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Nur ‘Atikah Binti Mohd Ali
International Islamic University Malaysia
Computer Science & IT Engineering

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Change Vulnerability Forecasting Using Deep Learning Algorithm for Southeast Asia Amelia Ritahani Ismail; Nur ‘Atikah Binti Mohd Ali; Junaida Sulaiman
Knowledge Engineering and Data Science Vol 1, No 2 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.496 KB) | DOI: 10.17977/um018v1i22018p74-78

Abstract

Climate change is expected to change people’s livelihood in significant ways. Several vulnerability factors and readiness factors used for measuring the prediction index of that particular country on how vulnerable of a country towards global change. Primary data was collected from University of Notre Dame Global Adaptation Index (ND-GAIN). The data has been trained for the forecasting purpose with support from the validated statistical analysis. The summary of the predicted index is visualized using machine learning tools. The results developed the correlation between vulnerability and readiness factors and shows the stability of the country towards climate change. The framework is applied to synthesize findings from Prediction index studies in South East Asia in dealing with vulnerability to climate change.
Co-Authors Amelia Ritahani Ismail Junaida Sulaiman