New Population Forecasting Techniques for Realistic Water Demand Management in Urban Centres of Botswana

Ditiro B. Moalafhi, Bhagabat P. Parida, and Kebuang P. Kenabatho


The Logistic Curve, Geometric Increase, ANNs, Population Forecasts


Rapid population growth, accompanied with increase in affluence, continues to put the semi-arid environments’ limited water resources under pressure. This study has attempted to compare population projections for two cities of Botswana (Gaborone and Francistown) using various methods including the Logistic Curve, and Artificial Neural Networks (ANNs). For both the cities, the Geometric Increase, Logistic Curve and ANNs performed better. The ANNs (-0.03 % deviation from observed) was the best for Gaborone City followed by the Geometric Increase (+2.97 % deviation from observed) and Logistic Curve (-3.40 deviation from observed). For the City of Francistown, the Logistic Curve outperformed the Geometric Increase and the ANNs with deviations of -0.03 %, -0.27 %, and +11.6 5 respectively. The findings indicate that the Cohort Component method for population projections should be supplemented with the superior methods of Geometric Increase, Logistic Curve and ANNs. As census data increase with time, ANNs might prove to be the best approach. With improved population projections, determining likely future water demands against available water resources could be improved and this can help feed into water resources planning strategies.

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