Change Vulnerability Forecasting Using Deep Learning Algorithm for Southeast Asia

Amelia Ritahani Ismail (International Islamic University Malaysia)
Nur ‘Atikah Binti Mohd Ali (International Islamic University Malaysia)
Junaida Sulaiman (Soft Computing and Intelligent System (SPINT), Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Kuantan, Pahang, Malaysia)

Knowledge Engineering and Data Science
Vol 1, No 2 (2018)

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.