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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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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
Area :
80 000 km2
~ 1/8 area of Botswana
4 Dams:
350 M Cum.
Farm Land : ~ Food Security
AF_42 DAKAR WORKSHOP
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
Source: Hydrological Sciences Journal
AF_42 DAKAR WORKSHOP
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
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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Target and simulated runoff coefficients plotted for the entire study period
AF_42 DAKAR WORKSHOP
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
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Simulations up to year 2000 & Forecasts into the year 2016
AF_42 DAKAR WORKSHOP
A comparison between the forecasted runoff co-efficient
obtained from ANN, EXCEL Tool Box & Extrapolation.
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Comparison of Trend in Runoff Coefficints.
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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
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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
AF_42 DAKAR WORKSHOP
