAKAR WORKSHOP