TECHNIQUES TO DEVELOP FORECASTING MODEL ON LOW COST HOUSING IN URBAN AREA
DOI:
https://doi.org/10.11113/mjce.v14.15645Keywords:
urban area, accuracy, artificial neural network, forecasting.Abstract
The number of people who will live in urban areas is expected to double to more
than five billion between 1990 to 2025. Therefore, accurate predictions of the level
of aggregate demand for housing are very important. Various forecasting
techniques have been developed using probabilistic, statistics, simulation or
artificial intelligent. Hence, there is a need to identify different techniques, in terms
of accuracy, in the prediction of needs for facilities. This paper discusses the
Artificial Neural Networks (ANN) technique and compaes it with other techniques
in forecasting needs of housing in urban area. Investigation on previous research
and literature materials will be derived and compared in terms of errors in the
accuracy of the technique. The findings of this study indicates that the ANN model
performs best overall.
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