Abstract: The Dar es Salaam City has been growing at a rapid pace which surpasses the capacity of the City’s local government authority for urban planning and development as a result of expansion of informal, unplanned settlements with inadequate infrastructure and services. Moreover, the Local government authority lacks adequate understanding of the trend of the rapid urban spatial growth with time, which results into little understanding of the rate and extent of the spatial growth due to the complex driving factors. This growth of the urban area affects land cover, hydrology, geochemistry, biodiversity and the socio-economic setup of the City. Understanding the occurrence of urban growth in space and time is very challenging. Thus, due to the complex interactions between humans and the environment, an Urban Growth Prediction Model (UGM) was developed using environmental factors to forecast urban growth by employing Geographical Information Systems (GIS), Remote Sensing (RS) and Artificial Neural Networks (ANNs). The predictive ability of the developed Neural Network model was examined through model accuracy assessment techniques particularly Percentage Correct Match Metric (PCM) and urban growth dispersion metric. A comparison was carried between the actual land cover of 2011 and the simulated land cover of 2011, where the PCM estimate was 67.98%. The study findings show that the model predictive capability is sufficient to be used to forecast the future spatial urban growth. Thus, the proposed ANN Model results may be applied to urban planning practice and urban policy development.
Keywords: Artificial Neural Network, Informal Settlement, Geographical Information System, Remote Sensing, Urban Growth.
Title: Spatial-Temporal Modelling for effective prediction of Spatial Urban growth using Artificial Neural Network: Case Study- Dar-es-salaam, Tanzania
Author: Joseph Hayola, Rigobert Francis Buberwa, Beatrice Tarimo
International Journal of Interdisciplinary Research and Innovations
ISSN 2348-1218 (print), ISSN 2348-1226 (online)
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