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Data Visualization Of Urban Water Demand Analysis Using Machine learning

Hersi, Ibrahim

The goal of the project is to visualise patterns of urban development in a visually engaging interactive map. The goal is to develop a statistical model to predict underground water resources and water consumption in different buildings in the city of Uppsala. To achieve the goal, we use machine learning algorithms on a dataset of buildings in Uppsala. The thesis consists of three parts; first and foremost, we discuss the importance of data visualisation in machine learning and developing a visually engaging map that shows the relationship between underground water resources and urban buildings. In the second part, we explain the used machine learning algorithms more in-depth and how they were used to predict water consumption in urban buildings. In the last part, we predict the building type, for example, apartments,house, from a dataset of unknown buildings.

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Data Visualization Of Urban Water Demand Analysis Using Machine learning

Hersi, Ibrahim

The goal of the project is to visualise patterns of urban development in a visually engaging interactive map. The goal is to develop a statistical model to predict underground water resources and water consumption in different buildings in the city of Uppsala. To achieve the goal, we use machine learning algorithms on a dataset of buildings in Uppsala. The thesis consists of three parts; first and foremost, we discuss the importance of data visualisation in machine learning and developing a visually engaging map that shows the relationship between underground water resources and urban buildings. In the second part, we explain the used machine learning algorithms more in-depth and how they were used to predict water consumption in urban buildings. In the last part, we predict the building type, for example, apartments,house, from a dataset of unknown buildings.

Data Visualization Of Urban Water Demand Analysis Using Machine learning

Hersi, Ibrahim

The goal of the project is to visualise patterns of urban development in a visually engaging interactive map. The goal is to develop a statistical model to predict underground water resources and water consumption in different buildings in the city of Uppsala. To achieve the goal, we use machine learning algorithms on a dataset of buildings in Uppsala. The thesis consists of three parts; first and foremost, we discuss the importance of data visualisation in machine learning and developing a visually engaging map that shows the relationship between underground water resources and urban buildings. In the second part, we explain the used machine learning algorithms more in-depth and how they were used to predict water consumption in urban buildings. In the last part, we predict the building type, for example, apartments,house, from a dataset of unknown buildings.

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Title:

Data Visualization Of Urban Water Demand Analysis Using Machine learning

Contributors:

Hersi, Ibrahim (author)

Publication date:

2020-01-01

Remarks:

IT

Type of media:

Theses

Type of material:

Electronic Resource

Language:

English

Keywords:

Engineering and Technology , Teknik och teknologier

Classification:

DDC: 720

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