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WELCOME TO THE SECOND AIACC WORKSHOP, DAKAR

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

  2. 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

  3. Area : 80 000 km2 ~ 1/8 area of Botswana 4 Dams: 350 M Cum. Farm Land : ~ Food Security AF_42 DAKAR WORKSHOP

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  6. Multi-cell representation The Limpopo Basin AF_42 DAKAR WORKSHOP

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  8. 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

  9. Source: Hydrological Sciences Journal AF_42 DAKAR WORKSHOP

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  11. 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

  12. 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. AF_42 DAKAR WORKSHOP

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  15. Output of the best minimized performance function adopted for the study AF_42 DAKAR WORKSHOP

  16. T = TARGET A = ACHIEVEMENT Regression between target and modelled runoff coeffs AF_42 DAKAR WORKSHOP

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  18. Target and simulated runoff coefficients plotted for the entire study period AF_42 DAKAR WORKSHOP

  19. 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

  20. Simulations up to year 2000 & Forecasts into the year 2016 AF_42 DAKAR WORKSHOP

  21. A comparison between the forecasted runoff co-efficient obtained from ANN, EXCEL Tool Box & Extrapolation. AF_42 DAKAR WORKSHOP

  22. Comparison of Trend in Runoff Coefficints. AF_42 DAKAR WORKSHOP

  23. 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

  24. 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

  25. Thank You for listening AF_42 DAKAR WORKSHOP

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