Urban Growth Development Using Group Method of Data Handling

Obarijima Priscilla Evoh, Vincent Ike Emeka Anireh and Onate Egerton Taylor
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Obarijima Priscilla Evoh: Rivers State University, Nigeria
Vincent Ike Emeka Anireh: Rivers State University, Nigeria
Onate Egerton Taylor: Rivers State University, Nigeria

European Journal of Information Technologies and Computer Science, 2022, vol. 2, issue 3, 10-16

Abstract: Urbanization has significantly increased over the last two centuries. In year 1800 only 2% of people lived in cities, while in year 1900 this percent increased to 12%. Recent studies indicate that in year 2008 more than 50% of the world population lived in urban areas, with this percentage expected to reach 75% by year 2030. Urban land cover occupies only 2% or 3% of the earth surface, yet it has been recognized that urban growth is associated with many socioeconomic and environmental problems. For example, impervious surfaces that result from urbanization dramatically increase peak discharges associated with storm and snowmelt events, which in turn makes more likely downstream flooding as storm waters exceed stream channel capacities. Urbanization is a very important aspect in country’s development, that’s why this thesis presents urban growth development in Port Harcourt city. The thesis proposed a model in predicting the urban expansion in terms of population growth and land use expansion. The model was trained on a satellite imagery of greater Port Harcourt city. The satellite imagery covers Port Harcourt, Obio/Akpor, Eleme, Etchem, Oyibo, and Omumma local government area in Rivers state. The model was trained using group method of data handling. The result of the model shows a greater change in land use for grater Port Harcourt City, it also shows that by 2035, there will be an increase in pollution for about 5,449,213. The is to say that greater Port Harcourt city will have over 5 million population increase.

Keywords: Group method of data handling; satellite imagery; urbanization (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:epw:comput:v:2:y:2022:i:3:id:10063

DOI: 10.24018/compute.2022.2.3.63

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