A Time Series Based Prediction Model For Urban Development Plans Using An Artificial Neural Network: Koya In 2020
Free (open access)
257 - 262
S. M. Abdullah
Urban development plans always designed based on two important factors; population growth and city’s expansion. In many cases, the size of expansion of a city depends on the rate of population growth. The expansion process for a city may include many types of projects, such as building new residential and commercial areas, opening internal and access roads, constructing new service buildings (schools, hospitals, etc.) with providing many types of services such as electricity power, communication access points and towers, and sewerage with water net systems. In this project, an accurate prediction model with prediction function is proposed. The model depends on a time series based Artificial Neural Network to achieve the work. The input data for this model will be the number of families, number of students, and number of registered cars to get fuel in Koya petrol stations. These records that are distributed over different spans of years (from 2000 to 2013) were collected in Koya city. This work has general and specific aims. The specific aim of this work is to support some governmental service departments in Koya through providing a correct prediction about the size of requirements for this city in 2020. This case could be generalized to other cities similar to Koya, which is the general aim of this work.