Authors:
Yassir Alharbi
1
;
Daniel Arribas-Be
2
and
Frans Coenen
3
Affiliations:
1
Department of Computer Science, University of Liverpool, Liverpool, U.K., Almahd College Taibah University, Al-Madinah Al-Munawarah and Saudi Arabia
;
2
Geographic Data Science Lab., Department of Geography & Planning, University of Liverpool and U.K.
;
3
Department of Computer Science, University of Liverpool, Liverpool and U.K.
Keyword(s):
Bottom-up Hierarchical Classification, Time Series Forecasting, UN Sustainable Development Goals.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
Abstract:
A framework is presented which can be used to forecast weather an individual geographic area will meet its UN Sustainable Development Goals, or not, at some time t. The framework comprises a bottom up hierarchical classification system where the leaf nodes hold forecast models and the intermediate nodes and root node “logical and” operators. Features of the framework include the automated generation of the: associated taxonomy, the threshold values with which leaf node prediction values will be compared and the individual forecast models. The evaluation demonstrates that the proposed framework can be successfully employed to predict whether individual geographic areas will meet their SDGs.